Startseite Influence of MTHFR polymorphism, alone or in combination with smoking and alcohol consumption, on cancer susceptibility
Artikel Open Access

Influence of MTHFR polymorphism, alone or in combination with smoking and alcohol consumption, on cancer susceptibility

  • Yonghui Huang , Qiurui Hu , Zhenxia Wei , Li Chen , Ying Luo , Xiaojie Li EMAIL logo und Cuiping Li EMAIL logo
Veröffentlicht/Copyright: 26. September 2023
Artikel downloaden (PDF)
Open Life Sciences
Aus der Zeitschrift Open Life Sciences Band 18 Heft 1

Abstract

5,10-methylenetetrahydrofolate reductase (MTHFR) mutations play a significant role in various types of cancers, serving as crucial regulators of folate levels in this process. Several studies have examined the effects of smoking and drinking on MTHFR-related cancers, yielding inconsistent results. Therefore, the objective of this study was to evaluate the magnitude of the effects of gene-smoking or gene-drinking interactions on cancer development. We conducted a comprehensive literature search in PubMed, Web of Science, CNKI, and Wan Fang databases up until May 10th, 2022, to identify relevant articles that met our inclusion criteria. The extracted data from these studies were used to calculate the overall odds ratio (OR) and corresponding 95% confidence interval (95% CI) using either a fixed-effect or random-effect model in Stata version 11.2. Stratified analyses were performed based on ethnicity, control group origin, and cancer classification to assess the risk of cancers associated with gene-smoking or gene-drinking interactions. Sensitivity analyses were conducted to investigate potential sources of heterogeneity, and publication bias was assessed using the Begg’s test and Egger’s test. Additionally, regression analysis was employed to explore the influence of relevant variables on heterogeneity. To evaluate the statistical correlations, analytical methods such as the false-positive report probability and the Bayesian false discovery probability were applied to assess the reliability of the findings. In our meta-analysis, a total of 47 articles were included, comprising 13,701 cases and 21,995 controls for the C677T polymorphism and 5,149 cases and 8,450 controls for the A1298C polymorphism. The results indicated a significant association between C677T polymorphism and cancer risks when combined with smoking (CT + TT vs CC, OR [95% CI] = 1.225 [1.009–1.487], p = 0.041). Stratified analysis further revealed a significant increase in liver cancer risk for individuals with the C677T when combined with smoking (liver cancer: CT + TT vs CC, OR [95% CI] = 1.564 [1.014–2.413], p = 0.043), particularly among Asian smokers (CT + TT vs CC, OR [95% CI] = 1.292 [1.007–1.658], p = 0.044). Regarding the A1298C polymorphism, an elevated risk of cancer was observed in mixed populations alone (CC + AC vs AA, OR [95% CI] = 1.609 [1.087–2.381], p = 0.018), as well as when combined with smoking (CC + AC vs AA, OR [95% CI] = 1.531 [1.127–2.080], p = 0.006). In non-drinkers, C677T polymorphism was found to be associated with esophageal cancer risk (C677T: CT + TT vs CC, OR [95% CI] = 1.544 [1.011–2.359], p = 0.044) and colon cancer risk (CC + AC vs AA, OR [95% CI] = 1.877 [1.166–3.054], p = 0.010), but there was no clear link between this polymorphism and cancer risk among drinkers. The association between the C677T polymorphism and cancer risk among smokers was found to be significant, suggesting that the combination of tobacco and the C677T polymorphism may enhance the carcinogenic process, particularly in liver cancer. However, no similar relationship was observed for the A1298C polymorphism. Interestingly, significantly increased cancer risk was observed in individuals with C677T genetic variants who were nondrinkers, but not among drinkers. These findings highlight the potential role of the C677T polymorphism in modifying cancer risk in specific contexts, such as smoking and alcohol consumption.

Keywords: MTHFR ; cancer; polymorphism; meta-analysis

1 Introduction

Cancer is a serious human disease that has led to high mortality rates worldwide. Nowadays, increasing evidence suggests that cancer development is the result of the combined effect of environmental and genetic factors. Single nucleotide polymorphism (SNP) is the most common and stable genetic variants, and germline sequence variation could be discovered in more than 1% of the general population, which might account for 80% of the whole genome heterogeneity [1]. Based on the fundamental structure changes, researchers use SNPs expediently for genotyping to determine whether they are directly or indirectly linked to specific traits or diseases [2].

Folic acid (vitamin B9) is a crucial micronutrient that cannot be synthesized by the body and is found in dark green leafy vegetables and legumes [3]. Through the action of dihydrofolate (DHF) reductase, folic acid is converted to DHF, which is then further reduced to tetrahydrofolate (THF). THF is transformed into 5,10-methylenetetrahydrofolate (5,10-MTHF). Under the influence of the MTHF reductase (MTHFR) enzyme, 5,10-MTHF is converted to 5-MTHF, which acts as a methyl donor in the synthesis of pyrimidines and purines. Additionally, in the methylation pathway, 5-MTHF plays a critical role by converting homocysteine (Hcy) into S-adenosylmethionine (SAM). SAM is instrumental in DNA methylation processes [4].

MTHFR is one of the most extensively studied genes, playing a crucial role in folate metabolism [5]. MTHFR is located at the short arm terminus of chromosome 1 (1p36.3). The DNA sequence of this MTHFR is approximately 2.2 kb (kilobases) long and consists of 11 exons [6]. The C677T (rs1801133) and A1298C (rs1801131) are the most extensively studied mutations in the MTHFR variants [7]. They are located in exons 4 and 7, respectively. Both variations are associated with a reduction in enzyme activity [8,9]. C677T variation is present in approximately 20–40% of the population. Research findings indicate that individuals with heterozygous (CT) and homozygous mutant (TT) genotypes exhibit enzyme activities reduced to 65% and 30%, respectively, in comparison to the wild-type individuals (CC), under the C → T condition [10]. This variant involves the conversion of cytosine to thymine, leading to the substitution of methionine with valine at amino acid position 222 in the MTHFR enzyme structure [11]. Additionally, this mutation introduces an HinfI restriction site. As a result, the enzyme’s temperature stability is compromised, causing its efficiency to decrease by approximately half and resulting in elevated levels of Hcy in individuals with low folate intake [12]. The A1298C polymorphism is located 2.1 kb downstream of C677T and results in an A to C conversion at codon 429, leading to the substitution of glutamine with alanine in the MTHFR protein. This ultimately results in decreased enzyme activity. However, compared to the C677T homozygous genotype (TT) that significantly increases plasma Hcy levels, the 1298CC genotype does not show a significant increase in Hcy levels [13].

Tobacco smoke contains a substantial quantity of carcinogens and toxic substances that have the potential to promote the development of cancer and other diseases [14]. These harmful components can contribute to conditions such as asthma and trigger inflammatory responses within the body. Additionally, tobacco smoke has the ability to induce genetic mutations, playing a significant role in the underlying mechanisms of tobacco-related illnesses [15]. Scientific studies have demonstrated that smoking can result in double-stranded DNA breaks, which, if left unrepaired, significantly increase the individual’s risk of cancer. Moreover, smoking also influences the levels of Hcy in the body, leading to alterations in global DNA methylation [16].

Alcohol (ethanol) is also considered a carcinogen, and acetaldehyde, a metabolite of ethanol, can induce inflammation in the body, leading to the generation of reactive oxygen species and subsequent downstream effects. Moreover, acetaldehyde exhibits high reactivity with DNA and possesses various carcinogenic and genotoxic properties [17]. Due to its high reactivity with DNA, acetaldehyde can potentially form DNA adducts, altering its physical properties and interfering with DNA synthesis and repair, which is one of the major contributing factors in its carcinogenic mechanism.

With a further understanding of MTHFR polymorphisms, numerous studies have been carried out to explore the relationship between MTHFR polymorphisms and cancers. However, the specific interactions between these polymorphisms and smoking or drinking have not been thoroughly evaluated. Therefore, we conducted a comprehensive meta-analysis using the largest available sample size to investigate the impact of MTHFR SNPs on cancers in conjunction with smoking or drinking. To the best of our knowledge, no previous study has systematically examined the association between MTHFR variants, smoking or drinking, and various types of cancers. We anticipate that this meta-analysis will provide valuable insights into the field of cancer prevention.

2 Material and methods

2.1 Literature search strategy and selection criteria

The relevant medical literature was retrieved from PubMed, Web of Science, CNKI, and Wan Fang electronic databases using various combinations of search terms, including “smoking,” “tobacco,” “cigarette,” “drinking,” “alcohol,” “C677T,” “A1298C,” “rs1801133,” “1801131,” “MTHFR,” “Methylenetetrahydrofolate reductase,” and “cancer.” The search was conducted until May 10th, 2022. To ensure the inclusion of as many relevant studies as possible, we manually screened the references of other reviews, including meta-analyses. The search process was performed multiple times to ensure that no additional articles were missed. Both Chinese and English articles were considered in our analysis.

2.2 Inclusive and exclusive criteria

The selection criteria for eligible studies were as follows: (a) original and case-control studies; (b) examining the relationship between the C677T or A1298C polymorphism and cancer risk; (c) Articles were included in our study if they reported smoking and drinking status categorized as either “never” and “current/ever” or as “nonsmoker/nondrinker” and “smoker/drinker”; (d) providing original data for calculating the crude odds ratio (OR) and 95% confidence interval (CI) among smokers and drinkers; and (e) in cases where multiple articles reported the same primary data by the same author, only the most recent one was considered.

The articles excluded were based on the following criteria: (a) duplicate and overlapped studies; (b) case-only studies; (c) reviews (including meta-analysis), as well as meeting abstracts; (d) studies uncorrelated with one of MTHFR polymorphisms (C677T and A1298C); (e) The classification of smoking and drinking data did not meet our requirements: For example, if smoking was only classified by pack-years and alcohol consumption was only classified by the amount consumed, and if there was a lack of genetic typing data available for those with smoking or drinking status, then they were not included in the analysis.

2.3 Data extraction

The raw data were independently extracted from the included studies by two investigators: including the last name of the first author, year of publication, country, ethnicity, cancer types, control group origin, the sample size of genotypes among smokers and drinkers, and genotyping method. The extracted data were then reviewed by other researchers to ensure accuracy. In case of any discrepancies during the data extraction process, all authors discussed and reached a consensus. For subgroup analysis, the ethnicity was generally categorized into three groups: Asian, Caucasian, and Mixed. Control group origin was divided into two groups: hospital-based and population-based. Extractive studies that provided information on smoking or drinking classification (never, current/ever) were used to further evaluate the impact of these variables on cancer in relation to the polymorphisms.

2.4 Statistical analysis

The association between C677T or A1298C polymorphism and cancer risks among smokers and drinkers was evaluated using crude ORs and 95% CIs. Various models were employed to calculate the overall OR in MTHFR polymorphisms, specifically homozygote comparison (TT vs CC; CC vs AA), heterozygote comparison (CT vs CC; AC vs AA), dominant model (TT + CT vs CC; CC + AC vs AA), and recessive model (CC vs CT + TT; AA vs AC + CC). Subgroup analyses were conducted to examine the effects of these polymorphisms based on ethnicity, source of control, and cancer types.

To assess heterogeneity across studies, two statistical indices, namely I 2 and chi-square p-value, were utilized, taking into account the variation in sample sizes. If I 2 > 50% and chi-square p-value ≤0.05, indicating significant heterogeneity among studies, a random-effects model was used to estimate the combined OR and 95% CI. Otherwise, a fixed-effect model was employed.

Meta-regression analyses were conducted to investigate potential sources of heterogeneity that could influence the results, taking into account the year of publication, ethnicity, and control group origin. Sensitivity analyses were carried out to assess the robustness of the findings by systematically removing individual studies and examining their impact on the overall results. Publication bias was assessed using the Begg funnel plot, which plots the standard error against the log (OR), and the Egger regression asymmetry test. All statistical analyses were performed using Stata software (version 11.2). A p-value below 0.05 was considered statistically significant, and all p-values were two-sided.

To assess the credibility of significant associations, we employed the false-positive report probability (FPRP) and Bayesian false discovery probability (BFDP) as calculation methods. We set a pre-established threshold of 0.2 for FPRP and considered values below this threshold as noteworthy. Additionally, we used prior probabilities of 0.25, 0.1, 0.01, 0.001, and 0.0001 to determine the association between an odds ratio (OR) of 1.5 and cancer risk. Furthermore, we set a threshold of 0.8 for BFDP, and considered values below this threshold as noteworthy. Prior probabilities of 0.01, 0.001, and 0.00001 were used in the BFDP calculations. Similarly, we set 0.8 as the thresholds of BFDP and took 0.01, 0.001, 0.00001 as the prior probability of BFDP. It is important to note that FPRP values below 0.2 and BFDP values below 0.8 were considered significant and deserving of attention [18,19].

3 Result

3.1 Identification and characteristic of studies

As depicted in Figure 1, our search strategy yielded a total of 243 studies. Through an initial screening of titles and abstracts, we excluded 82 studies that were deemed irrelevant, as well as 39 reviews, including meta-analyses, based on the exclusion criteria. Subsequently, upon careful examination of the full text of each article, we excluded 5 studies due to data overlap or duplication, 2 studies without complete full text access, and 68 studies that lacked sufficient data for calculation. Ultimately, our analysis included a total of 47 studies, comprising 44 studies with 13,701 cases and 21,995 controls for C677T polymorphism [20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57], 19 studies with 5,149 cases and 8,450 controls for A1298C polymorphism [20,22,23,25,27,31,33,36,39,41,43,44,45,46,48,58,59,60] (Tables 1 and 2).

Figure 1 The entire flow diagram of filtering the available articles in this Study.
Figure 1

The entire flow diagram of filtering the available articles in this Study.

Table 1

Characteristics of the eligible studies for C677T polymorphism among smokers, non-smokers, drinkers, and non-drinkers

No. Author Year Ethnicity Country Cancer type Source of control Sample size of case Sample size of control Smoking Non-smoking Drinking Non-drinking
Case: CC/CT/TT Control: CC/CT/TT Case: CC/CT/TT Control: CC/CT/TT Case: CC/CT/TT Control: CC/CT/TT Case: CC/CT/TT Control: CC/CT/TT Genotyping Method
1 Han 2020 Asian China Gastric cancer HB 307 560 55/54/13 43/94/21 72/86/27 121/201/80 42/35/8 38/78/18 85/105/32 126/217/83 SNP
2 Ding 2018 Asian China Lung cancer PB 521 1,030 88/98/18 89/94/19 153/137/26 352/372/103 31/38/8 30/40/11 210/197/36 411/197/36 SNPscan
3 Álvarez-Avellón 2017 Caucasian Spain Lung cancer HB 871 825 NA NA NA NA 187/190/66 201/218/63 45/57/22 61/48/11 SNP
4 Nakao 2016 Asian Japan Pancreatic caner HB 360 400 79/100/36 61/105/32 48/61/36 63/89/50 80/103/43 70/128/55 47/58/29 54/66/27 SNP-type
5 Peres 2016 Mixed Brazil Liver cancer HB 71 356 13/26a 64/93a 15/17a 85/114a 14/29a 64/102a 14/14a 85/105a PCR-RFLP
6 Zara-Lopes 2016 Mixed Brazil Thyroid cancer PB 100 144 34/4b 36/2b 34/4b 95/11b 16/4b 38/3b 65/15b 93/10b PCR-RFLP
7 Zara-Lopes 2016 Mixed Brazil Breast cancer PB 100 144 28/8b 36/2b 55/9b 95/11b 35/11b 38/3b 48/6b 93/10b PCR-RFLP
8 Svensson 2016 Asian Japan Colorectal cancer PB 356 709 NA NA NA NA 87/61/28 90/135/33 86/84/30 151/190/81 SNP
9 Galbiatti 2011 Mixed Brazil Head and neck cancer HB 322 531 108/155a 86/129a 29/31a 140/176a 90/133a 104/158a 40/60a 122/147a PCR-RFLP
10 Tsai 2011 Asian Taiwan Oral cancer PB 620 620 303/142/32 241/179/47 88/44/11 81/57/15 NA NA NA NA PCR
11 Zhao 2011 Asian China Esophageal cancer HB 155 310 46/36a 95/38a 22/53a 85/93a 51/62a 94/74a 17/25a 85/57a PCR
12 Arslan 2010 Asian China Lung cancer PB 64 61 25/23/6 24/20/2 5/4/1 5/9/1 NA NA NA NA multiple PCR
13 Cao 2010 Asian China Nasopharyngeal carcinoma PB 529 577 175/96/20 123/79/8 135/73/12 209/109/21 NA NA NA NA PCR-RFLP
14 Wu 2010 Asian Taiwan Prostate cancer PB 218 436 116/61a 164/172a 23/18a 57/43a NA NA NA NA PCR
15 Liu 2009 Asian Taiwan Lung cancer HB 358 716 172/98/23 282/232/49 33/26/6 80/59/14 NA NA NA NA PCR-RFLP
16 Rouissi 2009 Caucasian Tunisia Bladder cancer PB 185 191 50/44/8 30/31/5 14/17/1 33/32/9 NA NA NA NA PCR-RFLP
17 Platek 2009 Mixed America Breast cancer PB 1,063 1,890 NA NA NA NA 351/365/98 641/726/181 78/81/21 147/129/20 PCR
18 Lin 2008 Asian Taiwan Colorectal cancer HB 362 362 65/22/4 41/32/11 167/86/18 144/102/32 29/13/2 22/23/6 203/95/20 163/111/37 PCR
19 Ni 2008 Asian China Laryngeal cancer PB 207 400 39/132a 82/60a 9/27a 70/188a NA NA NA NA PCR-RFLP
20 Suzuki 2008 Asian Japan Pancreatic caner HB 157 785 NA NA NA NA 37/49/12 163/204/68 13/27/6 113/143/50 PCR
21 He 2007 Asian China Gastric cancer PB 467 540 34/98/74 56/84/69 31/95/109 52/123/94 NA NA NA NA PCR-RFLP
22 He 2007 Asian China Esophageal cancer PB 584 540 31/129/116 56/84/69 39/128/125 52/123/94 NA NA NA NA PCR-RFLP
23 Mu 2007 Asian China Liver cancer HB 204 415 26/75a 69/119a 23/58a 66/136a 20/51a 82/44a 29/82a 171/91a PCR-RFLP
24 Suzuki 2007 Asian Japan Head and neck cancer HB 237 711 96/18b 201/41b 48/8b 211/45b 142/16b 354/74b 45/11b 193/42b PCR
25 Wang 2007 Asian China Gastric cancer PB 467 540 34/98/74 56/84/69 31/95/109 52/123/94 NA NA NA NA PCR-RFLP
26 Wang 2007 Asian China Esophageal cancer PB 584 540 31/129/116 56/84/69 39/128/125 52/123/94 NA NA NA NA PCR-RFLP
27 Suzuki 2007 Asian Japan Lung cancer HB 515 1,030 NA NA NA NA 39/49/17 236/298/118 15/23/3 143/176/59 PCR
28 Song 2006 Asian China Rectal cancer PB 113 414 16/23a 75/93a 9/7a 17/29a 14/16a 58/67a 30/50a 115/167a PCR-RFLP
29 Song 2006 Asian China Colon cancer PB 93 414 14/25a 75/93a 6/3a 17/29a 14/13a 58/67a 17/48a 115/167a PCR-RFLP
30 Shi 2005 Caucasian America Lung cancer HB 1,051 1,141 403/385/87 408/438/111 80/83/13 90/81/14 275/282/62 334/341/72 163/152/28 134/152/40 PCR
31 Wang 2005 Asian China Pancreatic caner HB 163 337 15/67a 83/76a 16/65a 59/119a 10/41a 44/44a 21/91a 91/158a PCR-RFLP
32 Yang 2005 Asian Japan Esophageal cancer HB 165 495 35/42/13 74/103/36 28/40/7 112/124/44 39/48/7 24/40/17 24/34/13 162/187/63 PCR
33 Jiang 2005 Asian China Colon cancer PB 53 343 NA NA NA NA 10/7a 46/63a 9/27a 86/144a PCR-RFLP
34 Jiang 2005 Asian China Rectal cancer PB 73 343 NA NA NA NA 11/7a 46/63a 21/33a 86/144a PCR-RFLP
35 Bi 2005 Asian China Gastric cancer HB 155 188 NA NA NA NA 44/60a 49/52a 49/35a 31/23a PCR-RFLP
36 Zhou 2005 Asian China Colorectal cancer HB 478 838 84/112a 141/165a 45/32a 67/88a 69/83a 98/106a 60/61a 110/147a PCR-RFLP
37 Lin 2004 Caucasian America Bladder cancer PB 410 410 132/175a 103/115a 44/59a 93/93a NA NA NA NA PCR-RFLP
38 Mu 2004 Asian China Gastric cancer PB 206 415 28/76a 69/119a 21/64a 66/136a 98/42a 171/82a 35/14a 91/44a PCR-RFLP
39 Wu 2004 Asian China Gastric cancer HB 89 223 12/56a 34/85a 4/17a 31/67a NA NA NA NA PCR
40 Zhang 2004 Caucasian German Esophageal cancer HB 241 256 54/46/22 58/59/20 6/6/2 39/44/11 NA NA NA NA PCR
41 Zhang 2004 Asian China Esophageal cancer HB 189 141 8/54/40 13/28/31 8/39/40 12/26/31 NA NA NA NA PCR
42 Jeng 2003 Asian China Lung cancer PB 59 232 16/15a 41/43a 18/8a 74/56a NA NA NA NA PCR-RFLP
43 Gao 2002 Asian China Gastric cancer PB 107 200 19/62a 33/78a 3/22a 29/56a 9/29a 16/33a 13/52a 47/103a PCR-RFLP
44 Matsuo 2002 Asian Japan Colorectal cancer HB 72 242 31/44a 52/49a 30/75a 65/37a NA NA NA NA PCR

HB: Hospital-based; PB: Population-based; NA: not available. a dominant model (TT + CT/CC); b recessive model (CC/CT + TT).

