Home Association of interleukin-10 rs1800896, rs1800872, and interleukin-6 rs1800795 polymorphisms with squamous cell carcinoma risk: A meta-analysis
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Association of interleukin-10 rs1800896, rs1800872, and interleukin-6 rs1800795 polymorphisms with squamous cell carcinoma risk: A meta-analysis

  • Zhenxia Wei , Xiaoping Su , Qiurui Hu , Yonghui Huang , Cuiping Li EMAIL logo and Xuanping Huang EMAIL logo
Published/Copyright: April 15, 2023

Abstract

The relationship between interleukin (IL)-10 and IL-6 gene polymorphisms and squamous cell carcinoma (SCC) has been demonstrated but with inconsistent conclusions. The aim of this study was to evaluate the potential associations of IL gene polymorphisms and the SCC risk. PubMed, Cochrane Library, Web of Science, China National Knowledge Infrastructure, China Biomedical Database, WanFang, and China Science and Technology Journal Database databases were searched for articles reporting the correlations of IL-10 and IL-6 gene polymorphisms with the SCC risk. Odds ratio and 95% confidence interval were calculated using Stata Version 11.2. Meta-regression, sensitivity, and publication bias were analyzed. False-positive reporting probability and Bayesian measure of the false-discovery probability were used to explore the credibility of the calculation. Twenty-three articles were included. The IL-10 rs1800872 polymorphism showed a significant correlation with the SCC risk in the overall analysis. Studies pooled by ethnicity revealed that the IL-10 rs1800872 polymorphism reduced the SCC risk in the Caucasian population. The results of this study suggest that the IL-10 rs1800872 polymorphism may confer a genetic susceptibility to SCC, particularly oral SCC, in Caucasians. However, the IL-10 rs1800896 or IL-6 rs1800795 polymorphism was not significantly associated with the SCC risk.

1 Introduction

Cancer is a leading cause of death worldwide. According to the 2019 World Health Organization estimates, cancer was the leading cause of death in 112 of 183 countries and may surpass cardiovascular disease as the leading cause of death in many countries [1]. Squamous cell carcinoma (SCC) is a malignant tumor that arises in tissues and organs covered by the squamous epithelium, including the skin, oral cavity, esophagus, cervix, vagina, bronchus, urinary bladder, and renal pelvis [24]. Currently, combination therapy is the most common treatment modality for SCC [5,6]. Regardless of the treatment modality, patients develop severe scarring, increasing their financial burden and mental stress [79]. Extensive epidemiological and molecular biology studies have shown that chronic inflammation, unhealthy lifestyle, viral infections, and many other risk factors increase the SCC risk [1012]; however, the specific role of these factors in tumor development has not been elucidated.

Processes such as inflammatory cell infiltration and malignant cell metastasis are common in cancer. Cytokines might play a critical role in these processes. Interleukin (IL) is an immunomodulatory cytokine involved in cell proliferation and apoptosis that can promote tumor immune escape and accelerate the progression of malignant tumors by inhibiting the anti-tumor immune response in the tumor microenvironment [13]. T-helper cytokines are utilized by IL-10 to regulate the growth and differentiation of natural immune cells, keratinocytes, and endothelial cells and inhibit the activation and effector functions of T cells [14]. IL-6 can participate in the differentiation regulation of B cells and promote the release of antibodies by B cells. It can be produced and secreted by tumor cells, involved in the proliferation and differentiation of malignant tumor cells, and expressed at high levels in serum and tumor tissues of most cancers [15]. IL-10 and IL-6 genes are located on chromosomes 10 and 7, respectively, and polymorphic in the region where the gene begins transcription. In recent years, the relationship between IL-10 and IL-6 gene polymorphisms and cancer have attracted great attention. Gene polymorphisms are closely related to changes in the IL expression level, leading to the occurrence of many cancers. Gene polymorphisms in the IL-10 and IL-6 promoter regions might affect the expression of gene-encoded proteins associated with the risk and prognosis of SCC [16,17]. The correlations of IL-10 rs1800896(-1082) and rs1800872(-592) and IL-6 rs1800795(-174) promoter-region polymorphisms with the SCC risk have been extensively studied. The polymorphisms are located near the transcription factor binding site and related to the pathogenesis of SCC, including cervical SCC [18]. However, the study results have been inconclusive and inconsistent [19,20].