Table 2

Characteristics of the eligible studies for A1298C polymorphism among smokers, non-smokers, drinkers, and non-drinkers

No. Author Year Ethnicity Country Cancer type Source of control Sample size of case Sample size of control Smoking Non-smoking Drinking Non-drinking
Case: AA/AC/CC Control: AA/AC/CC Case: AA/AC/CC Control: AA/AC/CC Case: AA/AC/CC Control: AA/AC/CC Case: AA/AC/CC Control: AA/AC/CC Genotyping Method
1 Nakao 2016 Asian Japan Pancreatic cancer HB 360 400 145/62/8 142/49/7 95/45/5 143/53/6 152/68/6 184/62/7 88/39/7 101/40/6 PCR-RFLP
2 Peres 2016 Mixed Brazil Liver cancer HB 71 356 19/20a 85/72a 13/19a 119/80a 21/22a 91/75a 11/17a 113/77a PCR
3 Svensson 2016 Asian Japan Colorectal cancer PB 356 709 NA NA NA NA 103/37/8 167/88/6 127/61/12 266/137/19 SNP
4 Al-Motassem 2015 Caucasian Jordan Lung cancer PB 98 89 20/37/18 11/10/1 8/6/6 21/34/10 NA NA NA NA PCR
5 Galbiatti 2011 Mixed Brazil Head and neck cancer HB 322 531 120/143a 128/87a 28/32a 188/128a 105/118a 160/102a 42/58a 156/113a PCR-RFLP
6 Cao 2010 Asian China Nasopharyngeal carcinoma PB 529 577 156/108/14 127/73/8 82/113/11 197/120/14 NA NA NA NA PCR-RFLP
7 Arslan 2010 Asian China Lung cancer PB 64 61 27/23/4 26/19/3 2/6/2 2/10/1 NA NA NA NA PCR-RFLP
8 Naghibalhossaini 2010 Mixed Iran Colorectal cancer PB 175 231 21/23/7 9/12/3 17/29/5 17/23/10 NA NA NA NA PCR-RFLP
9 Promthet 2010 Asian Thailand Colon cancer HB 130 130 NA NA NA NA 17/38/2 19/25/2 26/46/1 35/46/3 PCR-RFLP
10 Rouissi 2009 Caucasian Tunisia Bladder cancer PB 185 191 40/22/4 50/44/8 46/23/5 12/19/1 NA NA NA NA PCR-RFLP
11 Zhou 2008 Asian China Colorectal cancer HB 478 838 127/67a 180/126a 44/33a 94/61a 102/49a 128/86a 70/51a 156/101a multiple PCR
12 Suzuki 2007 Asian Japan Lung cancer HB 515 1,030 NA NA NA NA 65/35/4 420/199/25 29/11/1 232/123/20 PCR
13 Suzuki 2008 Asian Japan Head and neck cancer HB 237 711 111/1b 229/11b 55/1b 242/12b 151/6b 409/17b 55/1b 223/11b PCR
14 Song 2006 Asian China Colon cancer PB 93 414 104/64a 26/12a 31/15a 4/5a 74/51a 15/12a 189/95a 42/19a PCR-RFLP
15 Song 2006 Asian China Rectal cancer PB 114 414 14/2a 26/12a 27/11a 4/5a 56/22a 15/12a 19/11a 42/19a PCR-RFLP
16 Shi 2005 Caucasian America Lung cancer HB 1,051 1,141 692/389/94 473/416/77 88/73/15 91/80/14 285/268/66 321/279/57 150/162/31 173/124/29 PCR
17 Jiang 2005 Asian China Colon cancer PB 53 343 NA NA NA NA 9/8a 69/38a 27/9a 157/71a PCR-RFLP
18 Jiang 2005 Asian China Rectal cancer PB 73 343 NA NA NA NA 12/6a 69/38a 45/8a 157/71a PCR-RFLP
19 Lin 2004 Caucasian America Bladder cancer PB 410 410 142/165a 110/114a 50/53a 79/106a NA NA NA NA PCR-RFLP
20 Matsuo 2002 Asian Japan Colorectal cancer HB 72 242 48/26a 70/31a 46/21a 87/53a NA NA NA NA PCR

HB: Hospital-based; PB: Population-based; NA: not available. a dominant model (CC + AC/AA); b recessive model (AA/CC + AC).

In the final analysis, 36 articles investigated the relationship between C677T polymorphism and cancers among smokers, while 27 articles focused on the relationship between C677T polymorphism and cancers among drinkers. As for the A1298C gene, 19 articles were relevant, with 16 of them exploring the association between A1298C polymorphism and cancers among smokers and 12 articles examining the association between A1298C polymorphism and cancers among drinkers.

3.1.1 C677T polymorphism among smokers and drinkers

Among the 36 smoking-related literatures, there were 5 studies focused on lung cancer, 6 studies for gastric cancer, 6 studies for esophageal cancer, 3 studies for colorectal cancer, 2 studies for pancreatic cancer, 2 studies for liver cancer, and 2 studies for bladder cancer. The remaining types of cancers, such as breast cancer and thyroid cancer, did not have enough studies for subgroup analysis and were represented by less than one article each. In terms of control group source, 17 studies were hospital-based, and 19 studies were population-based. Regarding ethnicity, 28 studies included Asian population, 4 studies included Caucasian population, and 4 studies included mixed population.

Among the 27 alcohol-related literature, there were 4 studies focused on lung cancer, 3 studies on pancreatic cancer, 3 studies on colorectal cancer, 4 studies on gastric cancer, 2 studies on liver cancer, 2 studies on esophageal cancer, 2 studies on colon cancer, and 2 studies on rectal cancer. In terms of control group source, 16 studies were hospital-based and 11 studies were population-based. Regarding ethnicity, 20 studies included Asian population, 2 studies included Caucasian population, and 5 studies included mixed population. Table 1 provides a summary of the characteristics of the included studies for the C677T polymorphism among smokers and drinkers.

3.1.2 A1298C polymorphism among smokers and drinkers

Among the 15 smoking-related studies, there were 3 studies focused on lung cancer, 3 studies on colorectal cancer, and 2 studies on bladder cancer. For other types of cancer such as colon cancer, head and neck cancer, and liver cancer, there were fewer than 2 studies conducted for each, thus subgroup analysis was not performed. In terms of control group source, 7 studies were hospital-based and 8 studies were population-based. Regarding ethnicity, 8 studies included Asian population, 4 studies included Caucasian population, and 3 studies included mixed population.

For the 13 alcohol-related studies, there were 3 studies on colon cancer, 2 studies on colorectal cancer, 2 studies on rectal cancer, and others. Concerning the control group source, 7 studies were hospital-based and 6 studies were population-based. Regarding ethnicity, 10 studies included Asian population and 2 studies included mixed population. Table 2 provides the characteristics of the included studies for the A1298C polymorphism among smokers and drinkers.

3.2 Quantitative synthesis

3.2.1 Relationship between C677T polymorphism and cancer among smokers, non-smokers, drinkers, and non-drinkers

The analysis of the included articles revealed that the C677T polymorphism significantly increased overall cancer susceptibility among smokers in the dominant model (I 2 = 83.20%, CT + TT vs CC, OR [95% CI] = 1.225 [1.009–1.487], p = 0.041), However, no significant association was observed among non-smokers. In subgroup analyses, Asian smokers were to have that elevated risk of cancer with 1.292-fold in the dominant model, whereas Asian non-smokers were not. No association was observed among Caucasian smokers or non-smokers. Liver cancer risk was elevated among smokers by 1.564-fold, compared to non-smokers. Furthermore, smokers had a 1.564-fold increased risk of liver cancer compared to non-smokers. The risk of esophageal cancer was significantly increased in both smokers and non-smokers. However, no significant difference was found between smokers and non-smokers in terms of the association between the C677T polymorphism and other types of cancer such as lung cancer and gastric cancer. The detailed results are presented in Table 3.

Table 3

Integral analysis of the association between C677T polymorphism and cancer risk among smoking population

Comparative model No. Z p OR (95% CI) Heterogeneity Z Begg’s Test t Egger’s test FPRP p-valuea FPRP statistical powerb FPRP prior probability BFDP prior probability
Heterogeneity chi-squared p I 2 0.25 0.10 0.01 0.001 0.0001 0.01 0.001 0.00001
Smoker
TT/CC
Overall 17 0.79 0.432 1.135 (0.827–1.558) 64.71 0.00 75.30% 0.12 0.902 0.29 0.779 0.433 0.958 0.576 0.803 0.978 0.998 1.000 0.996 1.000 1.000
Ethnicity
Caucasian 3 1.13 0.259 0.852 (0.645–1.125) 1.05 0.59 0.00% 0.00 1.000 1.14 0.459 0.259 0.958 0.448 0.708 0.964 0.996 1.000 0.996 1.000 1.000
Asian 14 0.83 0.404 1.177 (0.802–1.728) 58.81 0.00 77.90% 0.77 0.443 −0.46 0.657 0.406 0.892 0.577 0.804 0.978 0.998 1.000 0.995 1.000 1.000
Source of control
HB 8 2.17 0.030 0.798 (0.651–0.978) 10.48 0.16 33.20% 0.37 0.711 −0.25 0.814 0.030 0.958 0.085 0.218 0.754 0.969 0.997 0.982 0.998 1.000
PB 9 1.95 0.051 1.582 (0.998–2.508） 34.21 0.00 76.60% 0.31 0.754 0.13 0.903 0.051 0.410 0.272 0.528 0.925 0.992 0.999 0.981 0.998 1.000
Cancer types
Lung cancer 4 1.45 0.148 0.833 (0.650–1.067) 2.38 0.50 0.00% 1.70 0.089 2.65 0.118 0.148 0.961 0.316 0.581 0.938 0.994 0.999 0.994 0.999 1.000
Gastric cancer 3 0.54 0.591 1.218 (0.594–2.498) 8.23 0.02 75.70% 0.52 0.602 . . 0.590 0.715 0.712 0.881 0.988 0.999 1.000 0.994 0.999 1.000
Esophageal cancer 5 2.17 0.030 1.834 (1.061–3.171) 13.18 0.01 69.70% 0.98 0.327 −1.37 0.264 0.030 0.236 0.276 0.533 0.926 0.992 0.999 0.971 0.997 1.000
CT/CC
Overall 17 0.24 0.814 1.029 (0.809–1.311) 81.17 0.00 80.30% 1.36 0.174 1.45 0.167 0.817 0.999 0.710 0.880 0.988 0.999 1.000 0.998 1.000 1.000
Ethnicity
Caucasian 3 1.41 0.158 0.881 (0.739–1.050) 0.06 0.97 0.00% 0.00 1.000 −3.00 0.205 0.157 0.999 0.320 0.586 0.940 0.994 0.999 0.996 1.000 1.000
Asian 14 0.49 0.623 1.082 (0.791–1.480) 80.71 0.00 83.90% 1.20 0.228 1.82 0.093 0.622 0.980 0.656 0.851 0.984 0.998 1.000 0.997 1.000 1.000
Source of control
HB 8 2.20 0.028 0.758 (0.592–0.970) 17.44 0.02 59.90% 0.37 0.711 −0.22 0.832 0.028 0.846 0.089 0.227 0.764 0.970 0.997 0.978 0.998 1.000
PB 9 1.45 0.148 1.339 (0.902–1.988) 51.14 0.00 84.40% 1.56 0.118 2.17 0.066 0.148 0.713 0.383 0.651 0.953 0.995 1.000 0.991 0.999 1.000
Cancer types
Lung cancer 4 1.95 0.051 0.862 (0.743–1.001) 3.39 0.34 11.60% 0.34 0.734 0.42 0.717 0.052 1.000 0.134 0.317 0.836 0.981 0.998 0.991 0.999 1.000
Gastric cancer 3 0.35 0.726 1.184 (0.459–3.053) 20.07 0.00 90.00% 0.52 0.602 0.727 0.688 0.760 0.905 0.991 0.999 1.000 0.993 0.999 1.000
Esophageal cancer 5 1.76 0.079 1.714 (0.939–3.129) 21.10 0.00 81.00% 0.49 0.624 0.37 0.734 0.079 0.332 0.417 0.683 0.959 0.996 1.000 0.984 0.998 1.000
CT + TT/CC
Overall 33 2.05 0.041 1.225 (1.009–1.487) 189.95 0.00 83.20% 1.78 0.075 2.78 0.009 0.040 0.980 0.109 0.269 0.802 0.976 0.998 0.987 0.999 1.000
Ethnicity
Caucasian 4 0.99 0.323 0.927 (0.798–1.077) 2.43 0.49 0.00% −0.34 1.000 0.55 0.639 0.322 1.000 0.491 0.743 0.970 0.997 1.000 0.998 1.000 1.000
Asian 27 2.02 0.044 1.292 (1.007–1.658) 182.94 0.00 85.80% 1.38 0.169 2.71 0.012 0.044 0.880 0.131 0.311 0.832 0.980 0.998 0.985 0.998 1.000
Mix 2 0.18 0.859 1.030 (0.742–1.429) 0.75 0.39 0.00% 0.00 1.000 – – 0.860 0.988 0.723 0.887 0.989 0.999 1.000 0.997 1.000 1.000
Source of control
HB 16 0.74 0.460 1.097 (0.858–1.402) 70.04 0.00 78.60% 2.03 0.043 1.81 0.092 0.460 0.994 0.581 0.806 0.979 0.998 1.000 0.997 1.000 1.000
PB 17 1.91 0.056 1.347 (0.992–1.828) 112.67 0.00 85.80% 0.78 0.434 1.93 0.072 0.056 0.755 0.182 0.400 0.880 0.987 0.999 0.985 0.999 1.000
Cancer types
Lung cancer 5 2.17 0.030 0.857 (0.745–0.985) 3.67 0.45 0.00% −0.24 1.000 0.77 0.495 0.030 1.000 0.082 0.211 0.747 0.968 0.997 0.987 0.999 1.000
Gastric cancer 6 1.23 0.220 1.347 (0.837–2.169) 22.53 0.75 77.80% 0.56 0.573 0.26 0.807 0.220 0.671 0.496 0.747 0.970 0.997 1.000 0.992 0.999 1.000
Esophageal cancer 6 2.25 0.024 1.755 (1.076–2.863) 22.63 0.00 78.80% 0.56 0.573 0.26 0.806 0.024 0.265 0.216 0.452 0.901 0.989 0.999 0.967 0.997 1.000
Colorectal cancer 3 0.33 0.739 0.884 (0.430–1.820) 11.40 0.00 82.40% 0.00 1.000 −0.50 0.707 0.738 0.778 0.740 0.895 0.989 0.999 1.000 0.994 0.999 1.000
Pancreatic cancer 2 0.69 0.489 1.901 (0.309–11.712) 22.88 0.00 95.60% 0.00 1.000 0.489 0.399 0.786 0.917 0.992 0.999 1.000 0.991 0.999 1.000
Liver cancer 2 2.02 0.043 1.564 (1.014–2.413) 0.18 0.68 0.00% 0.00 1.000 0.043 0.425 0.234 0.478 0.910 0.990 0.999 0.979 0.998 1.000
Bladder cancer 2 0.62 0.536 1.101 (0.812–1.492) 0.75 0.39 0.00% 0.00 1.000 0.535 0.977 0.622 0.831 0.982 0.998 1.000 0.997 1.000 1.000
TT/CT + CC
Overall 20 0.77 0.444 1.036 (0.946–1.136) 25.72 0.14 26.10% 0.29 0.770 −0.11 0.913 0.452 1.000 0.575 0.803 0.978 0.998 1.000 0.999 1.000 1.000
Ethnicity
Caucasian 3 0.70 0.483 0.920 (0.729–1.161) 1.40 0.50 0.00% 0.00 1.000 1.00 0.501 0.482 0.997 0.592 0.813 0.980 0.998 1.000 0.997 1.000 1.000
Asian 15 0.94 0.345 1.049 (0.949–1.160) 19.06 0.16 26.50% 1.29 0.198 −1.59 0.135 0.351 1.000 0.513 0.760 0.972 0.997 1.000 0.999 1.000 1.000
Source of control
HB 9 1.45 0.146 0.895 (0.770–1.040) 5.03 0.76 0.00% 0.73 0.466 −0.90 0.400 0.148 1.000 0.307 0.571 0.936 0.993 0.999 0.996 1.000 1.000
PB 11 2.37 0.018 1.150 (1.024–1.291) 13.99 0.17 28.50% 0.62 0.533 0.84 0.423 0.018 1.000 0.051 0.138 0.639 0.947 0.994 0.983 0.998 1.000
Cancer types
Lung cancer 4 0.98 0.329 0.899 (0.726–1.113) 1.88 0.60 0.00% 1.70 0.089 2.97 0.097 0.328 0.997 0.497 0.748 0.970 0.997 1.000 0.997 1.000 1.000
Gastric cancer 3 0.57 0.567 1.054 (0.880–1.264) 0.79 0.67 0.00% 0.52 0.602 0.570 1.000 0.631 0.837 0.983 0.998 1.000 0.998 1.000 1.000
Esophageal cancer 5 2.22 0.027 1.175 (1.019–1.356) 3.98 0.41 0.00% 0.98 0.327 −1.48 0.235 0.027 1.000 0.076 0.198 0.731 0.965 0.996 0.986 0.999 1.000
Non-smoker
TT/CC
Overall 17 0.12 0.906 1.017 (0.764–1.355) 42.31 0.00 62.20% 0.45 0.650 −0.67 0.511 0.908 0.996 0.732 0.891 0.989 0.999 1.000 0.997 1.000 1.000
Ethnicity
Caucasian 3 0.40 0.689 0.870 (0.441–1.716) 1.50 0.47 0.00% 1.04 0.296 −0.87 0.543 0.688 0.779 0.726 0.888 0.989 0.999 1.000 0.994 0.999 1.000
Asian 14 0.21 0.833 1.034 (0.756–1.414) 40.67 0.00 68.00% 0.11 0.913 −0.41 0.690 0.834 0.990 0.717 0.883 0.988 0.999 1.000 0.997 1.000 1.000
Source of control
HB 8 2.07 0.039 0.759 (0.585–0.986) 8.40 0.30 16.70% 0.37 0.711 1.64 0.152 0.039 0.834 0.123 0.295 0.822 0.979 0.998 0.983 0.998 1.000
PB 9 0.91 0.361 1.204 (0.809–1.792) 24.46 0.00 67.30% 0.10 0.917 −0.87 0.413 0.360 0.861 0.557 0.790 0.976 0.998 1.000 0.995 1.000 1.000
Cancer types
Lung cancer 4 1.81 0.070 0.709 (0.488–1.028) 2.13 0.55 0.00% −0.34 1.000 1.27 0.331 0.070 0.627 0.250 0.500 0.917 0.991 0.999 0.986 0.999 1.000
Gastric cancer 3 0.62 0.535 1.290 (0.577–2.884) 14.15 0.00 85.90% 0.52 0.602 0.535 0.643 0.714 0.882 0.988 0.999 1.000 0.993 0.999 1.000
Esophageal cancer 5 2.87 0.004 1.550 (1.149–2.092) 4.62 0.33 13.40% 0.98 0.327 −0.94 0.418 0.004 0.415 0.029 0.083 0.499 0.909 0.990 0.892 0.988 1.000
CT/CC
Overall 17 0.25 0.804 0.985 (0.878–1.106) 19.47 0.25 17.80% 0.95 0.343 1.37 0.191 0.798 1.000 0.705 0.878 0.988 0.999 1.000 0.999 1.000 1.000
Ethnicity
Caucasian 3 0.71 0.476 1.142 (0.792–1.647) 0.21 0.90 0.00% 0.00 1.000 −0.56 0.674 0.477 0.928 0.607 0.822 0.981 0.998 1.000 0.996 1.000 1.000
Asian 14 0.50 0.617 0.969 (0.858–1.095) 18.55 0.14 29.90% 1.20 0.228 1.41 0.184 0.614 1.000 0.648 0.847 0.984 0.998 1.000 0.999 1.000 1.000
Source of control
HB 8 0.88 0.377 0.923 (0.773–1.103) 8.89 0.27 21.20% 1.61 0.108 1.89 0.108 0.378 1.000 0.531 0.773 0.974 0.997 1.000 0.998 1.000 1.000
PB 9 0.43 0.667 1.034 (0.888–1.204) 9.67 0.29 17.30% −0.10 1.000 0.52 0.620 0.667 1.000 0.667 0.857 0.985 0.999 1.000 0.998 1.000 1.000
Cancer types
Lung cancer 4 0.66 0.512 0.931 (0.752–1.153) 2.32 0.51 0.00% −0.34 1.000 −0.11 0.922 0.512 0.999 0.606 0.822 0.981 0.998 1.000 0.998 1.000 1.000
Gastric cancer 3 0.14 0.886 1.031 (0.679–1.565) 4.70 0.10 57.40% 0.52 0.602 0.886 0.961 0.734 0.892 0.989 0.999 1.000 0.996 1.000 1.000
Esophageal cancer 5 2.32 0.020 1.379 (1.051–1.808) 1.45 0.84 0.00% 0.49 0.624 0.07 0.945 0.020 0.729 0.076 0.199 0.732 0.965 0.996 0.970 0.997 1.000
CT + TT/CC
Overall 33 1.37 0.171 1.123 (0.951–1.325) 88.16 0.00 63.70% 0.36 0.722 1.19 0.242 0.169 1.000 0.337 0.604 0.944 0.994 0.999 0.996 1.000 1.000
Ethnicity
Caucasian 4 1.12 0,262 1.177 (0.885–1.565) 0.54 0.91 0.00% 1.02 0.308 −1.07 0.397 0.262 0.952 0.452 0.713 0.965 0.996 1.000 0.995 1.000 1.000
Asian 27 1.34 0.180 1.145 (0.940–1.395) 86.15 0.00 69.80% 0.79 0.428 1.32 0.200 0.179 0.996 0.350 0.618 0.947 0.994 0.999 0.996 1.000 1.000
Mix 2 0.72 0.469 0.848 (0.544–1.324) 0.00 0.99 0.00% 0.00 1.000 0.468 0.855 0.622 0.831 0.982 0.998 1.000 0.995 1.000 1.000
Source of control
HB 16 1.06 0.290 1.158 (0.882–1.521) 56.09 0.00 73.30% 2.03 0.043 1.97 0.069 0.292 0.969 0.475 0.730 0.968 0.997 1.000 0.996 1.000 1.000
PB 17 1.26 0.209 1.084 (0.956–1.230) 31.90 0.01 49.80% 0.95 0.343 0.04 0.972 0.211 1.000 0.387 0.655 0.954 0.995 1.000 0.998 1.000 1.000
Cancer types
Lung cancer 5 1.37 0.172 0.871 (0.714–1.062) 3.76 0.44 0.00% −2.24 1.000 −0.26 0.808 0.172 0.996 0.341 0.609 0.945 0.994 0.999 0.996 1.000 1.000
Gastric cancer 6 1.51 0.130 1.410 (0.904–2.199) 16.27 0.01 69.30% 0.94 0.348 2.12 0.101 0.130 0.608 0.390 0.658 0.955 0.995 1.000 0.990 0.999 1.000
Esophageal cancer 6 3.62 0.000 1.540 (1.219–1.945) 3.98 0.55 0.00% −0.19 1.000 −0.10 0.922 0.000 0.413 0.002 0.006 0.065 0.412 0.875 0.470 0.900 0.999
Colorectal cancer 3 0.24 0.811 1.153 (0.360–3.688) 34.37 0.00 94.20% 1.04 0.296 0.69 0.614 0.810 0.671 0.784 0.916 0.992 0.999 1.000 0.993 0.999 1.000
Pancreatic cancer 2 0.70 0.484 1.317 (0.609–2.845) 3.95 0.05 74.70% 0.00 1.000 0.483 0.630 0.697 0.874 0.987 0.999 1.000 0.993 0.999 1.000
Liver cancer 2 0.30 0.762 1.072 (0.684–1.680) 0.60 0.44 0.00% 0.00 1.000 0.762 0.929 0.711 0.881 0.988 0.999 1.000 0.996 1.000 1.000
Bladder cancer 2 1.07 0.286 1.256 (0.826–1.910) 0.28 0.60 0.00% 0.00 1.000 0.287 0.797 0.519 0.764 0.973 0.997 1.000 0.994 0.999 1.000
TT/CT + CC
Overall 20 1.28 0.202 1.084 (0.958–1.226) 34.28 0.02 44.60% 0.23 0.820 −2.12 0.048 0.199 1.000 0.374 0.642 0.952 0.995 0.999 0.997 1.000 1.000
Ethnicity
Caucasian 3 0.61 0.543 0.816 (0.425–1.568) 1.82 0.40 0.00% 1.04 0.296 −0.74 0.596 0.542 0.728 0.691 0.870 0.987 0.999 1.000 0.994 0.999 1.000
Asian 15 0.01 0.994 1.001 (0.808–1.240) 31.77 0.00 55.90% 0.69 0.488 −2.15 0.051 0.993 1.000 0.749 0.899 0.990 0.999 1.000 0.998 1.000 1.000
Source of control
HB 9 2.01 0.045 0.795 (0.636–0.994) 4.90 0.77 0.00% −0.10 1.000 0.51 0.628 0.044 0.939 0.124 0.297 0.823 0.979 0.998 0.986 0.999 1.000
PB 11 2.93 0.003 1.250 (1.077–1.451) 18.41 0.05 45.70% 1.25 0.213 −1.52 0.162 0.003 0.992 0.010 0.030 0.251 0.772 0.971 0.915 0.991 1.000
Cancer types
Lung cancer 4 1.66 0.096 0.738 (0.516–1.056) 1.58 0.66 0.00% −0.34 1.000 2.22 0.156 0.097 0.711 0.289 0.550 0.931 0.993 0.999 0.989 0.999 1.000
Gastric cancer 3 0.85 0.394 1.242 (0.755–2.043) 9.57 0.01 79.10% 0.52 0.602 0.393 0.771 0.605 0.821 0.981 0.998 1.000 0.994 0.999 1.000
Esophageal cancer 5 2.07 0.038 1.253 (1.012–1.551) 4.70 0.32 15.00% 0.98 0.327 −1.50 0.231 0.038 0.951 0.108 0.266 0.799 0.976 0.998 0.985 0.998 1.000