The advantage of meta-analyses is reduction in random errors through quantitative and comprehensive analyses of all eligible research data. To date, no studies have focused on the correlations of IL-10 or IL-6 gene polymorphisms with the SCC risk. Therefore, the aim of this meta-analysis was to clarify the correlations of the IL-10 rs1800896 and rs1800872 and IL-6 rs1800795 gene polymorphisms with the SCC risk, including subgroup analyses by ethnicity, control source, and cancer type. The risk assessment was expected to be more detailed and accurate compared to previous studies. At the same time to provide ideas for cancer prevention and clinical treatment.

2 Materials and methods

2.1 Literature search

We extracted articles reporting the correlations of IL-10 rs1800896 and rs1800872 and IL-6 rs1800795 gene polymorphisms with the SCC risk from PubMed, Cochrane Library, Web of Science, China National Knowledge Infrastructure, China Biomedical Database, WanFang, and China Science and Technology Journal Database databases. The search terms were (“Interleukin-10” OR “IL-10” OR “IL10”) (“Interleukin-6” OR “IL-6” OR “IL6”) AND (“squamous cell carcinoma” OR “squamous cancer” OR “squamous cell tumor”) AND (“polymorphism” OR “genetic polymorphism” OR “polymorphisms”). Moreover, references of relevant studies, including meta-analyses, were searched manually to screen more studies for inclusion. We limited our search to human studies and did not specify any minimum number of cases or controls required or the year of publication. Articles published in English or Chinese were likely to be included.

2.2 Inclusion and exclusion criteria

Inclusion criteria were (1) case–control study reporting the associations of rs1800896, rs1800872, and rs1800795 polymorphisms in the promoter regions of IL-10 and IL-6 genes with the SCC risk, (2) information available regarding the distribution of cases and controls, allowing calculation of the odds ratio (OR) with 95% confidence interval (CI), and (3) full text available without duplication. Exclusion criteria were (1) original study design other than case–control or study without genotype data, (2) cancer not specified as SCC, (3) case report, (4) non-human study, (5) review (including meta-analysis), and (6) duplicate or overlapping data.

2.3 Study design and extracted information

We extracted the following information: surname of the first author, date of publication, country, participants’ ethnicity (Asian, Caucasian, or mixed descent), control source (population-, or hospital-based), cancer type (e.g., oral SCC), sample sizes of case and control groups, and detailed data on the genetics and genotyping of case and control studies. Two investigators (Z.W. and X.S.) independently extracted information based on the constituted standards, and a third investigator (Q.H.) reviewed the information. Disagreements were resolved through a discussion among the three investigators, ensuring more accurate data extraction. Investigators selected studies by reviewing the abstracts and full text based on the aforementioned eligibility criteria. We manually searched the references in selected studies, including meta-analyses, to screen more studies for inclusion. Among similar studies, those with the largest sample size or most recent publication were selected.

2.4 Statistical analysis

The correlations of IL-10 rs1800896 and rs1800872 and IL-6 rs1800795 gene polymorphisms with the SCC risk were estimated using 95% CI and OR. The significance of OR was determined with the z-test. A p-value <0.05 was set to indicate statistical significance. The combined OR was evaluated using four genetic models, including homozygote comparison (GG/AA; CC/AA; CC/GG), heterozygote comparison (AG/AA; AC/AA; GC/GG), dominant (AG + GG/AA; AC + CC/AA; GC + CC/GG), and recessive (GG/AA + AG; CC/AA + AC; CC/GG + GC) models in IL-10 rs1800896 and rs1800872 and IL-6 rs1800795. Subgroup analyses were conducted by cancer type, ethnicity, and control source. I 2 statistics were used to evaluate the heterogeneity among studies. Studies were homogenous at I 2 < 50%, and the fixed-effects model was used to combine the OR and 95% CI; otherwise, the random-effects model was used.

The occurrence of publication bias was determined with Egger’s and Begg’s tests. Stata version 11.2 (Stata Corporation, College Station, TX) was used, with the significance level set as a bilateral α of 0.05. A p-value <0.05 was considered to indicate publication bias.

Three predefined sources of heterogeneity were detected using meta-regression analyses: publication year, ethnicity, and control source. The rationality of the meta-analysis results was checked with sensitivity analyses. Excluding one study each time and combining the remaining studies revealed no substantial changes in the corresponding combined OR; thus, our results were considered to be statistically robust. In addition, the false-positive reporting probability (FPRP) was evaluated. We confirmed a FPRP <0.2 and appointed prior probabilities of 0.25, 0.1, 0.01, 0.001, and 0.0001 to examine an OR of 1.5 associated with the SCC risk. The results were significant at FPRP <0.2 [21]. The Bayesian measure of false-discovery probability (BFDP) was estimated using Excel computed tables to evaluate the reliability of the statistically significant associations [22]. The results were significant at BFDP <0.8.

3 Results

3.1 Study characteristics

After applying the eligibility criteria, 222 articles were retrieved. After reading the titles and abstracts, 154 articles were excluded for reasons such as irrelevant study topic, meta-analysis or review design, and Greek language. After reviewing the full text of the remaining 67 articles, 44 articles without genotypic data of patients with SCC were excluded. Finally, 23 articles met all requirements for inclusion in this study. Figure 1 shows a flowchart of the study selection process.

Figure 1 
                  Whole flow diagram of the study selection process.
Figure 1

Whole flow diagram of the study selection process.

Fifteen studies, involving 3,311 cases and 4,756 controls, investigated the association of the IL-10 rs1800896 polymorphism with the SCC risk [16,19,20,2334]. Eleven studies, involving 3,069 cases and 4,265 controls, investigated the association of the IL-10 rs1800872 polymorphism with the SCC risk [16,20,24,26,27,29,30,3437]. Eight studies, involving 1,315 cases and 1,905 controls, investigated the association of the IL-6 rs1800795 polymorphism with the SCC risk [20,33,3843]. Nine and six of the 15 included studies on IL-10 rs1800896 involved the Caucasian and Asian ethnicities, respectively. Control sources were hospital-based in six studies and population-based in nine studies. A total of six cancer types were reported. Oral and cervical SCC were reported in five and three articles, respectively. Lung, head and neck, laryngeal, and esophageal SCC were reported in one article each. Table 1 shows the characteristics of the included studies on IL-10 rs1800896.

Table 1

Characteristics of included case–control studies on IL-10 rs1800896 polymorphism and squamous cell carcinoma