Bold values indicates statistically significant OR values, values with FPRP less than 0.2, and values with BFDP less than 0.8.

The analysis revealed that the overall risk of cancer was higher among non-drinkers in the dominant model (I 2 = 80.50%, CT + TT vs CC, OR [95% CI] = 1.248 [1.001–1.557], p = 0.049), compared with drinkers. Subgroup analyses found that there was no significant difference between drinkers and non-drinkers in Asians, as well as a mixed population. Additionally, non-drinkers had a 1.544-fold increased risk of esophageal cancer and a 1.877-fold increased risk of colon cancer compared to drinkers. The detailed results are presented in Table 4.

Table 4

Integral analysis of the association between C677T polymorphism and cancer risk among drinking population

Comparative model No. Z p OR (95% CI) Heterogeneity Z Begg’s Test t Egger’s test FPRP p-valuea FPRP statistical powerb FPRP prior probability BFDP prior probability
Heterogeneity chi-squared p I 2 0.25 0.10 0.01 0.001 0.0001 0.01 0.001 0.000010
Drinker
TT/CC 11 1.60 0.109 0.881 (0.754–1.029) 14.68 0.144 31.90% 2.49 0.013 −4.27 0.002 0.110 1.000 0.248 0.497 0.916 0.991 0.999 0.995 1.000 1.000
Overall
Ethnicity
Asian 8 3.07 0.002 0.667 (0.515–0.864) 7.64 0.366 8.40% 1.61 0.108 −2.62 0.040 0.002 0.502 0.013 0.037 0.299 0.811 0.977 0.834 0.981 1.000
Source of control
HB 8 1.74 0.083 0.837 (0.685–1.023) 13.69 0.057 48.90% 2.10 0.035 −4.79 0.003 0.082 0.987 0.200 0.429 0.892 0.988 0.999 0.992 0.999 1.000
PB 3 0.41 0.682 0.950 (0.744–1.213) 0.47 0.791 0.00% 1.04 0.296 −14.98 0.042 0.681 0.998 0.672 0.860 0.985 0.999 1.000 0.998 1.000 1.000
Cancer types
Lung cancer 4 0.17 0.863 1.021 (0.802–1.301) 1.00 0.802 0.00% 1.02 0.308 −3.51 0.072 0.867 0.999 0.722 0.886 0.988 0.999 1.000 0.998 1.000 1.000
Pancreatic cancer 2 1.58 0.115 0.715 (0.472–1.085) 0.08 0.775 0.00% 0.00 1.000 0.115 0.629 0.354 0.622 0.948 0.995 0.999 0.989 0.999 1.000
Colorectal cancer 2 1.01 0.311 0.755 (0.438–1.301) 1.86 0.173 46.20% 0.00 1.000 0.311 0.673 0.581 0.806 0.979 0.998 1.000 0.993 0.999 1.000
CT/CC
Overall 11 2.51 0.012 0.799 (0.670–0.952) 21.56 0.017 53.60% 1.40 0.161 −2.04 0.071 0.012 0.979 0.036 0.100 0.550 0.925 0.992 0.967 0.997 1.000
Ethnicity
Asian 8 3.88 0.000 0.697 (0.581–0.836) 13.84 0.054 49.40% 0.12 0.902 −0.43 0.682 0.000 0.684 0.000 0.001 0.014 0.127 0.594 0.277 0.794 0.997
Source of control
HB 8 1.87 0.061 0.879 (0.768–1.006) 12.90 0.074 45.80% 1.36 0.174 −2.44 0.051 0.061 1.000 0.155 0.355 0.858 0.984 0.998 0.993 0.999 1.000
PB 3 1.29 0.196 0.733 (0.457–1.174) 8.39 0.015 76.20% 0.00 1.000 −0.64 0.636 0.196 0.653 0.474 0.730 0.967 0.997 1.000 0.992 0.999 1.000
Cancer types
Lung cancer 4 0.30 0.764 0.976 (0.833–1.144) 0.18 0.980 0.00% 0.34 0.734 −0.64 0.585 0.764 1.000 0.696 0.873 0.987 0.999 1.000 0.998 1.000 1.000
Pancreatic cancer 2 1.11 0.269 0.840 (0.616–1.145) 1.61 0.204 38.10% 0.270 0.928 0.466 0.724 0.966 0.997 1.000 0.995 1.000 1.000
Colorectal cancer 2 4.00 0.000 0.460 (0.314–0.673) 0.03 0.862 0.00% 0.000 0.028 0.007 0.020 0.183 0.694 0.958 0.193 0.707 0.996
CT + TT/CC
Overall 24 0.41 0.681 0.965 (0.812–1.146) 74.01 0.000 68.90% 0.12 0.901 0.18 0.862 0.685 1.000 0.673 0.860 0.985 0.999 1.000 0.998 1.000 1.000
Ethnicity
Asian 19 0.37 0.714 0.951 (0.727–1.244) 72.67 0.000 75.20% 0.00 1.000 0.42 0.681 0.714 0.995 0.683 0.866 0.986 0.999 1.000 0.997 1.000 1.000
Mix 3 0.61 0.541 0.954 (0.820–1.110) 0.81 0.667 0.00% 1.04 0.296 2.56 0.237 0.542 1.000 0.619 0.830 0.982 0.998 1.000 0.998 1.000 1.000
Source of control
HB 15 0.49 0.627 1.061 (0.835–1.349) 60.21 0.000 76.70% 0.68 0.488 0.58 0.573 0.629 0.998 0.654 0.850 0.984 0.998 1.000 0.998 1.000 1.000
PB 9 2.25 0.024 0.855 (0.747–0.980) 9.98 0.267 19.80% 0.73 0.466 −0.83 0.434 0.024 1.000 0.068 0.180 0.708 0.961 0.996 0.985 0.999 1.000
Cancer types
Lung cancer 4 0.18 0.858 0.986 (0.849–1.146) 0.21 0.976 0.00% 1.70 0.089 −4.41 0.048 0.854 1.000 0.719 0.885 0.988 0.999 1.000 0.999 1.000 1.000
Pancreatic cancer 3 0.64 0.522 1.307 (0.576–2.963) 15.05 0.001 86.70% 1.04 0.296 15.06 0.042 0.521 0.629 0.713 0.882 0.988 0.999 1.000 0.993 0.999 1.000
Colorectal cancer 3 1.39 0.166 0.659 (0.366–1.189) 7.99 0.018 75.00% 0.00 1.000 −0.47 0.720 0.166 0.485 0.507 0.755 0.971 0.997 1.000 0.990 0.999 1.000
Gastric cancer 4 0.45 0.654 0.881 (0.507–1.531) 10.31 0.016 70.90% −0.34 1.000 0.38 0.740 0.653 0.839 0.700 0.875 0.987 0.999 1.000 0.995 1.000 1.000
Liver cancer 2 1.42 0.155 2.511 (0.705–8.945) 7.13 0.008 86.00% 0.00 1.000 0.155 0.213 0.686 0.868 0.986 0.999 1.000 0.988 0.999 1.000
Esophageal cancer 2 0.04 0.965 0.979 (0.384–2.496) 5.61 0.018 82.20% 0.00 1.000 0.965 0.789 0.786 0.917 0.992 0.999 1.000 0.993 0.999 1.000
Colon cancer 2 1.20 0.230 0.673 (0.352–1.285) 0.44 0.505 0.00% 0.00 1.000 0.230 0.511 0.574 0.802 0.978 0.998 1.000 0.991 0.999 1.000
Rectal cancer 2 0.95 0.344 0.741 (0.398–1.379) 1.31 0.253 23.40% 0.00 1.000 0.344 0.631 0.621 0.831 0.982 0.998 1.000 0.993 0.999 1.000
TT/CT + CC
Overall 14 0.77 0.443 0.948 (0.826–1.087) 21.96 0.056 40.80% 0.88 0.381 −0.69 0.505 0.444 1.000 0.571 0.800 0.978 0.998 1.000 0.998 1.000 1.000
Ethnicity
Asian 9 2.52 0.012 0.757 (0.609–0.940) 10.05 0.261 20.40% 1.77 0.076 −1.83 0.109 0.012 0.875 0.039 0.108 0.570 0.931 0.993 0.960 0.996 1.000
Mix 3 1.26 0.206 1.901 (0.702–5.142) 5.33 0.070 62.50% 0.00 1.000 4.98 0.126 0.206 0.320 0.658 0.853 0.985 0.998 1.000 0.990 0.999 1.000
Source of control
HB 9 1.78 0.075 0.852 (0.715–1.016) 12.55 0.128 36.20% 2.40 0.016 −3.70 0.008 0.075 0.997 0.183 0.402 0.881 0.987 0.999 0.993 0.999 1.000
PB 5 1.05 0.296 1.126 (0.902–1.405) 6.30 0.178 36.50% 1.22 0.221 1.53 0.224 0.293 0.997 1.000 1.000
Cancer types
Lung cancer 4 0.29 0.771 1.034 (0.825–1.297) 1.21 0.751 0.00% 1.02 0.308 −2.44 0.135 0.772 0.999 0.699 0.874 0.987 0.999 1.000 0.998 1.000 1.000
Pancreatic cancer 2 1.09 0.274 0.814 (0.563–1.177) 0.08 0.774 0.00% 0.00 1.000 0.274 0.327 0.538 0.777 0.975 0.997 1.000 0.995 0.999 1.000
Colorectal cancer 2 0.24 0.812 0.869 (0.271–2.782) 2.10 0.148 52.30% 0.00 1.000 0.813 0.672 0.784 0.916 0.992 0.999 1.000 0.993 0.999 1.000
Breast cancer 2 0.82 0.411 1.710 (0.476–6.148) 3.67 0.056 72.70% 0.00 1.000 0.411 0.420 0.746 0.898 0.990 0.999 1.000 0.991 0.999 1.000
Non-drinker
TT/CC
Overall 11 0.08 0.932 0.983 (0.670–1.444) 38.63 0.000 74.10% 0.47 0.640 0.64 0.538 0.930 0.976 0.741 0.896 0.990 0.999 1.000 0.997 1.000 1.000
Ethnicity
Asian 8 0.66 0.507 0.867 (0.568–1.322) 23.71 0.001 70.50% −0.12 1.000 −0.03 0.975 0.507 0.889 0.631 0.837 0.983 0.998 1.000 0.996 1.000 1.000
Source of control
HB 8 0.72 0.470 0.854 (0.557–1.310) 20.67 0.004 66.10% 1.11 0.266 1.18 0.283 0.470 0.872 0.618 0.829 0.982 0.998 1.000 0.995 1.000 1.000
PB 3 0.76 0.450 1.342 (0.626–2.878) 11.68 0.003 82.90% 0.00 1.000 0.43 0.740 0.450 0.613 0.688 0.869 0.986 0.999 1.000 0.994 0.999 1.000
Cancer types
Lung cancer 4 0.35 0.725 1.161 (0.507–2.660) 16.83 0.001 82.20% 0.34 0.734 −0.17 0.880 0.724 0.728 0.749 0.900 0.990 0.999 1.000 0.994 0.999 1.000
Pancreatic cancer 2 0.58 0.564 1.176 (0.679–2.036) 0.07 0.786 0.00% 0.563 0.808 0.676 0.862 0.986 0.999 1.000 0.995 0.999 1.000
Colorectal cancer 2 3.12 0.002 0.549 (0.376–0.800) 1.08 0.300 7.00% 0.002 0.156 0.033 0.094 0.533 0.920 0.991 0.796 0.975 1.000
CT/CC
Overall 11 0.58 0.565 1.079 (0.833–1.397) 41.92 0.000 76.10% 1.09 0.276 −0.06 0.953 0.564 0.994 0.630 0.836 0.983 0.998 1.000 0.997 1.000 1.000
Ethnicity
Asian 8 0.35 0.728 1.063 (0.751–1.505) 36.81 0.000 81.00% 1.36 0.174 −0.40 0.706 0.731 0.974 0.692 0.871 0.987 0.999 1.000 0.997 1.000 1.000
Source of control
HB 8 0.03 0.974 0.910 (0.778–1.065) 13.58 0.059 48.40% 1.86 0.063 4.46 0.004 0.240 1.000 0.419 0.683 0.960 0.996 1.000 0.997 1.000 1.000
PB 3 0.72 0.472 1.231 (0.699–2.167) 16.98 0.000 88.20% 0.00 1.000 −1.80 0.322 0.471 0.753 0.652 0.849 0.984 0.998 1.000 0.994 0.999 1.000
Cancer types
Lung cancer 4 1.18 0.240 1.342 (0.822–2.192) 17.43 0.001 82.80% 0.34 0.734 −0.29 0.800 0.240 0.672 0.517 0.763 0.973 0.997 1.000 0.993 0.999 1.000
Pancreatic cancer 2 0.88 0.378 1.207 (0.794–1.835) 1.17 0.280 14.50% 0.00 1.000 0.379 0.845 0.573 0.801 0.978 0.998 1.000 0.995 1.000 1.000
Colorectal cancer 2 2.49 0.013 0.727 (0.566–0.935) 0.22 0.635 0.00% 0.00 1.000 0.013 0.750 0.049 0.135 0.632 0.945 0.994 0.959 0.996 1.000
CT + TT/CC
Overall 24 1.97 0.049 1.248 (1.001–1.557) 117.97 0.000 80.50% 1.22 0.224 0.93 0.361 0.050 0.948 0.136 0.320 0.838 0.981 0.998 0.987 0.999 1.000
Ethnicity
Asian 19 1.81 0.071 1.286 (0.979–1.689) 106.47 0.000 83.10% 1.33 0.184 0.69 0.502 0.071 0.866 0.196 0.423 0.890 0.988 0.999 0.989 0.999 1.000
Mix 3 1.34 0.179 1.206 (0.918–1.585) 1.11 0.574 0.00% 1.04 0.296 −4.44 0.141 0.179 0.941 0.363 0.631 0.950 0.995 0.999 0.994 0.999 1.000
Source of control
HB 15 1.30 0.192 1.229 (0.902–1.674) 85.04 0.000 83.50% 1.39 0.166 2.19 0.048 0.191 0.897 0.390 0.657 0.955 0.995 1.000 0.994 0.999 1.000
PB 9 1.69 0.091 1.290 (0.960–1.732) 26.80 0.001 70.10% 0.31 0.754 −0.58 0.577 0.090 0.842 0.243 0.491 0.914 0.991 0.999 0.990 0.999 1.000
Cancer types
Lung cancer 4 0.98 0.326 1.311 (0.764–2.250) 23.39 0.000 87.20% 0.34 0.734 −0.29 0.802 0.326 0.687 0.587 0.810 0.979 0.998 1.000 0.993 0.999 1.000
Pancreatic caner 3 1.70 0.089 1.577 (0.933–2.664) 5.17 0.075 61.30% 0.00 1.000 0.26 0.841 0.089 0.426 0.384 0.652 0.954 0.995 1.000 0.986 0.999 1.000
Colorectal cancer 3 3.50 0.000 0.693 (0.564–0.851) 0.73 0.686 0.00% 1.04 0.296 1.11 0.466 0.000 0.644 0.002 0.006 0.067 0.419 0.878 0.592 0.936 0.999
Gastric cancer 4 0.29 0.774 0.939 (0.610–1.445) 6.43 0.092 54.30% 0.34 0.734 1.59 0.253 0.775 0.940 0.712 0.881 0.988 0.999 1.000 0.996 1.000 1.000
Liver cancer 2 0.80 0.421 2.130 (0.337–13.456) 15.55 0.000 93.60% 0.00 1.000 0.421 0.355 0.781 0.914 0.992 0.999 1.000 0.991 0.999 1.000
Esophageal cancer 2 2.01 0.044 1.544 (1.011–2.359) 1.49 0.223 32.80% 0.00 1.000 0.045 0.447 0.230 0.473 0.908 0.990 0.999 0.979 0.998 1.000
Colon cancer 2 2.59 0.010 1.877 (1.166–3.054) 0.03 0.873 0.00% 0.00 1.000 0.011 0.183 0.155 0.355 0.858 0.984 0.998 0.943 0.994 1.000
Rectal cancer 2 0.28 0.782 1.057 (0.714–1.563) 0.25 0.620 0.00% 0.00 1.000 0.781 0.960 0.709 0.880 0.988 0.999 1.000 0.997 1.000 1.000
TT/CT + CC
Overall 14 0.09 0.931 1.012 (0.781–1.311) 29.00 0.007 55.20% 0.33 0.743 0.89 0.391 0.928 0.999 0.736 0.893 0.989 0.999 1.000 0.998 1.000 1.000
Ethnicity
Asian 9 1.42 0.155 0.869 (0.717–1.054) 14.37 0.073 44.30% 0.31 0.754 −0.30 0.772 0.154 0.996 0.317 0.582 0.939 0.994 0.999 0.995 1.000 1.000
Mix 3 2.40 0.017 1.757 (1.108–2.786) 0.79 0.673 0.00% 0.00 1.000 −0.72 0.602 0.017 0.251 0.165 0.373 0.867 0.985 0.998 0.957 0.996 1.000
Source of control
HB 9 1.79 0.073 0.828 (0.673–1.018) 15.54 0.049 48.50% 0.31 0.754 0.63 0.551 0.073 0.980 0.183 0.402 0.881 0.987 0.999 0.991 0.999 1.000
PB 5 1.31 0.190 1.321 (0.871–2.005) 8.36 0.079 52.10% 0.73 0.462 0.91 0.430 0.191 0.725 0.442 0.703 0.963 0.996 1.000 0.992 0.999 1.000
Cancer types
Lung cancer 4 0.10 0.922 1.033 (0.540–1.977) 11.21 0.011 73.20% −0.34 1.000 −0.24 0.836 0.922 0.870 0.761 0.905 0.991 0.999 1.000 0.995 0.999 1.000
Pancreatic cancer 2 0.24 0.808 1.062 (0.654–1.725) 0.72 0.395 0.00% 0.00 1.000 0.808 0.919 0.725 0.888 0.989 0.999 1.000 0.996 1.000 1.000
Colorectal cancer 2 2.50 0.012 0.634 (0.444–0.906) 1.17 0.280 14.30% 0.00 1.000 0.012 0.391 0.086 0.221 0.758 0.969 0.997 0.950 0.995 1.000
Breast cancer 2 1.71 0.086 1.615 (0.934–2.795) 0.50 0.480 0.00% 0.00 1.000 0.087 0.396 0.397 0.664 0.956 0.995 1.000 0.986 0.999 1.000