No. Author Year Ethnicity Country Cancer type Source of control Sample size of case Sample size of control Genotype distribution MAF Genotyping method
Case Control
AA AG GG AA AG GG
1 Pasvenskaite et al. 2021 Caucsian Lithuania Laryngeal SCC HB 300 533 70 163 67 148 269 116 0.48 PCR
2 Mao et al. 2021 Asian China Oral SCC HB 125 110 109 16 0 98 12 0 0.06 PCR
3 Chen et al. 2020 Asian China Esophageal SCC HB 721 1,208 625 84 4 1,061 136 4 0.06 PCR
4 Goud et al. 2019 Asian Malaysia Oral SCC PB 41 48 37 4 0 39 9 0 0.07 PCR-RFLP
5 Sharma et al. 2018 Caucsian India Oral SCC PB 100 150 51 36 13 100 50 0 0.22 PCR
6 Hussain et al. 2016 Caucsian India Oral SCC HB 232 221 69 158 5 127 93 1 0.29 PCR-RFLP
7 Torres-Poveda et al. 2016 Caucsian Mexico Cervical SCC PB 200 200 121 70 9 110 78 12 0.24 PCR
8 Zhou et al. 2014 Asian China Laryngeal SCC PB 146 119 115 26 5 107 11 1 0.09 PCR-RFLP
9 Torres-Poveda et al. 2012 Caucsian Mexico Cervical SCC HB 204 166 125 66 13 92 62 12 0.24 PCR
10 Jeong et al. 2010 Asian Korea Head and neck SCC HB 290 358 238 38 2 304 45 1 0.07 PCR
11 Vairaktaris et al. 2008 Caucsian Greece and Germany Oral SCC PB 144 141 46 96 2 81 60 0 0.28 PCR
12 Guo et al. 2005 Asian China Esophageal SCC PB 203 443 117 81 5 267 164 12 0.22 PCR-RFLP
13 Zoodsma et al. 2005 Caucsian Holland Cervical SCC PB 512 606 121 242 149 130 307 169 0.47 PCR
14 Seifart et al. 2005 Caucsian Germany Lung SCC PB 40 243 13 17 10 86 115 42 0.42 PCR
15 El-Omar et al. 2003 Caucsian United States Esophageal SCC PB 53 210 16 28 9 59 103 48 0.47 PCR-TaqMan

Abbreviations: PB, population-based; HB, hospital-based; SCC, squamous cell carcinoma; MAF, minor allele frequency.

Seven and four of the 11 included studies on IL-10 rs1800872 involved the Caucasian and Asian populations, respectively. Control sources were population-based in six studies and hospital-based in five studies. A total of four cancer types were reported. Esophageal, cervical, oral, and lung SCC were reported in four, three, two, and two articles, respectively. Table 2 shows the characteristics of the included studies on IL-10 rs1800872.

Table 2

Characteristics of included case–control studies on IL-10 rs1800872 polymorphism and squamous cell carcinoma

No. Author Year Ethnicity Country Cancer type Source of control Sample size of case Sample size of control Genotype distribution MAF Genotyping method
Case Control
AA AC CC AA AC CC
1 Pasvenskaite et al. 2021 Caucsian Lithuania Laryngeal SCC HB 300 533 21 102 177 33 179 321 0.23 PCR
2 Chen et al. 2020 Asian China Esophageal SCC HB 721 1,208 349 301 65 550 523 128 0.31 PCR
3 Sharma et al. 2018 Caucsian India Oral SCC PB 100 150 25 54 21 18 88 44 0.47 PCR
4 Torres-Poveda et al. 2016 Caucsian Mexico Cervical SCC HB 200 200 58 98 44 30 85 85 0.47 PCR
5 Singh et al. 2017 Caucsian India Oral SCC PB 250 250 39 168 43 14 173 63 0.45 PCR-RFLP
6 Zhou et al. 2014 Asian China Laryngeal SCC PB 146 119 63 70 13 64 39 16 0.32 PCR-RFLP
7 Sun et al. 2013 Asian China Esophageal SCC HB 380 380 162 163 31 191 141 33 0.28 PCR
8 Torres-Poveda et al. 2012 Caucsian Mexico Cervical SCC HB 204 166 49 105 50 30 70 66 0.45 PCR
9 Wang et al. 2006 Asian China Esophageal SCC PB 203 443 95 88 20 182 196 65 0.35 PCR-RFLP
10 Zoodsma et al. 2005 Caucsian Holland Cervical SCC PB 512 606 25 172 300 26 175 405 0.20 PCR
11 El-Omar et al. 2003 Caucsian United States Esophageal SCC PB 53 210 3 15 35 13 70 127 0.22 PCR-TaqMan

Abbreviations: PB, population-based; HB, hospital-based; SCC, squamous cell carcinoma; MAF, minor allele frequency.