Bold value indicates statistically significant OR values, values with FPRP less than 0.2, and values with BFDP less than 0.8.

3.2.2 Relationship between A1298C polymorphism and cancer among smokers, non-smokers, drinkers, and non-drinkers

The analysis of included articles revealed that A1298C polymorphism was irrelevant to overall cancer risk among smokers and non-smokers. However, subgroup analyses revealed a significant increase in cancer risk among the mixed population in the dominant model, with a 1.531-fold increased risk for nonsmokers and a 1.609-fold increased risk for smokers. No significant association between A1298C polymorphism and cancer risk was observed among Asian and Caucasian populations, regardless of smoking status. Detailed results can be found in Table 5.

Table 5

Integral analysis of the association between A1298C polymorphism and cancer risk among smoking population

Comparative model No. Z p OR (95% CI) Heterogeneity Z Begg’s Test t Egger’s test FPRP p-valuea FPRP statistical powerb FPRP prior probability BFDP prior probability
Heterogeneity chi-squared p I 2 0.25 0.10 0.01 0.001 0.0001 0.01 0.001 0.000010
smoker
CC/AA
Overall 7 0.23 0.815 0.968 (0.740–1.268) 6.69 0.35 10.40% 1.20 0.230 1.76 0.128 0.813 0.997 0.710 0.880 0.988 0.999 1.000 0.998 1.000 1.000
Ethnicity
Caucasian 3 0.28 0.777 1.169 (0.398–3.432) 5.34 0.07 62.60% 1.04 0.296 0.77 0.583 0.776 0.675 0.775 0.912 0.991 0.999 1.000 0.993 0.999 1.000
Asian 3 0.79 0.429 1.286 (0.689–2.402) 0.12 0.94 0.00% 0.00 1.000 −0.28 0.824 0.430 0.685 0.653 0.850 0.984 0.998 1.000 0.994 0.999 1.000
Source of control
HB 2 0.98 0.325 0.857 (0.629–1.166) 0.28 0.60 0.00% 0.00 1.000 0.326 0.945 0.509 0.756 0.972 0.997 1.000 0.996 1.000 1.000
PB 5 1.25 0.211 1.427 (0.817–2.491) 4.97 0.29 19.60% 0.73 0.462 0.83 0.469 0.211 0.570 0.526 0.769 0.973 0.997 1.000 0.992 0.999 1.000
Cancer type
Lung cancer 3 0.71 0.480 1.556 (0.457–5.306) 5.29 0.07 62.20% 1.04 0.296 1.70 0.339 0.480 0.477 0.751 0.901 0.990 0.999 1.000 0.992 0.999 1.000
AC/AA
Overall 7 0.20 0.838 0.965 (0.684–1.361) 18.76 0.01 68.00% 0.00 1.000 1.85 0.124 0.839 0.982 0.719 0.885 0.988 0.999 1.000 0.997 1.000 1.000
Ethnicity
Caucasian 3 0.94 0.349 0.781 (0.465–1.311) 4.88 0.09 59.00% 1.04 0.296 1.05 0.485 0.350 0.725 0.591 0.813 0.979 0.998 1.000 0.994 0.999 1.000
Asian 3 1.40 0.162 1.213 (0.926–1.590) 0.02 0.99 0.00% 0.00 1.000 −0.67 0.622 0.162 0.938 0.341 0.608 0.945 0.994 0.999 0.994 0.999 1.000
Source of control
HB 2 0.45 0.633 0.862 (0.452–1.645) 7.43 0.01 86.50% 0.00 1.000 0.652 0.782 0.714 0.882 0.988 0.999 1.000 0.995 0.999 1.000
PB 5 0.52 0.605 1.076 (0.815–1.422) 4.76 0.31 15.90% −0.24 1.000 −0.14 0.901 0.607 0.990 0.648 0.846 0.984 0.998 1.000 0.997 1.000 1.000
Cancer type
Lung cancer 3 0.01 0.993 1.003 (0.502–2.005) 6.63 0.04 69.80% 1.04 0.296 5.23 0.12 0.993 0.873 0.773 0.911 0.991 0.999 1.000 0.995 0.999 1.000
CC + AC/AA
Overall 14 0.52 0.606 1.065 (0.839–1.351) 42.46 0.00 69.40% 0.77 0.443 1.68 0.118 0.604 0.998 0.645 0.845 0.984 0.998 1.000 0.998 1.000 1.000
Ethnicity
Caucasian 4 0.29 0.770 0.934 (0.592–1.475) 13.96 0.00 78.50% 1.02 0.308 1.34 0.312 0.770 0.926 0.714 0.882 0.988 0.999 1.000 0.996 1.000 1.000
Asian 7 0.51 0.608 1.052 (0.868–1.275) 7.08 0.31 15.20% 1.20 0.230 −0.37 0.727 0.605 1.000 0.645 0.845 0.984 0.998 1.000 0.998 1.000 1.000
Mix 3 2.73 0.006 1.531 (1.127–2.080) 2.17 0.34 7.90% 1.04 0.296 −17.92 0.035 0.006 0.448 0.041 0.115 0.588 0.935 0.993 0.922 0.992 1.000
Source of control
HB 6 0.29 0.771 1.057 (0.728–1.535) 28.22 0.00 82.30% 0.00 1.000 2.01 0.115 0.771 0.967 0.705 0.878 0.987 0.999 1.000 0.997 1.000 1.000
PB 8 0.95 0.343 1.102 (0.901–1.348) 9.66 0.21 27.60% 0.37 0.711 −0.51 0.627 0.345 0.999 0.509 0.757 0.972 0.997 1.000 0.997 1.000 1.000
Cancer type
Lung cancer 3 0.36 0.719 1.158 (0.521–2.573) 9.42 0.01 78.80% 1.04 0.296 3.25 0.19 0.719 0.737 0.745 0.898 0.990 0.999 1.000 0.994 0.999 1.000
Colorectal cancer 3 1.02 0.307 0.853 (0.628–1.158) 1.65 0.44 0.00% 0.00 1.000 0.68 0.618 0.308 0.943 0.495 0.746 0.970 0.997 1.000 0.996 1.000 1.000
Bladder cancer 2 0.41 0.684 0.890 (0.508–1.558) 2.56 0.11 60.90% 0.00 1.000 0.683 0.844 0.708 0.879 0.988 0.999 1.000 0.995 1.000 1.000
CC/AA + AC
Overall 8 0.27 0.79 1.036 (0.800–1.340) 6.34 0.50 0.00% 0.37 0.711 0.21 0.841 0.788 0.998 0.703 0.877 0.987 0.999 1.000 0.998 1.000 1.000
Ethnicity
Caucasian 3 0.41 0.678 1.065 (0.791–1.433) 3.41 0.18 41.30% 1.04 0.296 0.69 0.614 0.677 0.988 0.673 0.861 0.985 0.999 1.000 0.997 1.000 1.000
Asian 4 0.26 0.794 0.928 (0.531–1.622) 3.1 0.38 3.20% 1.02 0.308 −1.93 0.193 0.793 0.877 0.731 0.891 0.989 0.999 1.000 0.995 1.000 1.000
Source of control
HB 3 0.37 0.711 0.946 (0.706–1.269) 2.55 0.28 21.60% 1.04 0.296 −1.08 0.474 0.711 0.990 0.683 0.866 0.986 0.999 1.000 0.997 1.000 1.000
PB 5 1.22 0.223 1.405 (0.813–2.430) 3.25 0.52 0.00% 0.73 0.462 1.01 0.388 0.224 0.593 0.531 0.773 0.974 0.997 1.000 0.992 0.999 1.000
Cancer type
Lung cancer 3 0.57 0.567 1.092 (0.808–1.474) 3.19 0.20 37.30% 1.04 0.296 1.35 0.407 0.565 0.981 0.634 0.838 0.983 0.998 1.000 0.997 1.000 1.000
non-smoker
CC/AA
Overall 7 0.97 0.332 1.239 (0.803–1.912) 3.26 0.78 0.00% 0.30 0.764 −0.14 0.891 0.333 0.806 0.553 0.788 0.976 0.998 1.000 0.994 0.999 1.000
Ethnicity
Caucasian 3 0.61 0.539 1.223 (0.643–2.328) 0.21 0.90 0.00% 0.00 1.000 0.79 0.575 0.540 0.733 0.688 0.869 0.986 0.999 1.000 0.994 0.999 1.000
Asian 3 1.49 0.136 1.664 (0.852–3.250) 0.31 0.86 0.00% 0.00 1.000 −0.16 0.899 0.136 0.381 0.517 0.763 0.973 0.997 1.000 0.988 0.999 1.000
Source of control
HB 2 0.41 0.68 1.149 (0.594–2.222) 0.03 0.87 0.00% 0.00 1.000 0.680 0.786 0.722 0.886 0.988 0.999 1.000 0.994 0.999 1.000
PB 5 0.93 0.352 1.314 (0.739–2.334) 3.11 0.54 0.00% 0.24 0.806 −0.31 0.777 0.352 0.674 0.610 0.824 0.981 0.998 1.000 0.993 0.999 1.000
Cancer type
Lung cancer 3 0.65 0.515 1.244 (0.645–2.397) 0.3 0.86 0.00% 0.00 1.000 1.75 0.33 0.514 0.712 0.684 0.867 0.986 0.999 1.000 0.994 0.999 1.000
AC/AA
Overall 7 0.1 0.924 0.975 (0.584–1.628) 24.32 0.00 75.30% 1.20 0.230 −1.83 0.126 0.923 0.927 0.749 0.900 0.990 0.999 1.000 0.996 1.000 1.000
Ethnicity
Caucasian 3 1.46 0.146 0.569 (0.266–1.216) 5.41 0.07 63.00% 0.00 1.000 −1.73 0.333 0.146 0.341 0.561 0.793 0.977 0.998 1.000 0.988 0.999 1.000
Asian 3 1.84 0.066 1.643(0.967–2.791) 4.47 0.11 55.30% 0.00 1.000 −0.86 0.549 0.066 0.368 0.351 0.618 0.947 0.994 0.999 0.983 0.998 1.000
Source of control
HB 2 0.48 0.628 1.082 (0.786–1.489) 0.86 0.35 0.00% 0.00 1.000 0.629 0.978 0.659 0.853 0.985 0.998 1.000 0.997 1.000 1.000
PB 5 0.4 0.692 0.829 (0.328–2.095) 21.53 0.00 81.40% 0.73 0.462 −2.12 0.125 0.692 0.678 0.754 0.902 0.990 0.999 1.000 0.993 0.999 1.000
Cancer type
Lung cancer 3 0.75 0.451 0.858 (0.576–1.278) 1.32 0.52 0.00% 0.00 1.000 −1.42 0.39 0.451 0.893 0.603 0.820 0.980 0.998 1.000 0.996 1.000 1.000
CC + AC/AA
Overall 14 0.2 0.843 1.030 (0.767–1.383) 35.5 0.00 63.40% 1.42 0.155 −2.27 0.043 0.844 0.994 0.718 0.884 0.988 0.999 1.000 0.997 1.000 1.000
Ethnicity
Caucasian 4 1.62 0.104 0.792 (0.597–1.050) 4.09 0.25 26.60% 1.02 0.308 −1.59 0.253 0.105 0.884 0.263 0.517 0.922 0.992 0.999 0.991 0.999 1.000
Asian 7 0.19 0.848 1.047 (0.655–1.673) 18.06 0.01 66.80% 1.20 0.230 −2.81 0.038 0.848 0.934 0.731 0.891 0.989 0.999 1.000 0.996 1.000 1.000
Mix 3 2.38 0.018 1.609 (1.087–2.381) 1.74 0.42 0.00% 0.00 1.000 −0.37 0.777 0.017 0.363 0.126 0.301 0.826 0.980 0.998 0.960 0.996 1.000
Source of control
HB 6 1.57 0.116 1.187 (0.958–1.471) 7.1 0.21 29.50% 0.75 0.452 0.96 0.391 0.117 0.984 0.263 0.518 0.922 0.992 0.999 0.994 0.999 1.000
PB 8 0.9 0.366 0.761 (0.420–1.376) 28.4 0.00 75.30% 0.37 0.711 −2.52 0.045 0.366 0.669 0.621 0.831 0.982 0.998 1.000 0.993 0.999 1.000
Cancer type
Lung cancer 3 0.42 0.676 0.923 (0.632–1.346) 0.33 0.85 0.00% 0.00 1.000 −1.65 0.347 0.677 0.955 0.680 0.865 0.986 0.999 1.000 0.996 1.000 1.000
Colorectal cancer 3 0.18 0.854 0.966 (0.669–1.396) 1.07 0.59 0.00% 0.00 1.000 −0.13 0.92 0.854 0.976 0.724 0.887 0.989 0.999 1.000 0.997 1.000 1.000
Bladder cancer 2 1.43 0.153 0.584 (0.279–1.222) 2.36 0.12 57.70% 0.00 1.000 0.153 0.363 0.559 0.792 0.977 0.998 1.000 0.989 0.999 1.000
CC/AA + AC
Overall 8 0.51 0.612 1.108 (0.745–1.647) 4.34 0.50 0.00% 0.87 0.386 0.11 0.912 0.612 0.933 0.663 0.855 0.985 0.998 1.000 0.996 1.000 1.000
Ethnicity
Caucasian 3 1.21 0.226 1.458 (0.792–2.683) 1.21 0.55 0.00% 0.00 1.000 1.13 0.461 0.226 0.536 0.558 0.791 0.977 0.998 1.000 0.991 0.999 1.000
Asian 4 0.36 0.719 1.116 (0.613–2.033) 1.8 0.61 0.00% −0.34 1.000 −0.24 0.836 0.720 0.833 0.722 0.886 0.988 0.999 1.000 0.995 0.999 1.000
Source of control
HB 3 0.03 0.98 0.992 (0.546–1.804) 1.09 0.58 0.00% 1.04 0.296 −1.61 0.354 0.979 0.904 0.765 0.907 0.991 0.999 1.000 0.995 1.000 1.000
PB 5 0.7 0.482 1.210 (0.711–2.057) 5.08 0.28 21.20% 0.73 0.462 0.59 0.599 0.481 0.786 0.647 0.846 0.984 0.998 1.000 0.995 0.999 1.000
Cancer type
Lung cancer 3 1.2 0.23 1.460 (0.787–2.709) 1.36 0.51 0.00% 0.00 1.000 1.35 0.406 0.230 0.534 0.564 0.795 0.977 0.998 1.000 0.992 0.999 1.000

Bold value indicates statistically significant OR values, values with FPRP less than 0.2, and values with BFDP less than 0.8.

Similar to the findings for C677T polymorphism, the A1298C variation was associated with an increased cancer risk among non-drinkers in the dominant model. The analysis showed a significant association with a 1.171-fold increased risk in non-drinkers. However, no significant association was observed between A1298C polymorphism and cancer risk among drinkers. Subgroup analyses indicated that the presence of the dominant variant did not correlate with cancer risk among both smokers and non-smokers. Please refer to Table 6 for detailed results.

Table 6

Integral analysis of the association between A1298C polymorphism and cancer risk among drinking population

Comparative model No. Z p OR (95% CI) Heterogeneity Z Begg’s Test t Egger’s test FPRP p-valuea FPRP statistical powerb FPRP prior probability BFDP prior probability
Heterogeneity chi-squared p I 2 0.25 0.10 0.01 0.001 0.0001 0.01 0.001 0.000010
Drinker
CC/AA
Overall 5 1.61 0.108 1.305 (0.943–1.807) 1.19 0.88 0.00% 0.24 0.806 −0.09 0.932 0.109 0.799 0.290 0.551 0.931 0.993 0.999 0.991 0.999 1.000
Ethnicity
Asian 4 0.89 0.374 1.309 (0.723–2.369) 1.19 0.76 0.00% −0.34 1.000 −0.24 0.836 0.374 0.674 0.625 0.833 0.982 0.998 1.000 0.994 0.999 1.000
Cancer type
Lung cancer 2 1.29 0.198 1.271 (0.882–1.829) 0.16 0.69 0.00% 0.00 1.000 0.197 0.814 0.420 0.685 0.960 0.996 1.000 0.993 0.999 1.000
AC/AA
Overall 5 0.88 0.377 1.078 (0.913–1.272) 6.11 0.19 34.60% 0.24 0.806 0.28 0.795 0.374 1.000 0.529 0.771 0.974 0.997 1.000 0.998 1.000 1.000
Ethnicity
Asian 4 0.49 0.626 1.093 (0.765–1.561) 6.11 0.11 50.90% 0.34 0.734 0.46 0.692 0.625 0.959 0.662 0.854 0.985 0.998 1.000 0.997 1.000 1.000
Cancer type
Lung cancer 2 0.85 0.395 1.093 (0.890–1.342) 0.04 0.85 0.00% 0.00 1.000 0.396 0.999 0.543 0.781 0.975 0.997 1.000 0.997 1.000 1.000
CC + AC/AA
Overall 12 1.65 0.100 1.115 (0.979–1.269) 18.62 0.07 40.90% 0.75 0.451 −0.50 0.625 0.099 1.000 0.229 0.471 0.908 0.990 0.999 0.996 1.000 1.000
Ethnicity
Asian 9 0.28 0.778 0.974 (0.808–1.173) 10.33 0.24 22.50% 0.52 0.602 0.00 0.999 0.781 1.000 0.701 0.875 0.987 0.999 1.000 0.998 1.000 1.000
Cancer type
Colon cancer 3 0.99 0.324 1.292 (0.777–2.148) 1.43 0.49 0.00% 0.00 1.000 0.32 0.806 0.323 0.718 0.575 0.802 0.978 0.998 1.000 0.994 0.999 1.000
Colorectal cancer 2 1.88 0.061 0.745 (0.548–1.013) 0.07 0.79 0.00% 0.00 1.000 0.060 0.761 0.192 0.417 0.887 0.988 0.999 0.986 0.999 1.000
Lung cancer 2 1.14 0.254 1.121 (0.922–1.263) 0.00 0.98 0.00% 0.00 1.000 0.061 1.000 0.154 0.353 0.857 0.984 0.998 0.994 0.999 1.000
Rectal cancer 2 1.25 0.211 0.642 (0.321–1.285) 0.75 0.39 0.00% 0.00 1.000 0.211 0.458 0.580 0.806 0.979 0.998 1.000 0.991 0.999 1.000
CC/AA + AC
Overall 6 1.33 0.183 1.224 (0.909–1.649) 2.34 0.80 0.00% 0.00 1.000 −0.40 0.708 0.184 0.909 0.377 0.645 0.952 0.995 1.000 0.994 0.999 1.000
Ethnicity
Asian 5 0.61 0.541 1.168 (0.711–1.919) 2.29 0.68 0.00% 0.24 0.806 −0.30 0.784 0.540 0.838 0.659 0.853 0.985 0.998 1.000 0.995 1.000 1.000
Cancer type
Lung cancer 2 1.13 0.258 1.225 (0.862–1.739) 0.17 0.68 0.00% 0.00 1.000 0.256 0.871 0.469 0.726 0.967 0.997 1.000 0.995 0.999 1.000
Non-drinker
CC/AA
Overall 5 0.70 0.482 1.152 (0.777–1.706) 1.92 0.75 0.00% 0.73 0.462 −2.55 0.084 0.480 0.906 0.614 0.827 0.981 0.998 1.000 0.996 1.000 1.000
Ethnicity
Asian 4 0.24 0.810 1.072 (0.610–1.884) 1.88 0.60 0.00% 1.02 0.308 −3.48 0.074 0.809 0.879 0.734 0.892 0.989 0.999 1.000 0.995 1.000 1.000
Cancer type
Lung cancer 2 0.38 0.707 1.105 (0.657–1.860) 1.10 0.29 9.10% 0.00 1.000 0.707 0.875 0.708 0.879 0.988 0.999 1.000 0.995 1.000 1.000
AC/AA
Overall 5 1.54 0.123 1.169 (0.959–1.425) 5.82 0.21 31.20% 0.24 0.806 −0.90 0.433 0.122 0.993 0.270 0.526 0.924 0.992 0.999 0.994 0.999 1.000
Ethnicity
Asian 4 0.04 0.970 0.995 (0.772–1.284) 1.93 0.59 0.00% −0.31 1.000 0.15 0.893 0.969 0.999 0.744 0.897 0.990 0.999 1.000 0.998 1.000 1.000
Cancer type
Lung cancer 2 0.31 0.759 1.119 (0.547–2.888) 3.37 0.07 70.40% 0.00 1.000 0.816 0.728 0.771 0.910 0.991 0.999 1.000 0.993 0.999 1.000
CC + AC/AA
Overall 12 2.13 0.033 1.171 (1.012–1.354) 20.68 0.04 46.80% 0.34 0.732 −1.06 0.313 0.033 1.000 0.090 0.230 0.766 0.971 0.997 0.988 0.999 1.000
Ethnicity
Asian 9 0.34 0.735 0.969 (0.806–1.164) 8.55 0.38 6.40% 0.73 0.466 −1.05 0.327 0.736 1.000 0.688 0.869 0.986 0.999 1.000 0.998 1.000 1.000
Cancer type
Colon cancer 3 0.31 0.755 1.062 (0.726–1.554) 1.16 0.56 0.00% 0.00 1.000 −1.48 0.378 0.757 0.962 0.702 0.876 0.987 0.999 1.000 0.997 1.000 1.000
Colorectal cancer 2 0.24 0.811 1.034 (0.787–1.359) 0.23 0.63 0.00% 0.00 1.000 0.811 0.996 0.709 0.880 0.988 0.999 1.000 0.998 1.000 1.000
Lung cancer 2 0.15 0.883 1.058 (0.501–2.231) 3.90 0.05 74.40% 0.00 1.000 0.882 0.820 0.763 0.906 0.991 0.999 1.000 0.994 0.999 1.000
Rectal cancer 2 0.62 0.537 0.694 (0.271–2.214) 3.62 0.06 72.40% 0.00 1.000 0.537 0.527 0.754 0.902 0.990 0.999 1.000 0.992 0.999 1.000
CC/AA + AC
Overall 6 0.03 0.973 0.994 (0.683–1.446) 3.07 0.69 0.00% 1.50 0.133 −2.16 0.097 0.975 0.982 0.749 0.899 0.990 0.999 1.000 0.997 1.000 1.000
Ethnicity
Asian 5 0.11 0.911 0.970 (0.570–1.652) 3.10 0.54 0.00% 1.71 0.086 −6.27 0.008 0.911 0.916 0.749 0.899 0.990 0.999 1.000 0.996 1.000 1.000
Cancer type
Lung cancer 2 0.21 0.830 0.946 (0.570–1.569) 0.60 0.44 0.00% 0.00 1.000 0.830 0.912 0.732 0.891 0.989 0.999 1.000 0.996 1.000 1.000

Bold value indicates statistically significant OR values, values with FPRP less than 0.2, and values with BFDP less than 0.8.

3.3 Publication bias

Possible publication bias was examined based on the result of the calculation of Begg’s test and Egger’s test. For C677T polymorphism, some results showed publication bias based on Egger’s test values (for smokers: CT + TT vs CC, p = 0.009; for non-smokers: TT vs CT + CC, p = 0.043; for drinkers: TT vs CC, p = 0.002). For A1298C polymorphism (for non-smokers: CC + AC vs AA, p = 0.043).

3.4 Sensitivity analysis

Only the homozygote comparison of smokers in the sensitivity analysis detected transformation of results for A1298C, indicating the reliability of most of our findings. To explore the sources of heterogeneity, we conducted a meta-regression using appropriate models based on I 2 and chi-square p-values. The meta-regression analysis identified the year of publication and the region of the control group as potential sources of heterogeneity.

3.5 FPRP and BFDP tests

The FPRP and BFDP values for the C677T and A1298C polymorphisms are presented in Tables 3–6. The FPRP was utilized to assess the likelihood of significant findings in the results. Based on an OR of 1.5, with a prior probability of 0.25 and 0.1, several values were found to be less than 0.2 in almost all models. However, there were only a few BFDP estimations below 0.8 when considering a prior probability of 0.01, 0.001, and 0.000001 with an OR of 1.5. It is worth noting that only the estimations with FPRP less than 0.2 and BFDP less than 0.8 are deemed significant. These significant estimations suggest that our results should be interpreted cautiously.

4 Discussion

The two SNPs of most interest in MTHFR, C677T and A1298C, are both thought to be associated with possible carcinogenesis by altering the stability of the MHTFR enzyme [61].

Although the correlation between MTHFR SNPs and cancer susceptibility has been widely studied, most meta-analyses supported an association between the MHTFR SNPs and an increased risk of cancer [62,63,64]. Here, as possible cancer risk factors, tobacco and alcohol consumption might have direct or indirect synergistic actions on cancers with enzymes regulated by these SNPs. We decided to use this meta-analysis to assess the association of MTHFR C677T or A1298C polymorphism alone and in combination with smoking or drinking on cancers.

Chemicals found in tobacco, such as nitrosamines, can cause DNA recombination, a process that can lead to mutations in other cancer related genes, thereby increasing the risk of the disease [65].

Our research suggests that smoking increases the overall risk of cancer in individuals with the C677T variant. However, no such effect was found in carriers of the A1298C variant. This may be related to the loss of enzyme activity caused by MTHFR mutation, where the enzyme activity of A1298C mutant is reduced to a lesser extent than C677T. Individuals carrying the TT genotype of the C677T polymorphism exhibit elevated levels of total homocysteine (tHcy) and reduced levels of folate in their serum compared to those with the CT and CC genotypes [66]. These mutations lead to a reduction in the universal methyl donor SAM during folic acid metabolism, which may result in genomic instability and DNA fragmentation. Under normal conditions, cells maintain a standard methylation pattern by balancing the DNA methylation and demethylation processes. This balance is disrupted under pathological conditions such as inflammation, oxidative stress, and cancer, leading to different phenotypes [67].

Moreover, previous studies have demonstrated that hydrocarbons in tobacco reduce the bioactivity of vitamin B12 and folic acid. Smokers in the National Center for Health Statistics study also had lower levels of red blood cell folate compared to non-smokers [68]. Similarly, a study showed that pregnant women exposed to smoking had lower levels of folic acid compared to women not exposed to tobacco [69].

Furthermore, a significantly elevated risk of carriers harboring the C677T variant was observed in Asian smokers while not in Caucasian smokers, which is consistent with a previous study [62]. This could be attributed to the differences in the frequency of the T allele. Xie et al. in 2015 reported that there were prominent differences in T allele frequencies among Asian (0.396), Indian (0.132), Caucasian (0.326), Middle Eastern (0.201), and African (0.196) populations [63].

Therefore, it is possible that the combined effects of tobacco and C677T on cancers disrupt DNA methylation pathways through folic acid metabolism. However, some researchers have found that the cancer-promoting effect of MTHFR polymorphism may not be influenced by smoking [70,71].

Our study revealed varying combined effects of the C677T polymorphism and smoking on different types of cancer. Interestingly, the role of smoking habits in promoting esophageal cancer risk was similar among both smokers and non-smokers, suggesting that smoking may not be as significant of a factor in esophageal cancer as previously believed. However, it remains an independent factor, as the presence of the C677T polymorphism increased the risk of esophageal cancer in our study, as well as in the meta-analysis conducted by Langevin et al. [72]. Consistent with previous evidence, we also observed that smoking was not a risk factor for colorectal [73] and bladder cancers [74]. The combined effect of tobacco and C677T mutation on liver cancer was the strongest, 1.564 for smokers and 1.072 for nonsmoker. This finding aligns with the meta-analysis conducted by Qi et al. [75], which identified the C677T mutation as a risk factor for liver cancer. In contrast, the situation is different for esophageal cancer, as individuals with the C677T polymorphism who smoke show a significant increase in the risk of liver cancer. This observation can possibly be attributed to the fact that folate metabolism primarily takes place in the liver [76]. An animal study demonstrated that a folate-deficient diet had an impact on gene expression in the liver of offspring mice but not in the colon. Additionally, smoking has been shown to exacerbate folate acid levels. This could be a significant contributing factor to the heightened risk of liver cancer associated with the combination of smoking and the C677T polymorphism. As for A1298C, the majority of studies indicate that it is not associated with an increased risk of cancer and may even have a protective effect against liver cancer [77], gastric cancer [78], and lung cancer [9], which supports the findings of our study, even when considering the influence of smoking. In brief, smoking might be an essential factor in the carcinogenesis in individuals with the C677T mutation, particularly increasing the risk of cancers in Asians, including liver cancer. On the other hand, the combined effect of smoking and A1298C polymorphism on cancers was not significant.

Alcohol is known to act as a folic acid antagonist, and excessive consumption can lead to folate deficiency due to reduced intake of food rich in micronutrients, impaired in intestinal absorption, and changes in metabolic pathways [79]. Thus, we anticipated that alcohol consumption could potentially induce DNA hypomethylation through the aforementioned mechanisms, leading to cancer. When investigating the effect of alcohol consumption on cancer risk, specifically for C677T polymorphism, our findings indicated a decreased risk of cancer among drinkers compared to non-drinkers, particularly for esophageal and colorectal cancer. This finding was contrary to our initial expectations. In a previous study, Taioli et al. showed that the protective effect of C677T variant on colorectal cancer was limited to individuals who regularly consumed alcohol and might be influenced by the folate level [73]. A Japanese case-control study also found a 69% lower risk of esophageal cancer in patients with the TT genotype in the heavy drinking subgroup [53]. Therefore, in addition to alcohol consumption, we suspect that the reduced risk of cancer in drinkers with MHTFR variants may be due to ignoring the interaction between folic acid intake and individual MTHFR variants. Adequate folate intake could offset the lower enzymatic activity of MTHFR that may increase the MTHFR and promote DNA synthesis, while the adequate provision of methyl donors could still be ensured [80]. Plenty of studies have also shown that the protective effect of the TT genotype is limited to individuals with high folic acid intake and low alcohol intake [81,82,83]. However, because of lack of detailed raw data on the association between smoking, drinking, and folic acid intake, we cannot further explore the relationship between MHTFR polymorphisms and smoking-folic acid associations or drinking-folic acid associations with cancers. In mixed populations, we observed a lower risk of cancer among drinkers with the C677T variant. For the A1298C polymorphism, we only observed a decreased overall cancer risk in the dominant model. In brief, alcohol consumption may act as a protective factor for cancer incidence in individuals with MTHFR variants. The protective effect appears to be organ-specific and race-specific for the C677T polymorphism, particularly under conditions of adequate folate levels.

When interpreting the results of our meta-analysis, it is important to acknowledge several limitations. First, we focused on studying the individual associations of the C677T and A1298C polymorphisms with cancer risk, without considering their combined effects or the influence of smoking and drinking. However, the available data on the combined effects and interactions between these polymorphisms, as well as smoking and drinking, were insufficient for further analysis. Second, due to limitations in the available data, we were unable to categorize smoking and alcohol consumption into detailed subgroups based on intensity (light/medium/heavy). Instead, we defined individuals as current/ever smokers or non-smokers, as well as drinkers or non-drinkers. This simplified classification may not fully capture the potential nuances of smoking and alcohol consumption patterns.

In conclusion, our study highlights the significant influence of smoking and drinking on the relationship between MTHFR polymorphisms and cancer development. The interaction between smoking and the C677T mutation was associated with an increased risk of cancers, particularly liver cancer, and this effect was most prominent in Asian populations. However, the A1298C polymorphism did not show a significant association with cancer risk, even in the presence of tobacco exposure. Conversely, a negative association was observed between alcohol consumption and cancer risk among individuals with either the C677T or A1298C mutation. Future studies with larger sample sizes are needed to further explore the combined effects of tobacco or alcohol and MTHFR polymorphisms at varying folate levels in cancer development (Figure 2).

Figure 2 Schematic of folate metabolism. Effects of reduced MTHFR activity on DNA synthesis and methylation. DHF, dihydrofolate; THF, tetrahydrofolate; SAM, S-adenosyl methionine; Hcy, homocysteine; SAH, S-adenosyl homocysteine.
Figure 2

Schematic of folate metabolism. Effects of reduced MTHFR activity on DNA synthesis and methylation. DHF, dihydrofolate; THF, tetrahydrofolate; SAM, S-adenosyl methionine; Hcy, homocysteine; SAH, S-adenosyl homocysteine.

  1. Funding information: This work was supported by grants from Guangxi Natural Science Foundation (Grant No.: 2019GXNSFAA245085) and Guangxi Medical and health suitable technology development and popularization application project (Grant No.: S2021084).

  2. Author contributions: C.P.L. – Conceptualization; Y.H.H., Q.R.H., Z.X.W., L.C., Y.L. – Methodology and Data collection; Y.H.H., C.P.L., X.J.L. – Calculation and Analyses; Y.H.H. – Original manuscript; Y.H.H., C.P.L., X.J.L. – Manuscript review, polishes and editing. All authors read and approved the final manuscript.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

[1] Syvänen AC. Accessing genetic variation: genotyping single nucleotide polymorphisms. Nat Rev Genet. 2001;2:930–42. 10.1038/35103535.Suche in Google Scholar PubMed

[2] Schork NJ, Fallin D, Lanchbury JS. Single nucleotide polymorphisms and the future of genetic epidemiology. Clin Genet. 2000;58:250–64. 10.1034/j.1399-0004.2000.580402.x.Suche in Google Scholar PubMed

[3] Pieroth R, Paver S, Day S, Lammersfeld C. Folate and its impact on cancer risk. Curr Nutr Rep. 2018;7:70–84. 10.1007/s13668-018-0237-y.Suche in Google Scholar PubMed PubMed Central

[4] Peterson CT, Rodionov DA, Osterman AL, Peterson SN. B vitamins and their role in immune regulation and cancer. Nutrients. 2020;12:3380. 10.3390/nu12113380.Suche in Google Scholar PubMed PubMed Central

[5] Rai V. Association of C677T polymorphism (rs1801133) in MTHFR gene with depression. Cell Mol Biol (Noisy-le-grand). 2017;63:60–7.10.14715/cmb/2017.63.6.13Suche in Google Scholar PubMed

[6] Rai V. Evaluation of methylenetetrahydrofolate reductase gene variant (C677T) as risk factor for bipolar disorder. Cell Mol Biol (Noisy-le-grand). 2011;57 Suppl:OL1558-66.Suche in Google Scholar

[7] van der Put NM, Gabreëls F, Stevens EM, Smeitink JA, Trijbels FJ, Eskes TK, et al. A second common mutation in the methylenetetrahydrofolate reductase gene: an additional risk factor for neural-tube defects. Am J Hum Genet. 1998;62:1044–51. 10.1086/301825.Suche in Google Scholar PubMed PubMed Central

[8] Kumar P, Yadav U, V, Rai. Methylenetetrahydrofolate reductase gene C677T polymorphism and breast cancer risk: Evidence for genetic susceptibility. Meta Gene. 2015;6:72–84. 10.1016/j.mgene.2015.08.008.Suche in Google Scholar PubMed PubMed Central

[9] Yang Y, Yang LJ, Deng MZ, Luo YY, Wu S, Xiong L, et al. MTHFR C677T and A1298C polymorphisms and risk of lung cancer: a comprehensive evaluation. Genet Mol Res. 2016;15:5–6. 10.4238/gmr.15027615.Suche in Google Scholar PubMed

[10] Nazki FH, Sameer AS, Ganaie BA. Folate: metabolism, genes, polymorphisms and the associated diseases. Gene. 2014;533:11–20. 10.1016/j.gene.2013.09.063.Suche in Google Scholar PubMed

[11] Rai V, Yadav U, Kumar P. Genotype prevalence and allele frequencies of 5,10-methylenetetrahydrofolate reductase (MTHFR) C677T mutation in two caste groups of India. Cell Mol Biol (Noisy-le-grand). 2012;58 Suppl:OL1695-701.Suche in Google Scholar

[12] Singh S, Choudhuri G, Kumar R, Agarwal S. Association of 5, 10- methylenetetrahydrofolate reductase C677T polymorphism in susceptibility to tropical chronic pancreatitis in north Indian population. Cell Mol Biol (Noisy-le-grand). 2012;58:122–7.Suche in Google Scholar

[13] Rai V. Methylenetetrahydrofolate reductase gene A1298C polymorphism and susceptibility to recurrent pregnancy loss: a meta-analysis. Cell Mol Biol (Noisy-le-grand). 2014;60:27–34.Suche in Google Scholar

[14] Luetragoon T, Rutqvist LE, Tangvarasittichai O, Andersson BÅ, Löfgren S, Usuwanthim K, et al. Interaction among smoking status, single nucleotide polymorphisms and markers of systemic inflammation in healthy individuals. Immunology. 2018;154:98–103.10.1111/imm.12864Suche in Google Scholar PubMed PubMed Central

[15] Andersson BÅ, Sayardoust S, Löfgren S, Rutqvist LE, Laytragoon-Lewin N. Cigarette smoking affects microRNAs and inflammatory biomarkers in healthy individuals and an association to single nucleotide polymorphisms is indicated. Biomarkers. 2019;24:180–5.10.1080/1354750X.2018.1539764Suche in Google Scholar PubMed

[16] Semmler A, Heese P, Stoffel-Wagner B, Muschler M, Heberlein A, Bigler L, et al. Alcohol abuse and cigarette smoking are associated with global DNA hypermethylation: results from the German Investigation on Neurobiology in Alcoholism (GINA). Alcohol. 2015;49:97–101.10.1016/j.alcohol.2015.01.004Suche in Google Scholar PubMed

[17] Rumgay H, Murphy N, Ferrari P, Soerjomataram I. Alcohol and cancer: epidemiology and biological mechanisms. Nutrients. 2021;13:3173.10.3390/nu13093173Suche in Google Scholar PubMed PubMed Central

[18] Wacholder S, Chanock S, Garcia-Closas M, El Ghormli L, Rothman N. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst. 2004;96:434–42. 10.1093/jnci/djh075.Suche in Google Scholar PubMed PubMed Central

[19] Wakefield J. A Bayesian measure of the probability of false discovery in genetic epidemiology studies. Am J Hum Genet. 2007;81:208–27. 10.1086/519024.Suche in Google Scholar PubMed PubMed Central

[20] Arslan S, Karadayi S, Yildirim ME, Ozdemir O, Akkurt I. The association between methylene-tetrahydrofolate reductase gene polymorphism and lung cancer risk. Mol Biol Rep. 2011;38:991–6. 10.1007/s11033-010-0194-z.Suche in Google Scholar PubMed

[21] Bi J. MTHFR C677T and P53 Condon 72 Pro/Arg Polymorphisms and Susceptibilities to Cardia and Non-cardia Gastric cancer, Master’s thesis, Fujian Medical University, 2005, pp. 18–22. [in Chinese]Suche in Google Scholar

[22] Zhou Z. Study on Role of environment-Dietary Factors, Genetic Susceptibility and Their Interaction in Colorectal Cancer Risk. PHD thesis, Forth Military Medical University, 2005, pp. 142–147. [in Chinese]Suche in Google Scholar

[23] Cao Y, Miao XP, Huang MY, Deng L, Liang XM, Lin DX, et al. Polymorphisms of methylenetetrahydrofolate reductase are associated with a high risk of nasopharyngeal carcinoma in a smoking population from Southern China. Mol Carcinog. 2010;49:928–34. 10.1002/mc.20669.Suche in Google Scholar PubMed

[24] Changming G, Jianzhong W, Jianhua D, Yanting L, Yu Z, Suping L, et al. Polymorphisms of methylenetetrahydrofolate reductase C677T and the risk of stomach cancer. Zhonghua Liu Xing Bing Xue Za Zhi. 2002;23:289–92. 10.3760/j.issn:0254-6450.2002.04.013.Suche in Google Scholar

[25] Song L. Molecular epidemiology study on gene polymorphisms of one-carbon metabolic enzymes and related environment exposure. Master's thesis, Zhejiang University, 2006, pp. 34–37. [in Chinese]Suche in Google Scholar

[26] Ding H, Wang Y, Chen Y, Liu C, Qiu H, Kang M, et al. Methylenetetrahydrofolate reductase tagging polymorphisms are associated with risk of non-small cell lung cancer in eastern Chinese Han population. Oncotarget. 2017;8:110326–36. 10.18632/oncotarget.22887.Suche in Google Scholar PubMed PubMed Central

[27] Galbiatti AL, Ruiz MT, Rodrigues JO, Raposo LS, Maníglia JV, Pavarino ÉC, et al. Polymorphisms and haplotypes in methylenetetrahydrofolate reductase gene and head and neck squamous cell carcinoma risk. Mol Biol Rep. 2012;39:635–43. 10.1007/s11033-011-0781-7.Suche in Google Scholar PubMed

[28] Han Z, Sheng H, Gao Q, Fan Y, Xie X. Associations of the MTHFR rs1801133 polymorphism with gastric cancer risk in the Chinese Han population. Biomed Rep. 2021;14:14. 10.3892/br.2020.1390.Suche in Google Scholar PubMed PubMed Central

[29] He Y, Wang Y, Zhang J, Li Y, Guo W, Wang N. Correlation between a Polymorphism in the Methylene Tetrahydrofolate Reductase Gene and Susceptibility to Carcinoma of the Esophagus and Gastric Cardia. Chin J Clin Oncol. 2007;34:194–7. 10.3969/j.issn.1000-8179.2007.04.004.Suche in Google Scholar

[30] Jeng YL, Wu MH, Huang HB, Lin WY, You SL, Chu TY, et al. The methylenetetrahydrofolate reductase 677C - > T polymorphism and lung cancer risk in a Chinese population. Anticancer Res. 2003;23:5149–52.Suche in Google Scholar

[31] Jiang Q, Chen K, Ma X, Li Q, Yu W, Shu G, et al. Diets, polymorphisms of methylenetetrahydrofolate reductase, and the susceptibility of colon cancer and rectal cancer. Cancer Detect Prev. 2005;29:146–54. 10.1016/j.cdp.2004.11.004.Suche in Google Scholar PubMed

[32] Jianzhong Wu CG, Jianhua Zhang YL, Yu Zang SL, Xu Hu TT. Polymorphisms of methylenetetrahydrofolate reductase C677T and the risk of esophageal cancer. Tumor. 2002;268–70.Suche in Google Scholar

[33] Lin J, Spitz MR, Wang Y, Schabath MB, Gorlov IP, Hernandez LM, et al. Polymorphisms of folate metabolic genes and susceptibility to bladder cancer: a case-control study. Carcinogenesis. 2004;25:1639–47. 10.1093/carcin/bgh175.Suche in Google Scholar PubMed

[34] Lin KM, Yang MD, Tsai CW, Chang WS, Hsiao CL, Jeng LB, et al. The role of MTHFR genotype in colorectal cancer susceptibility in Taiwan. Anticancer Res. 2018;38:2001–6. 10.21873/anticanres.12438.Suche in Google Scholar PubMed

[35] Liu CS, Tsai CW, Hsia TC, Wang RF, Liu CJ, Hang LW, et al. Interaction of methylenetetrahydrofolate reductase genotype and smoking habit in Taiwanese lung cancer patients. Cancer Genomics Proteom. 2009;6:325–9. http://ir.cmu.edu.tw/ir/handle/310903500/27914.Suche in Google Scholar

[36] Matsuo K, Hamajima N, Hirai T, Kato T, Inoue M, Takezaki T, et al. Methionine synthase reductase gene A66G polymorphism is associated with risk of colorectal cancer. Asian Pac J Cancer Prev. 2002;3:353–9.Suche in Google Scholar

[37] Mu L, Cao W, Zhang Z, Cai L, Jiang Q, You N, et al. Methylenetetrahydrofolate reductase (MTHFR) C677T and A1298C polymorphisms and the risk of primary Hepatocellular Carcinoma (HCC) in a Chinese population. Cancer Causes Control. 2007;18:665–75. 10.1007/s10552-007-9012-x.Suche in Google Scholar PubMed PubMed Central

[38] Mu LN, Ding BG, Chen CW, Wei GR, Zhou XF, Wang RH, et al. A case-control study on the relationship between methyl-tetra-hydrofolic acid reductase 677 gene polymorphism and the risk of stomach cancer. Zhonghua Liu Xing Bing Xue Za Zhi. 2004;25:495–8. 10.3760/j.issn:0254-6450.2004.06.009.Suche in Google Scholar

[39] Nakao H, Wakai K, Ishii N, Kobayashi Y, Ito K, Yoneda M, et al. Associations between polymorphisms in folate-metabolizing genes and pancreatic cancer risk in Japanese subjects. BMC Gastroenterol. 2016;16:83. 10.1186/s12876-016-0503-7.Suche in Google Scholar PubMed PubMed Central

[40] Ni X, Tai J, Ma L, Huang Z, Fang J, Chen X, et al. Association between genetic polymorphisms in methylenetetrahydrofolate reductase and risk of laryngeal squamous cell carcinoma. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 2008;43:435–8. 10.3321/j.issn:1673-0860.2008.06.009.Suche in Google Scholar

[41] Peres NP, Galbiatti-Dias AL, Castanhole-Nunes MM, da Silva RF, Pavarino ÉC, Goloni-Bertollo EM, et al. Polymorphisms of folate metabolism genes in patients with cirrhosis and hepatocellular carcinoma. World J Hepatol. 2016;8:1234–43. 10.4254/wjh.v8.i29.1234.Suche in Google Scholar PubMed PubMed Central

[42] Platek Mary E, Shields Peter G, Catalin M, McCann Susan E, Bonner Matthew R, Jing N, et al. Alcohol consumption and genetic variation in methylenetetrahydrofolate reductase and 5-methyltetrahydrofolate-homocysteine methyltransferase in relation to breast cancer risk. Cancer Epidemiol Biomarkers Prev. 2009;18:15–6. 10.1158/1055-9965.EPI-09-0159.Suche in Google Scholar PubMed PubMed Central

[43] Rouissi K, Ouerhani S, Oliveira E, Marrakchi R, Cherni L, Ben Othman F, et al. Polymorphisms in one-carbon metabolism pathway genes and risk for bladder cancer in a Tunisian population. Cancer Genet Cytogenet. 2009;195:43–53. 10.1016/j.cancergencyto.2009.06.007.Suche in Google Scholar PubMed

[44] Shi Q, Zhang Z, Li G, Pillow PC, Hernandez LM, Spitz MR, et al. Sex differences in risk of lung cancer associated with methylene-tetrahydrofolate reductase polymorphisms. Cancer Epidemiol Biomarkers Prev. 2005;14:1477–84. 10.1158/1055-9965.EPI-04-0905.Suche in Google Scholar PubMed

[45] Suzuki T, Matsuo K, Hasegawa Y, Hiraki A, Wakai K, Hirose K, et al. One-carbon metabolism-related gene polymorphisms and risk of head and neck squamous cell carcinoma: case-control study. Cancer Sci. 2007;98:1439–46. 10.1111/j.1349-7006.2007.00533.x.Suche in Google Scholar PubMed

[46] Suzuki T, Matsuo K, Hiraki A, Saito T, Sato S, Yatabe Y, et al. Impact of one-carbon metabolism-related gene polymorphisms on risk of lung cancer in Japan: a case control study. Carcinogenesis. 2007;28:1718–25. 10.1093/carcin/bgm104.Suche in Google Scholar PubMed

[47] Suzuki T, Matsuo K, Sawaki A, Mizuno N, Hiraki A, Kawase T, et al. Alcohol drinking and one-carbon metabolism-related gene polymorphisms on pancreatic cancer risk. Cancer Epidemiol Biomarkers Prev. 2008;17:2742–7. 10.1158/1055-9965.EPI-08-0470.Suche in Google Scholar PubMed

[48] Svensson T, Yamaji T, Budhathoki S, Hidaka A, Iwasaki M, Sawada N, et al. Alcohol consumption, genetic variants in the alcohol- and folate metabolic pathways and colorectal cancer risk: the JPHC Study. Sci Rep. 2016;6:36607. 10.1038/srep36607.Suche in Google Scholar PubMed PubMed Central

[49] Tsai CW, Hsu CF, Tsai MH, Tsou YA, Hua CH, Chang WS, et al. Methylenetetrahydrofolate reductase (MTHFR) genotype, smoking habit, metastasis and oral cancer in Taiwan. Anticancer Res. 2011;31:2395–9.Suche in Google Scholar

[50] Wang L, Miao X, Tan W, Lu X, Zhao P, Zhao X, et al. Genetic polymorphisms in methylenetetrahydrofolate reductase and thymidylate synthase and risk of pancreatic cancer. Clin Gastroenterol Hepatol. 2005;3:743–51. 10.1016/s1542-3565(05)00156-4.Suche in Google Scholar PubMed

[51] Wang Y, Guo W, He Y, Chen Z, Wen D, Zhang X, et al. Association of MTHFR C677T and SHMT(1) C1420T with susceptibility to ESCC and GCA in a high incident region of Northern China. Cancer Causes Control. 2007;18:143–52. 10.1007/s10552-006-0097-4.Suche in Google Scholar PubMed

[52] Wu HC, Chang CH, Tsai RY, Lin CH, Wang RF, Tsai CW, et al. Significant association of methylenetetrahydrofolate reductase single nucleotide polymorphisms with prostate cancer susceptibility in Taiwan. Anticancer Res. 2010;30:3573–7. 10.1097/CAD.0b013e32833d19f0.Suche in Google Scholar PubMed

[53] Yang CX, Matsuo K, Ito H, Shinoda M, Hatooka S, Hirose K, et al. Gene-environment interactions between alcohol drinking and the MTHFR C677T polymorphism impact on esophageal cancer risk: results of a case-control study in Japan. Carcinogenesis. 2005;26:1285–90. 10.1093/carcin/bgi076.Suche in Google Scholar PubMed

[54] Zara-Lopes T, Gimenez-Martins AP, Nascimento-Filho CH, Castanhole-Nunes MM, Galbiatti-Dias AL, Padovani-Júnior JA, et al. Role of MTHFR C677T and MTR A2756G polymorphisms in thyroid and breast cancer development. Genet Mol Res. 2016;15:6–7. 10.4238/gmr.15028222.Suche in Google Scholar PubMed

[55] Zhang J, Zotz RB, Li Y, Wang R, Kiel S, Schulz WA, et al. Methylenetetrahydrofolate reductase C677T polymorphism and predisposition towards esophageal squamous cell carcinoma in a German Caucasian and a northern Chinese population. Medizinhist J. 2004;130:574–80.10.1007/s00432-004-0585-4Suche in Google Scholar PubMed

[56] Zhao P, Lin F, Li Z, Lin B, Lin J, Luo R. Folate intake, methylenetetrahydrofolate reductase polymorphisms, and risk of esophageal cancer. Asian Pac J Cancer Prev. 2011;12:2019–23. 10.1007/978-3-642-16602-0_13.Suche in Google Scholar

[57] Álvarez-Avellón SM, Fernández-Somoano A, Navarrete-Muñoz EM, Vioque J, Tardón A. Effect of alcohol and its metabolites in lung cancer: CAPUA study. Med Clin (Barc). 2017;148:531–8. 10.1016/j.medcli.2016.12.033.Suche in Google Scholar PubMed

[58] Al-Motassem Y, Shomaf M, Said I, Berger S, Ababneh N, Diab O, et al. Allele and genotype frequencies of the polymorphic methylenetetrahydrofolate reductase and lung cancer in the Jordanian population: a case control study. Asian Pac J Cancer Prev. 2015;16:3101–9. 10.7314/apjcp.2015.16.8.3101.Suche in Google Scholar PubMed

[59] Naghibalhossaini F, Mokarram P, Khalili I, Vasei M, Hosseini SV, Ashktorab H, et al. MTHFR C677T and A1298C variant genotypes and the risk of microsatellite instability among Iranian colorectal cancer patients. Cancer Genet Cytogenet. 2010;197:142–51. 10.1016/j.cancergencyto.2009.11.014.Suche in Google Scholar PubMed PubMed Central

[60] Promthet SS, Pientong C, Ekalaksananan T, Wiangnon S, Poomphakwaen K, Songserm N, et al. Risk factors for colon cancer in Northeastern Thailand: interaction of MTHFR codon 677 and 1298 genotypes with environmental factors. J Epidemiol. 2010;20:329–38. 10.2188/jea.je20090140.Suche in Google Scholar PubMed PubMed Central

[61] Hung RJ, Hashibe M, McKay J, Gaborieau V, Szeszenia-Dabrowska N, Zaridze D, et al. Folate-related genes and the risk of tobacco-related cancers in Central Europe. Carcinogenesis. 2007;28:1334–40.10.1093/carcin/bgm067Suche in Google Scholar PubMed

[62] Tang M, Wang SQ, Liu BJ, Cao Q, Li BJ, Li PC, et al. The methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism and tumor risk: evidence from 134 case-control studies. Mol Biol Rep. 2014;41:4659–73. 10.1007/s11033-014-3337-9.Suche in Google Scholar PubMed

[63] Xie SZ, Liu ZZ, Yu JH, Liu L, Wang W, Xie DL, et al. Association between the MTHFR C677T polymorphism and risk of cancer: evidence from 446 case-control studies. Tumour Biol. 2015;36:8953–72. 10.1007/s13277-015-3648-z.Suche in Google Scholar PubMed

[64] Zhu XL, Liu ZZ, Yan SX, Wang W, Chang RX, Zhang CY, et al. Association between the MTHFR A1298C polymorphism and risk of cancer: evidence from 265 case-control studies. Mol Genet Genomics. 2016;291:51–63. 10.1007/s00438-015-1082-y.Suche in Google Scholar PubMed

[65] Hecht SS, Hatsukami DK. Smokeless tobacco and cigarette smoking: chemical mechanisms and cancer prevention. Nat Rev Cancer. 2022;22:143–55.10.1038/s41568-021-00423-4Suche in Google Scholar PubMed PubMed Central

[66] Hiraoka M, Kagawa Y. Genetic polymorphisms and folate status. Congenit Anom (Kyoto). 2017;57:142–9. 10.1111/cga.12232.Suche in Google Scholar PubMed PubMed Central

[67] Mahmoud AM, Ali MM. Methyl donor micronutrients that modify DNA methylation and cancer outcome. Nutrients. 2019;11:608.10.3390/nu11030608Suche in Google Scholar PubMed PubMed Central

[68] Mannino DM, Mulinare J, Ford ES, Schwartz J. Tobacco smoke exposure and decreased serum and red blood cell folate levels: data from the Third National Health and Nutrition Examination Survey. Nicotine Tob Res. 2003;5:357–62.10.1080/1462220031000094330Suche in Google Scholar PubMed

[69] Tuenter A, Bautista Nino PK, Vitezova A, Pantavos A, Bramer WM, Franco OH, et al. Folate, vitamin B12, and homocysteine in smoking-exposed pregnant women: A systematic review. Matern Child Nutr. 2019;15:e12675.10.1111/mcn.12675Suche in Google Scholar PubMed PubMed Central

[70] Cui LH, Shin MH, Kim HN, Song HR, Piao JM, Kweon SS, et al. Methylenetetrahydrofolate reductase C677T polymorphism in patients with lung cancer in a Korean population. BMC Med Genet. 2011;12:28. 10.1186/1471-2350-12-28.Suche in Google Scholar PubMed PubMed Central

[71] Moore LE, Wiencke JK, Bates MN, Zheng S, Rey OA, Smith AH. Investigation of genetic polymorphisms and smoking in a bladder cancer case-control study in Argentina. Cancer Lett. 2004;211:199–207. 10.1016/j.canlet.2004.04.011.Suche in Google Scholar PubMed

[72] Langevin SM, Lin D, Matsuo K, Gao CM, Takezaki T, Stolzenberg-Solomon RZ, et al. Review and pooled analysis of studies on MTHFR C677T polymorphism and esophageal cancer. Toxicol Lett. 2009;184:73–80. 10.1016/j.toxlet.2008.09.003.Suche in Google Scholar PubMed PubMed Central

[73] Taioli E, Garza MA, Ahn YO, Bishop DT, Bost J, Budai B, et al. Meta- and pooled analyses of the methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism and colorectal cancer: a HuGE-GSEC review. Am J Epidemiol. 2009;170:1207–21. 10.1093/aje/kwp275.Suche in Google Scholar PubMed PubMed Central

[74] Kouidhi S, Rouissi K, Khedhiri S, Ouerhani S, Cherif M, Benammar-Elgaaied A. MTHFR gene polymorphisms and bladder cancer susceptibility: a meta-analysis including race, smoking status and tumour stage. Asian Pac J Cancer Prev. 2011;12:2227–32. 10.3233/CBM-2012-0253.Suche in Google Scholar PubMed

[75] Qi YH, Yao LP, Cui GB, Liang J, Shao QJ, Yan LF, et al. Meta-analysis of MTHFR C677T and A1298C gene polymorphisms: association with the risk of hepatocellular carcinoma. Clin Res Hepatol Gastroenterol. 2014;38:172–80. 10.1016/j.clinre.2013.10.002.Suche in Google Scholar PubMed

[76] Medici V, Halsted CH. Folate, alcohol, and liver disease. Mol Nutr Food Res. 2013;57:596–606.10.1002/mnfr.201200077Suche in Google Scholar PubMed PubMed Central

[77] Liang TJ, Liu H, Zhao XQ, Tan YR, Jing K, Qin CY. Quantitative assessment of the association between MTHFR rs1801131 polymorphism and risk of liver cancer. Tumour Biol. 2014;35:339–43. 10.1007/s13277-013-1046-y.Suche in Google Scholar PubMed

[78] Xia LZ, Liu Y, Xu XZ, Jiang PC, Ma G, Bu XF, et al. Methylenetetrahydrofolate reductase C677T and A1298C polymorphisms and gastric cancer susceptibility. World J Gastroenterol. 2014;20:11429–38. 10.3748/wjg.v20.i32.11429.Suche in Google Scholar PubMed PubMed Central

[79] Lieber CS. Relationships between nutrition, alcohol use, and liver disease. Alcohol Res Health. 2003;27:220–31. 10.1093/alcalc/agg021.Suche in Google Scholar PubMed

[80] Liu YX, Wang B, Wan MH, Tang WF, Huang FK, Li C. Meta-analysis of the relationship between the Methylenetetrahydrofolate reductase C677T genetic polymorphism, folate intake and esophageal cancer. Asian Pac J Cancer Prev. 2011;12:247–52. 10.1097/01.cad.0000390767.85658.83.Suche in Google Scholar PubMed

[81] Chen J, Giovannucci E, Kelsey K, Rimm EB, Stampfer MJ, Colditz GA, et al. A methylenetetrahydrofolate reductase polymorphism and the risk of colorectal cancer. Cancer Res. 1996;56:4862–4. 10.3109/9781439808078.006.Suche in Google Scholar

[82] Le Marchand L, Donlon T, Hankin JH, Kolonel LN, Wilkens LR, Seifried A. B-vitamin intake, metabolic genes, and colorectal cancer risk (United States). Cancer Causes Control. 2002;13:239–48. 10.1023/a:1015057614870.Suche in Google Scholar PubMed

[83] Le Marchand L, Wilkens LR, Kolonel LN, Henderson BE. The MTHFR C677T polymorphism and colorectal cancer: the multiethnic cohort study. Cancer Epidemiol Biomarkers Prev. 2005;14:1198–203. 10.1158/1055-9965.EPI-04-0840.Suche in Google Scholar PubMed

Received: 2023-02-27
Revised: 2023-05-17
Accepted: 2023-07-18
Published Online: 2023-09-26

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artikel in diesem Heft

  1. Biomedical Sciences
  2. Systemic investigation of inetetamab in combination with small molecules to treat HER2-overexpressing breast and gastric cancers
  3. Immunosuppressive treatment for idiopathic membranous nephropathy: An updated network meta-analysis
  4. Identifying two pathogenic variants in a patient with pigmented paravenous retinochoroidal atrophy
  5. Effects of phytoestrogens combined with cold stress on sperm parameters and testicular proteomics in rats
  6. A case of pulmonary embolism with bad warfarin anticoagulant effects caused by E. coli infection
  7. Neutrophilia with subclinical Cushing’s disease: A case report and literature review
  8. Isoimperatorin alleviates lipopolysaccharide-induced periodontitis by downregulating ERK1/2 and NF-κB pathways
  9. Immunoregulation of synovial macrophages for the treatment of osteoarthritis
  10. Novel CPLANE1 c.8948dupT (p.P2984Tfs*7) variant in a child patient with Joubert syndrome
  11. Antiphospholipid antibodies and the risk of thrombosis in myeloproliferative neoplasms
  12. Immunological responses of septic rats to combination therapy with thymosin α1 and vitamin C
  13. High glucose and high lipid induced mitochondrial dysfunction in JEG-3 cells through oxidative stress
  14. Pharmacological inhibition of the ubiquitin-specific protease 8 effectively suppresses glioblastoma cell growth
  15. Levocarnitine regulates the growth of angiotensin II-induced myocardial fibrosis cells via TIMP-1
  16. Age-related changes in peripheral T-cell subpopulations in elderly individuals: An observational study
  17. Single-cell transcription analysis reveals the tumor origin and heterogeneity of human bilateral renal clear cell carcinoma
  18. Identification of iron metabolism-related genes as diagnostic signatures in sepsis by blood transcriptomic analysis
  19. Long noncoding RNA ACART knockdown decreases 3T3-L1 preadipocyte proliferation and differentiation
  20. Surgery, adjuvant immunotherapy plus chemotherapy and radiotherapy for primary malignant melanoma of the parotid gland (PGMM): A case report
  21. Dosimetry comparison with helical tomotherapy, volumetric modulated arc therapy, and intensity-modulated radiotherapy for grade II gliomas: A single‑institution case series
  22. Soy isoflavone reduces LPS-induced acute lung injury via increasing aquaporin 1 and aquaporin 5 in rats
  23. Refractory hypokalemia with sexual dysplasia and infertility caused by 17α-hydroxylase deficiency and triple X syndrome: A case report
  24. Meta-analysis of cancer risk among end stage renal disease undergoing maintenance dialysis
  25. 6-Phosphogluconate dehydrogenase inhibition arrests growth and induces apoptosis in gastric cancer via AMPK activation and oxidative stress
  26. Experimental study on the optimization of ANM33 release in foam cells
  27. Primary retroperitoneal angiosarcoma: A case report
  28. Metabolomic analysis-identified 2-hydroxybutyric acid might be a key metabolite of severe preeclampsia
  29. Malignant pleural effusion diagnosis and therapy
  30. Effect of spaceflight on the phenotype and proteome of Escherichia coli
  31. Comparison of immunotherapy combined with stereotactic radiotherapy and targeted therapy for patients with brain metastases: A systemic review and meta-analysis
  32. Activation of hypermethylated P2RY1 mitigates gastric cancer by promoting apoptosis and inhibiting proliferation
  33. Association between the VEGFR-2 -604T/C polymorphism (rs2071559) and type 2 diabetic retinopathy
  34. The role of IL-31 and IL-34 in the diagnosis and treatment of chronic periodontitis
  35. Triple-negative mouse breast cancer initiating cells show high expression of beta1 integrin and increased malignant features
  36. mNGS facilitates the accurate diagnosis and antibiotic treatment of suspicious critical CNS infection in real practice: A retrospective study
  37. The apatinib and pemetrexed combination has antitumor and antiangiogenic effects against NSCLC
  38. Radiotherapy for primary thyroid adenoid cystic carcinoma
  39. Design and functional preliminary investigation of recombinant antigen EgG1Y162–EgG1Y162 against Echinococcus granulosus
  40. Effects of losartan in patients with NAFLD: A meta-analysis of randomized controlled trial
  41. Bibliometric analysis of METTL3: Current perspectives, highlights, and trending topics
  42. Performance comparison of three scaling algorithms in NMR-based metabolomics analysis
  43. PI3K/AKT/mTOR pathway and its related molecules participate in PROK1 silence-induced anti-tumor effects on pancreatic cancer
  44. The altered expression of cytoskeletal and synaptic remodeling proteins during epilepsy
  45. Effects of pegylated recombinant human granulocyte colony-stimulating factor on lymphocytes and white blood cells of patients with malignant tumor
  46. Prostatitis as initial manifestation of Chlamydia psittaci pneumonia diagnosed by metagenome next-generation sequencing: A case report
  47. NUDT21 relieves sevoflurane-induced neurological damage in rats by down-regulating LIMK2
  48. Association of interleukin-10 rs1800896, rs1800872, and interleukin-6 rs1800795 polymorphisms with squamous cell carcinoma risk: A meta-analysis
  49. Exosomal HBV-DNA for diagnosis and treatment monitoring of chronic hepatitis B
  50. Shear stress leads to the dysfunction of endothelial cells through the Cav-1-mediated KLF2/eNOS/ERK signaling pathway under physiological conditions
  51. Interaction between the PI3K/AKT pathway and mitochondrial autophagy in macrophages and the leukocyte count in rats with LPS-induced pulmonary infection
  52. Meta-analysis of the rs231775 locus polymorphism in the CTLA-4 gene and the susceptibility to Graves’ disease in children
  53. Cloning, subcellular localization and expression of phosphate transporter gene HvPT6 of hulless barley
  54. Coptisine mitigates diabetic nephropathy via repressing the NRLP3 inflammasome
  55. Significant elevated CXCL14 and decreased IL-39 levels in patients with tuberculosis
  56. Whole-exome sequencing applications in prenatal diagnosis of fetal bowel dilatation
  57. Gemella morbillorum infective endocarditis: A case report and literature review
  58. An unusual ectopic thymoma clonal evolution analysis: A case report
  59. Severe cumulative skin toxicity during toripalimab combined with vemurafenib following toripalimab alone
  60. Detection of V. vulnificus septic shock with ARDS using mNGS
  61. Novel rare genetic variants of familial and sporadic pulmonary atresia identified by whole-exome sequencing
  62. The influence and mechanistic action of sperm DNA fragmentation index on the outcomes of assisted reproduction technology
  63. Novel compound heterozygous mutations in TELO2 in an infant with You-Hoover-Fong syndrome: A case report and literature review
  64. ctDNA as a prognostic biomarker in resectable CLM: Systematic review and meta-analysis
  65. Diagnosis of primary amoebic meningoencephalitis by metagenomic next-generation sequencing: A case report
  66. Phylogenetic analysis of promoter regions of human Dolichol kinase (DOLK) and orthologous genes using bioinformatics tools
  67. Collagen changes in rabbit conjunctiva after conjunctival crosslinking
  68. Effects of NM23 transfection of human gastric carcinoma cells in mice
  69. Oral nifedipine and phytosterol, intravenous nicardipine, and oral nifedipine only: Three-arm, retrospective, cohort study for management of severe preeclampsia
  70. Case report of hepatic retiform hemangioendothelioma: A rare tumor treated with ultrasound-guided microwave ablation
  71. Curcumin induces apoptosis in human hepatocellular carcinoma cells by decreasing the expression of STAT3/VEGF/HIF-1α signaling
  72. Rare presentation of double-clonal Waldenström macroglobulinemia with pulmonary embolism: A case report
  73. Giant duplication of the transverse colon in an adult: A case report and literature review
  74. Ectopic thyroid tissue in the breast: A case report
  75. SDR16C5 promotes proliferation and migration and inhibits apoptosis in pancreatic cancer
  76. Vaginal metastasis from breast cancer: A case report
  77. Screening of the best time window for MSC transplantation to treat acute myocardial infarction with SDF-1α antibody-loaded targeted ultrasonic microbubbles: An in vivo study in miniswine
  78. Inhibition of TAZ impairs the migration ability of melanoma cells
  79. Molecular complexity analysis of the diagnosis of Gitelman syndrome in China
  80. Effects of maternal calcium and protein intake on the development and bone metabolism of offspring mice
  81. Identification of winter wheat pests and diseases based on improved convolutional neural network
  82. Ultra-multiplex PCR technique to guide treatment of Aspergillus-infected aortic valve prostheses
  83. Virtual high-throughput screening: Potential inhibitors targeting aminopeptidase N (CD13) and PIKfyve for SARS-CoV-2
  84. Immune checkpoint inhibitors in cancer patients with COVID-19
  85. Utility of methylene blue mixed with autologous blood in preoperative localization of pulmonary nodules and masses
  86. Integrated analysis of the microbiome and transcriptome in stomach adenocarcinoma
  87. Berberine suppressed sarcopenia insulin resistance through SIRT1-mediated mitophagy
  88. DUSP2 inhibits the progression of lupus nephritis in mice by regulating the STAT3 pathway
  89. Lung abscess by Fusobacterium nucleatum and Streptococcus spp. co-infection by mNGS: A case series
  90. Genetic alterations of KRAS and TP53 in intrahepatic cholangiocarcinoma associated with poor prognosis
  91. Granulomatous polyangiitis involving the fourth ventricle: Report of a rare case and a literature review
  92. Studying infant mortality: A demographic analysis based on data mining models
  93. Metaplastic breast carcinoma with osseous differentiation: A report of a rare case and literature review
  94. Protein Z modulates the metastasis of lung adenocarcinoma cells
  95. Inhibition of pyroptosis and apoptosis by capsaicin protects against LPS-induced acute kidney injury through TRPV1/UCP2 axis in vitro
  96. TAK-242, a toll-like receptor 4 antagonist, against brain injury by alleviates autophagy and inflammation in rats
  97. Primary mediastinum Ewing’s sarcoma with pleural effusion: A case report and literature review
  98. Association of ADRB2 gene polymorphisms and intestinal microbiota in Chinese Han adolescents
  99. Tanshinone IIA alleviates chondrocyte apoptosis and extracellular matrix degeneration by inhibiting ferroptosis
  100. Study on the cytokines related to SARS-Cov-2 in testicular cells and the interaction network between cells based on scRNA-seq data
  101. Effect of periostin on bone metabolic and autophagy factors during tooth eruption in mice
  102. HP1 induces ferroptosis of renal tubular epithelial cells through NRF2 pathway in diabetic nephropathy
  103. Intravaginal estrogen management in postmenopausal patients with vaginal squamous intraepithelial lesions along with CO2 laser ablation: A retrospective study
  104. Hepatocellular carcinoma cell differentiation trajectory predicts immunotherapy, potential therapeutic drugs, and prognosis of patients
  105. Effects of physical exercise on biomarkers of oxidative stress in healthy subjects: A meta-analysis of randomized controlled trials
  106. Identification of lysosome-related genes in connection with prognosis and immune cell infiltration for drug candidates in head and neck cancer
  107. Development of an instrument-free and low-cost ELISA dot-blot test to detect antibodies against SARS-CoV-2
  108. Research progress on gas signal molecular therapy for Parkinson’s disease
  109. Adiponectin inhibits TGF-β1-induced skin fibroblast proliferation and phenotype transformation via the p38 MAPK signaling pathway
  110. The G protein-coupled receptor-related gene signatures for predicting prognosis and immunotherapy response in bladder urothelial carcinoma
  111. α-Fetoprotein contributes to the malignant biological properties of AFP-producing gastric cancer
  112. CXCL12/CXCR4/CXCR7 axis in placenta tissues of patients with placenta previa
  113. Association between thyroid stimulating hormone levels and papillary thyroid cancer risk: A meta-analysis
  114. Significance of sTREM-1 and sST2 combined diagnosis for sepsis detection and prognosis prediction
  115. Diagnostic value of serum neuroactive substances in the acute exacerbation of chronic obstructive pulmonary disease complicated with depression
  116. Research progress of AMP-activated protein kinase and cardiac aging
  117. TRIM29 knockdown prevented the colon cancer progression through decreasing the ubiquitination levels of KRT5
  118. Cross-talk between gut microbiota and liver steatosis: Complications and therapeutic target
  119. Metastasis from small cell lung cancer to ovary: A case report
  120. The early diagnosis and pathogenic mechanisms of sepsis-related acute kidney injury
  121. The effect of NK cell therapy on sepsis secondary to lung cancer: A case report
  122. Erianin alleviates collagen-induced arthritis in mice by inhibiting Th17 cell differentiation
  123. Loss of ACOX1 in clear cell renal cell carcinoma and its correlation with clinical features
  124. Signalling pathways in the osteogenic differentiation of periodontal ligament stem cells
  125. Crosstalk between lactic acid and immune regulation and its value in the diagnosis and treatment of liver failure
  126. Clinicopathological features and differential diagnosis of gastric pleomorphic giant cell carcinoma
  127. Traumatic brain injury and rTMS-ERPs: Case report and literature review
  128. Extracellular fibrin promotes non-small cell lung cancer progression through integrin β1/PTEN/AKT signaling
  129. Knockdown of DLK4 inhibits non-small cell lung cancer tumor growth by downregulating CKS2
  130. The co-expression pattern of VEGFR-2 with indicators related to proliferation, apoptosis, and differentiation of anagen hair follicles
  131. Inflammation-related signaling pathways in tendinopathy
  132. CD4+ T cell count in HIV/TB co-infection and co-occurrence with HL: Case report and literature review
  133. Clinical analysis of severe Chlamydia psittaci pneumonia: Case series study
  134. Bioinformatics analysis to identify potential biomarkers for the pulmonary artery hypertension associated with the basement membrane
  135. Influence of MTHFR polymorphism, alone or in combination with smoking and alcohol consumption, on cancer susceptibility
  136. Catharanthus roseus (L.) G. Don counteracts the ampicillin resistance in multiple antibiotic-resistant Staphylococcus aureus by downregulation of PBP2a synthesis
  137. Combination of a bronchogenic cyst in the thoracic spinal canal with chronic myelocytic leukemia
  138. Bacterial lipoprotein plays an important role in the macrophage autophagy and apoptosis induced by Salmonella typhimurium and Staphylococcus aureus
  139. TCL1A+ B cells predict prognosis in triple-negative breast cancer through integrative analysis of single-cell and bulk transcriptomic data
  140. Ezrin promotes esophageal squamous cell carcinoma progression via the Hippo signaling pathway
  141. Ferroptosis: A potential target of macrophages in plaque vulnerability
  142. Predicting pediatric Crohn's disease based on six mRNA-constructed risk signature using comprehensive bioinformatic approaches
  143. Applications of genetic code expansion and photosensitive UAAs in studying membrane proteins
  144. HK2 contributes to the proliferation, migration, and invasion of diffuse large B-cell lymphoma cells by enhancing the ERK1/2 signaling pathway
  145. IL-17 in osteoarthritis: A narrative review
  146. Circadian cycle and neuroinflammation
  147. Probiotic management and inflammatory factors as a novel treatment in cirrhosis: A systematic review and meta-analysis
  148. Hemorrhagic meningioma with pulmonary metastasis: Case report and literature review
  149. SPOP regulates the expression profiles and alternative splicing events in human hepatocytes
  150. Knockdown of SETD5 inhibited glycolysis and tumor growth in gastric cancer cells by down-regulating Akt signaling pathway
  151. PTX3 promotes IVIG resistance-induced endothelial injury in Kawasaki disease by regulating the NF-κB pathway
  152. Pancreatic ectopic thyroid tissue: A case report and analysis of literature
  153. The prognostic impact of body mass index on female breast cancer patients in underdeveloped regions of northern China differs by menopause status and tumor molecular subtype
  154. Report on a case of liver-originating malignant melanoma of unknown primary
  155. Case report: Herbal treatment of neutropenic enterocolitis after chemotherapy for breast cancer
  156. The fibroblast growth factor–Klotho axis at molecular level
  157. Characterization of amiodarone action on currents in hERG-T618 gain-of-function mutations
  158. A case report of diagnosis and dynamic monitoring of Listeria monocytogenes meningitis with NGS
  159. Effect of autologous platelet-rich plasma on new bone formation and viability of a Marburg bone graft
  160. Small breast epithelial mucin as a useful prognostic marker for breast cancer patients
  161. Continuous non-adherent culture promotes transdifferentiation of human adipose-derived stem cells into retinal lineage
  162. Nrf3 alleviates oxidative stress and promotes the survival of colon cancer cells by activating AKT/BCL-2 signal pathway
  163. Favorable response to surufatinib in a patient with necrolytic migratory erythema: A case report
  164. Case report of atypical undernutrition of hypoproteinemia type
  165. Down-regulation of COL1A1 inhibits tumor-associated fibroblast activation and mediates matrix remodeling in the tumor microenvironment of breast cancer
  166. Sarcoma protein kinase inhibition alleviates liver fibrosis by promoting hepatic stellate cells ferroptosis
  167. Research progress of serum eosinophil in chronic obstructive pulmonary disease and asthma
  168. Clinicopathological characteristics of co-existing or mixed colorectal cancer and neuroendocrine tumor: Report of five cases
  169. Role of menopausal hormone therapy in the prevention of postmenopausal osteoporosis
  170. Precisional detection of lymph node metastasis using tFCM in colorectal cancer
  171. Advances in diagnosis and treatment of perimenopausal syndrome
  172. A study of forensic genetics: ITO index distribution and kinship judgment between two individuals
  173. Acute lupus pneumonitis resembling miliary tuberculosis: A case-based review
  174. Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm
  175. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs
  176. Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function
  177. Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms
  178. Evaluation of effects of small-incision approach treatment on proximal tibia fracture by deep learning algorithm-based magnetic resonance imaging
  179. An online diagnosis method for cancer lesions based on intelligent imaging analysis
  180. Medical imaging in rheumatoid arthritis: A review on deep learning approach
  181. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach
  182. Utility of neutrophil–lymphocyte ratio and platelet–lymphocyte ratio in predicting acute-on-chronic liver failure survival
  183. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia
  184. TNF-α and IL-8 levels are positively correlated with hypobaric hypoxic pulmonary hypertension and pulmonary vascular remodeling in rats
  185. Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
  186. Comparison of the prognostic value of four different critical illness scores in patients with sepsis-induced coagulopathy
  187. Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development
  188. Hepatobiliary surgery based on intelligent image segmentation technology
  189. Value of brain injury-related indicators based on neural network in the diagnosis of neonatal hypoxic-ischemic encephalopathy
  190. Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology
  191. Early diagnosis for the onset of peri-implantitis based on artificial neural network
  192. Clinical significance of the detection of serum IgG4 and IgG4/IgG ratio in patients with thyroid-associated ophthalmopathy
  193. Forecast of pain degree of lumbar disc herniation based on back propagation neural network
  194. SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
  195. Systematic evaluation of clinical efficacy of CYP1B1 gene polymorphism in EGFR mutant non-small cell lung cancer observed by medical image
  196. Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke
  197. A novel approach for minimising anti-aliasing effects in EEG data acquisition
  198. ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis
  199. Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model
  200. Lung nodule segmentation via semi-residual multi-resolution neural networks
  201. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI
  202. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis
  203. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer’s disease
  204. Effectiveness of the treatment of depression associated with cancer and neuroimaging changes in depression-related brain regions in patients treated with the mediator-deuterium acupuncture method
  205. Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis
  206. Monitoring and evaluation of anesthesia depth status data based on neuroscience
  207. Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches
  208. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
  209. Incidence of different pressure patterns of spinal cerebellar ataxia and analysis of imaging and genetic diagnosis
  210. Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model
  211. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders
  212. From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology
  213. Ecology and Environmental Science
  214. Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem
  215. Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
  216. Effects of cadmium stress on fruits germination and growth of two herbage species
  217. Bamboo charcoal affects soil properties and bacterial community in tea plantations
  218. Optimization of biogas potential using kinetic models, response surface methodology, and instrumental evidence for biodegradation of tannery fleshings during anaerobic digestion
  219. Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China
  220. Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve
  221. Food Science
  222. Effect of berrycactus fruit (Myrtillocactus geometrizans) on glutamate, glutamine, and GABA levels in the frontal cortex of rats fed with a high-fat diet
  223. Guesstimate of thymoquinone diversity in Nigella sativa L. genotypes and elite varieties collected from Indian states using HPTLC technique
  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
  228. Microbiological transglutaminase: Biotechnological application in the food industry
  229. Influence of solvent-free extraction of fish oil from catfish (Clarias magur) heads using a Taguchi orthogonal array design: A qualitative and quantitative approach
  230. Chromatographic analysis of the chemical composition and anticancer activities of Curcuma longa extract cultivated in Palestine
  231. The potential for the use of leghemoglobin and plant ferritin as sources of iron
  232. Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM
  233. Bioengineering and Biotechnology
  234. Biocompatibility and osteointegration capability of β-TCP manufactured by stereolithography 3D printing: In vitro study
  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
  251. Development of a new candidate vaccine against piglet diarrhea caused by Escherichia coli
  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
https://doi.org/10.1515/biol-2022-0680
Schlagwörter für diesen Artikel
MTHFR; cancer; polymorphism; meta-analysis
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Biomedical Sciences
  2. Systemic investigation of inetetamab in combination with small molecules to treat HER2-overexpressing breast and gastric cancers
  3. Immunosuppressive treatment for idiopathic membranous nephropathy: An updated network meta-analysis
  4. Identifying two pathogenic variants in a patient with pigmented paravenous retinochoroidal atrophy
  5. Effects of phytoestrogens combined with cold stress on sperm parameters and testicular proteomics in rats
  6. A case of pulmonary embolism with bad warfarin anticoagulant effects caused by E. coli infection
  7. Neutrophilia with subclinical Cushing’s disease: A case report and literature review
  8. Isoimperatorin alleviates lipopolysaccharide-induced periodontitis by downregulating ERK1/2 and NF-κB pathways
  9. Immunoregulation of synovial macrophages for the treatment of osteoarthritis
  10. Novel CPLANE1 c.8948dupT (p.P2984Tfs*7) variant in a child patient with Joubert syndrome
  11. Antiphospholipid antibodies and the risk of thrombosis in myeloproliferative neoplasms
  12. Immunological responses of septic rats to combination therapy with thymosin α1 and vitamin C
  13. High glucose and high lipid induced mitochondrial dysfunction in JEG-3 cells through oxidative stress
  14. Pharmacological inhibition of the ubiquitin-specific protease 8 effectively suppresses glioblastoma cell growth
  15. Levocarnitine regulates the growth of angiotensin II-induced myocardial fibrosis cells via TIMP-1
  16. Age-related changes in peripheral T-cell subpopulations in elderly individuals: An observational study
  17. Single-cell transcription analysis reveals the tumor origin and heterogeneity of human bilateral renal clear cell carcinoma
  18. Identification of iron metabolism-related genes as diagnostic signatures in sepsis by blood transcriptomic analysis
  19. Long noncoding RNA ACART knockdown decreases 3T3-L1 preadipocyte proliferation and differentiation
  20. Surgery, adjuvant immunotherapy plus chemotherapy and radiotherapy for primary malignant melanoma of the parotid gland (PGMM): A case report
  21. Dosimetry comparison with helical tomotherapy, volumetric modulated arc therapy, and intensity-modulated radiotherapy for grade II gliomas: A single‑institution case series
  22. Soy isoflavone reduces LPS-induced acute lung injury via increasing aquaporin 1 and aquaporin 5 in rats
  23. Refractory hypokalemia with sexual dysplasia and infertility caused by 17α-hydroxylase deficiency and triple X syndrome: A case report
  24. Meta-analysis of cancer risk among end stage renal disease undergoing maintenance dialysis
  25. 6-Phosphogluconate dehydrogenase inhibition arrests growth and induces apoptosis in gastric cancer via AMPK activation and oxidative stress
  26. Experimental study on the optimization of ANM33 release in foam cells
  27. Primary retroperitoneal angiosarcoma: A case report
  28. Metabolomic analysis-identified 2-hydroxybutyric acid might be a key metabolite of severe preeclampsia
  29. Malignant pleural effusion diagnosis and therapy
  30. Effect of spaceflight on the phenotype and proteome of Escherichia coli
  31. Comparison of immunotherapy combined with stereotactic radiotherapy and targeted therapy for patients with brain metastases: A systemic review and meta-analysis
  32. Activation of hypermethylated P2RY1 mitigates gastric cancer by promoting apoptosis and inhibiting proliferation
  33. Association between the VEGFR-2 -604T/C polymorphism (rs2071559) and type 2 diabetic retinopathy
  34. The role of IL-31 and IL-34 in the diagnosis and treatment of chronic periodontitis
  35. Triple-negative mouse breast cancer initiating cells show high expression of beta1 integrin and increased malignant features
  36. mNGS facilitates the accurate diagnosis and antibiotic treatment of suspicious critical CNS infection in real practice: A retrospective study
  37. The apatinib and pemetrexed combination has antitumor and antiangiogenic effects against NSCLC
  38. Radiotherapy for primary thyroid adenoid cystic carcinoma
  39. Design and functional preliminary investigation of recombinant antigen EgG1Y162–EgG1Y162 against Echinococcus granulosus
  40. Effects of losartan in patients with NAFLD: A meta-analysis of randomized controlled trial
  41. Bibliometric analysis of METTL3: Current perspectives, highlights, and trending topics
  42. Performance comparison of three scaling algorithms in NMR-based metabolomics analysis
  43. PI3K/AKT/mTOR pathway and its related molecules participate in PROK1 silence-induced anti-tumor effects on pancreatic cancer
  44. The altered expression of cytoskeletal and synaptic remodeling proteins during epilepsy
  45. Effects of pegylated recombinant human granulocyte colony-stimulating factor on lymphocytes and white blood cells of patients with malignant tumor
  46. Prostatitis as initial manifestation of Chlamydia psittaci pneumonia diagnosed by metagenome next-generation sequencing: A case report
  47. NUDT21 relieves sevoflurane-induced neurological damage in rats by down-regulating LIMK2
  48. Association of interleukin-10 rs1800896, rs1800872, and interleukin-6 rs1800795 polymorphisms with squamous cell carcinoma risk: A meta-analysis
  49. Exosomal HBV-DNA for diagnosis and treatment monitoring of chronic hepatitis B
  50. Shear stress leads to the dysfunction of endothelial cells through the Cav-1-mediated KLF2/eNOS/ERK signaling pathway under physiological conditions
  51. Interaction between the PI3K/AKT pathway and mitochondrial autophagy in macrophages and the leukocyte count in rats with LPS-induced pulmonary infection
  52. Meta-analysis of the rs231775 locus polymorphism in the CTLA-4 gene and the susceptibility to Graves’ disease in children
  53. Cloning, subcellular localization and expression of phosphate transporter gene HvPT6 of hulless barley
  54. Coptisine mitigates diabetic nephropathy via repressing the NRLP3 inflammasome
  55. Significant elevated CXCL14 and decreased IL-39 levels in patients with tuberculosis
  56. Whole-exome sequencing applications in prenatal diagnosis of fetal bowel dilatation
  57. Gemella morbillorum infective endocarditis: A case report and literature review
  58. An unusual ectopic thymoma clonal evolution analysis: A case report
  59. Severe cumulative skin toxicity during toripalimab combined with vemurafenib following toripalimab alone
  60. Detection of V. vulnificus septic shock with ARDS using mNGS
  61. Novel rare genetic variants of familial and sporadic pulmonary atresia identified by whole-exome sequencing
  62. The influence and mechanistic action of sperm DNA fragmentation index on the outcomes of assisted reproduction technology
  63. Novel compound heterozygous mutations in TELO2 in an infant with You-Hoover-Fong syndrome: A case report and literature review
  64. ctDNA as a prognostic biomarker in resectable CLM: Systematic review and meta-analysis
  65. Diagnosis of primary amoebic meningoencephalitis by metagenomic next-generation sequencing: A case report
  66. Phylogenetic analysis of promoter regions of human Dolichol kinase (DOLK) and orthologous genes using bioinformatics tools
  67. Collagen changes in rabbit conjunctiva after conjunctival crosslinking
  68. Effects of NM23 transfection of human gastric carcinoma cells in mice
  69. Oral nifedipine and phytosterol, intravenous nicardipine, and oral nifedipine only: Three-arm, retrospective, cohort study for management of severe preeclampsia
  70. Case report of hepatic retiform hemangioendothelioma: A rare tumor treated with ultrasound-guided microwave ablation
  71. Curcumin induces apoptosis in human hepatocellular carcinoma cells by decreasing the expression of STAT3/VEGF/HIF-1α signaling
  72. Rare presentation of double-clonal Waldenström macroglobulinemia with pulmonary embolism: A case report
  73. Giant duplication of the transverse colon in an adult: A case report and literature review
  74. Ectopic thyroid tissue in the breast: A case report
  75. SDR16C5 promotes proliferation and migration and inhibits apoptosis in pancreatic cancer
  76. Vaginal metastasis from breast cancer: A case report
  77. Screening of the best time window for MSC transplantation to treat acute myocardial infarction with SDF-1α antibody-loaded targeted ultrasonic microbubbles: An in vivo study in miniswine
  78. Inhibition of TAZ impairs the migration ability of melanoma cells
  79. Molecular complexity analysis of the diagnosis of Gitelman syndrome in China
  80. Effects of maternal calcium and protein intake on the development and bone metabolism of offspring mice
  81. Identification of winter wheat pests and diseases based on improved convolutional neural network
  82. Ultra-multiplex PCR technique to guide treatment of Aspergillus-infected aortic valve prostheses
  83. Virtual high-throughput screening: Potential inhibitors targeting aminopeptidase N (CD13) and PIKfyve for SARS-CoV-2
  84. Immune checkpoint inhibitors in cancer patients with COVID-19
  85. Utility of methylene blue mixed with autologous blood in preoperative localization of pulmonary nodules and masses
  86. Integrated analysis of the microbiome and transcriptome in stomach adenocarcinoma
  87. Berberine suppressed sarcopenia insulin resistance through SIRT1-mediated mitophagy
  88. DUSP2 inhibits the progression of lupus nephritis in mice by regulating the STAT3 pathway
  89. Lung abscess by Fusobacterium nucleatum and Streptococcus spp. co-infection by mNGS: A case series
  90. Genetic alterations of KRAS and TP53 in intrahepatic cholangiocarcinoma associated with poor prognosis
  91. Granulomatous polyangiitis involving the fourth ventricle: Report of a rare case and a literature review
  92. Studying infant mortality: A demographic analysis based on data mining models
  93. Metaplastic breast carcinoma with osseous differentiation: A report of a rare case and literature review
  94. Protein Z modulates the metastasis of lung adenocarcinoma cells
  95. Inhibition of pyroptosis and apoptosis by capsaicin protects against LPS-induced acute kidney injury through TRPV1/UCP2 axis in vitro
  96. TAK-242, a toll-like receptor 4 antagonist, against brain injury by alleviates autophagy and inflammation in rats
  97. Primary mediastinum Ewing’s sarcoma with pleural effusion: A case report and literature review
  98. Association of ADRB2 gene polymorphisms and intestinal microbiota in Chinese Han adolescents
  99. Tanshinone IIA alleviates chondrocyte apoptosis and extracellular matrix degeneration by inhibiting ferroptosis
  100. Study on the cytokines related to SARS-Cov-2 in testicular cells and the interaction network between cells based on scRNA-seq data
  101. Effect of periostin on bone metabolic and autophagy factors during tooth eruption in mice
  102. HP1 induces ferroptosis of renal tubular epithelial cells through NRF2 pathway in diabetic nephropathy
  103. Intravaginal estrogen management in postmenopausal patients with vaginal squamous intraepithelial lesions along with CO2 laser ablation: A retrospective study
  104. Hepatocellular carcinoma cell differentiation trajectory predicts immunotherapy, potential therapeutic drugs, and prognosis of patients
  105. Effects of physical exercise on biomarkers of oxidative stress in healthy subjects: A meta-analysis of randomized controlled trials
  106. Identification of lysosome-related genes in connection with prognosis and immune cell infiltration for drug candidates in head and neck cancer
  107. Development of an instrument-free and low-cost ELISA dot-blot test to detect antibodies against SARS-CoV-2
  108. Research progress on gas signal molecular therapy for Parkinson’s disease
  109. Adiponectin inhibits TGF-β1-induced skin fibroblast proliferation and phenotype transformation via the p38 MAPK signaling pathway
  110. The G protein-coupled receptor-related gene signatures for predicting prognosis and immunotherapy response in bladder urothelial carcinoma
  111. α-Fetoprotein contributes to the malignant biological properties of AFP-producing gastric cancer
  112. CXCL12/CXCR4/CXCR7 axis in placenta tissues of patients with placenta previa
  113. Association between thyroid stimulating hormone levels and papillary thyroid cancer risk: A meta-analysis
  114. Significance of sTREM-1 and sST2 combined diagnosis for sepsis detection and prognosis prediction
  115. Diagnostic value of serum neuroactive substances in the acute exacerbation of chronic obstructive pulmonary disease complicated with depression
  116. Research progress of AMP-activated protein kinase and cardiac aging
  117. TRIM29 knockdown prevented the colon cancer progression through decreasing the ubiquitination levels of KRT5
  118. Cross-talk between gut microbiota and liver steatosis: Complications and therapeutic target
  119. Metastasis from small cell lung cancer to ovary: A case report
  120. The early diagnosis and pathogenic mechanisms of sepsis-related acute kidney injury
  121. The effect of NK cell therapy on sepsis secondary to lung cancer: A case report
  122. Erianin alleviates collagen-induced arthritis in mice by inhibiting Th17 cell differentiation
  123. Loss of ACOX1 in clear cell renal cell carcinoma and its correlation with clinical features
  124. Signalling pathways in the osteogenic differentiation of periodontal ligament stem cells
  125. Crosstalk between lactic acid and immune regulation and its value in the diagnosis and treatment of liver failure
  126. Clinicopathological features and differential diagnosis of gastric pleomorphic giant cell carcinoma
  127. Traumatic brain injury and rTMS-ERPs: Case report and literature review
  128. Extracellular fibrin promotes non-small cell lung cancer progression through integrin β1/PTEN/AKT signaling
  129. Knockdown of DLK4 inhibits non-small cell lung cancer tumor growth by downregulating CKS2
  130. The co-expression pattern of VEGFR-2 with indicators related to proliferation, apoptosis, and differentiation of anagen hair follicles
  131. Inflammation-related signaling pathways in tendinopathy
  132. CD4+ T cell count in HIV/TB co-infection and co-occurrence with HL: Case report and literature review
  133. Clinical analysis of severe Chlamydia psittaci pneumonia: Case series study
  134. Bioinformatics analysis to identify potential biomarkers for the pulmonary artery hypertension associated with the basement membrane
  135. Influence of MTHFR polymorphism, alone or in combination with smoking and alcohol consumption, on cancer susceptibility
  136. Catharanthus roseus (L.) G. Don counteracts the ampicillin resistance in multiple antibiotic-resistant Staphylococcus aureus by downregulation of PBP2a synthesis
  137. Combination of a bronchogenic cyst in the thoracic spinal canal with chronic myelocytic leukemia
  138. Bacterial lipoprotein plays an important role in the macrophage autophagy and apoptosis induced by Salmonella typhimurium and Staphylococcus aureus
  139. TCL1A+ B cells predict prognosis in triple-negative breast cancer through integrative analysis of single-cell and bulk transcriptomic data
  140. Ezrin promotes esophageal squamous cell carcinoma progression via the Hippo signaling pathway
  141. Ferroptosis: A potential target of macrophages in plaque vulnerability
  142. Predicting pediatric Crohn's disease based on six mRNA-constructed risk signature using comprehensive bioinformatic approaches
  143. Applications of genetic code expansion and photosensitive UAAs in studying membrane proteins
  144. HK2 contributes to the proliferation, migration, and invasion of diffuse large B-cell lymphoma cells by enhancing the ERK1/2 signaling pathway
  145. IL-17 in osteoarthritis: A narrative review
  146. Circadian cycle and neuroinflammation
  147. Probiotic management and inflammatory factors as a novel treatment in cirrhosis: A systematic review and meta-analysis
  148. Hemorrhagic meningioma with pulmonary metastasis: Case report and literature review
  149. SPOP regulates the expression profiles and alternative splicing events in human hepatocytes
  150. Knockdown of SETD5 inhibited glycolysis and tumor growth in gastric cancer cells by down-regulating Akt signaling pathway
  151. PTX3 promotes IVIG resistance-induced endothelial injury in Kawasaki disease by regulating the NF-κB pathway
  152. Pancreatic ectopic thyroid tissue: A case report and analysis of literature
  153. The prognostic impact of body mass index on female breast cancer patients in underdeveloped regions of northern China differs by menopause status and tumor molecular subtype
  154. Report on a case of liver-originating malignant melanoma of unknown primary
  155. Case report: Herbal treatment of neutropenic enterocolitis after chemotherapy for breast cancer
  156. The fibroblast growth factor–Klotho axis at molecular level
  157. Characterization of amiodarone action on currents in hERG-T618 gain-of-function mutations
  158. A case report of diagnosis and dynamic monitoring of Listeria monocytogenes meningitis with NGS
  159. Effect of autologous platelet-rich plasma on new bone formation and viability of a Marburg bone graft
  160. Small breast epithelial mucin as a useful prognostic marker for breast cancer patients
  161. Continuous non-adherent culture promotes transdifferentiation of human adipose-derived stem cells into retinal lineage
  162. Nrf3 alleviates oxidative stress and promotes the survival of colon cancer cells by activating AKT/BCL-2 signal pathway
  163. Favorable response to surufatinib in a patient with necrolytic migratory erythema: A case report
  164. Case report of atypical undernutrition of hypoproteinemia type
  165. Down-regulation of COL1A1 inhibits tumor-associated fibroblast activation and mediates matrix remodeling in the tumor microenvironment of breast cancer
  166. Sarcoma protein kinase inhibition alleviates liver fibrosis by promoting hepatic stellate cells ferroptosis
  167. Research progress of serum eosinophil in chronic obstructive pulmonary disease and asthma
  168. Clinicopathological characteristics of co-existing or mixed colorectal cancer and neuroendocrine tumor: Report of five cases
  169. Role of menopausal hormone therapy in the prevention of postmenopausal osteoporosis
  170. Precisional detection of lymph node metastasis using tFCM in colorectal cancer
  171. Advances in diagnosis and treatment of perimenopausal syndrome
  172. A study of forensic genetics: ITO index distribution and kinship judgment between two individuals
  173. Acute lupus pneumonitis resembling miliary tuberculosis: A case-based review
  174. Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm
  175. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs
  176. Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function
  177. Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms
  178. Evaluation of effects of small-incision approach treatment on proximal tibia fracture by deep learning algorithm-based magnetic resonance imaging
  179. An online diagnosis method for cancer lesions based on intelligent imaging analysis
  180. Medical imaging in rheumatoid arthritis: A review on deep learning approach
  181. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach
  182. Utility of neutrophil–lymphocyte ratio and platelet–lymphocyte ratio in predicting acute-on-chronic liver failure survival
  183. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia
  184. TNF-α and IL-8 levels are positively correlated with hypobaric hypoxic pulmonary hypertension and pulmonary vascular remodeling in rats
  185. Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
  186. Comparison of the prognostic value of four different critical illness scores in patients with sepsis-induced coagulopathy
  187. Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development
  188. Hepatobiliary surgery based on intelligent image segmentation technology
  189. Value of brain injury-related indicators based on neural network in the diagnosis of neonatal hypoxic-ischemic encephalopathy
  190. Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology
  191. Early diagnosis for the onset of peri-implantitis based on artificial neural network
  192. Clinical significance of the detection of serum IgG4 and IgG4/IgG ratio in patients with thyroid-associated ophthalmopathy
  193. Forecast of pain degree of lumbar disc herniation based on back propagation neural network
  194. SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
  195. Systematic evaluation of clinical efficacy of CYP1B1 gene polymorphism in EGFR mutant non-small cell lung cancer observed by medical image
  196. Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke
  197. A novel approach for minimising anti-aliasing effects in EEG data acquisition
  198. ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis
  199. Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model
  200. Lung nodule segmentation via semi-residual multi-resolution neural networks
  201. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI
  202. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis
  203. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer’s disease
  204. Effectiveness of the treatment of depression associated with cancer and neuroimaging changes in depression-related brain regions in patients treated with the mediator-deuterium acupuncture method
  205. Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis
  206. Monitoring and evaluation of anesthesia depth status data based on neuroscience
  207. Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches
  208. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
  209. Incidence of different pressure patterns of spinal cerebellar ataxia and analysis of imaging and genetic diagnosis
  210. Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model
  211. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders
  212. From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology
  213. Ecology and Environmental Science
  214. Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem
  215. Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
  216. Effects of cadmium stress on fruits germination and growth of two herbage species
  217. Bamboo charcoal affects soil properties and bacterial community in tea plantations
  218. Optimization of biogas potential using kinetic models, response surface methodology, and instrumental evidence for biodegradation of tannery fleshings during anaerobic digestion
  219. Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China
  220. Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve
  221. Food Science
  222. Effect of berrycactus fruit (Myrtillocactus geometrizans) on glutamate, glutamine, and GABA levels in the frontal cortex of rats fed with a high-fat diet
  223. Guesstimate of thymoquinone diversity in Nigella sativa L. genotypes and elite varieties collected from Indian states using HPTLC technique
  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
  228. Microbiological transglutaminase: Biotechnological application in the food industry
  229. Influence of solvent-free extraction of fish oil from catfish (Clarias magur) heads using a Taguchi orthogonal array design: A qualitative and quantitative approach
  230. Chromatographic analysis of the chemical composition and anticancer activities of Curcuma longa extract cultivated in Palestine
  231. The potential for the use of leghemoglobin and plant ferritin as sources of iron
  232. Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM
  233. Bioengineering and Biotechnology
  234. Biocompatibility and osteointegration capability of β-TCP manufactured by stereolithography 3D printing: In vitro study
  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
  251. Development of a new candidate vaccine against piglet diarrhea caused by Escherichia coli
  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
Jetzt anmelden und 20% sparen
Institutioneller Zugang
Wie funktioniert Authentifizierung?
Haben Sie eine Idee, wie wir unsere Website verbessern können?
Bitte schreiben Sie uns.
© 2025 De Gruyter Brill
Heruntergeladen am 6.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/biol-2022-0680/html?lang=de
Button zum nach oben scrollen