Six and two of the nine included studies on IL-6 rs1800795 involved the Caucasian and Asian populations, respectively. Control sources were population-based in six studies and hospital-based in two studies. A total of five cancer types were reported. Oral and laryngeal cancer were reported in three and two articles, respectively. Lung, cervical, and esophageal SCC were reported in article each. Table 3 shows the characteristics of the included studies on IL-6 rs1800795.

Table 3

Characteristics of included case–control studies on IL-10 rs1800795 polymorphism and squamous cell carcinoma

No. Author Year Ethnicity Country Cancer type Source of control Sample size of case Sample size of control Genotype distribution MAF Genotyping method
Case Control
GG GC CC GG GC CC
1 Pasvenskaite et al. 2020 Caucsian Lithuania Laryngeal SCC HB 352 538 69 182 102 132 261 145 0.47 PCR
2 Candan Demiröz Abakay 2020 Caucsian Turkey Laryngeal SCC PB 80 50 38 31 11 29 15 5 0.30 PCR
3 Fernández-Mateos et al. 2019 Caucsian Spanish Oral SCC PB 70 70 12 33 25 8 23 39 0.34 PCR
4 Shi et al. 2014 Asian China Cervical SCC HB 418 518 131 201 86 181 259 78 0.42 PCR-RFLP
5 Gaur et al. 2011 Asian India Oral SCC PB 140 120 98 35 7 65 41 14 0.23 PCR-RFLP
6 Vairaktaris et al. 2008 Caucsian Greece and Germany Oral SCC PB 162 156 42 102 18 90 60 6 0.33 PCR
7 Seifart et al. 2005 Caucsian Germany Lung SCC PB 40 243 17 19 4 90 107 46 0.40 PCR
8 El-Omar et al. 2003 Caucsian United States Esophageal SCC PB 53 210 13 6 5 83 98 28 0.32 PCR-TaqMan

Abbreviations: PB, population-based; HB, hospital-based; SCC, squamous cell carcinoma; MAF, minor allele frequency.

3.2 Quantitative synthesis

Table 4 shows the relationship between the IL-10 rs1800896 gene polymorphism and the SCC risk. Data of 15 studies combined and analyzed according to four models revealed no significant association between the IL-10 rs1800896 gene polymorphism and the SCC risk (GG/AA: OR = 1.18, 95% CI: 0.96–1.45, p = 0.109; AG/AA: OR = 1.27, 95% CI: 0.99–1.61, p = 0.057; AG + GG/AA: OR = 1.27, 95% CI: 0.99–1.61, p = 0.057; GG/AA + AG: OR = 1.14, 95% CI: 0.96–1.35, p = 0.149; Figure 2). Subgroup analyses by ethnicity and control source revealed no significant correlation between the IL-10 rs800896 gene polymorphism and the SCC risk in any model. The subgroup analysis by cancer type revealed that the IL-10 rs1800896 gene polymorphism significantly increased the oral SCC risk in all four models (GG/AA: OR = 19.09, 95% CI: 4.48–81.45, p = 0.000; AG/AA: OR = 1.76, 95% CI: 1.05–2.95, p = 0.045; AG + GG/AA: OR = 1.94, 95% CI: 1.21–3.13, p = 0.006; GG/AA + AG: OR = 12.70, 95% CI: 3.09–52.16, p = 0.000). In addition, the IL-10 rs1800896 gene polymorphism increased the risk of laryngeal SCC (AG/AA: OR = 1.41, 95% CI: 1.04–1.93, p = 0.030), esophageal SCC (AG + GG/AA: OR = 0.48, 95% CI: 0.32–0.72, p = 0.000). Two gene models (AG/AA: I 2 = 74.10%, p = 0.000; AG + GG/AA: I 2 = 76.80%, p = 0.000). No positive results were found other than those aforementioned.

Table 4

Stratified analyses of the IL-10 rs1800896 polymorphism on squamous cell carcinoma risk

Comparative model No. Z p OR (95% CI) Heterogeneity Z Begg's test t Egger's test FPRP p-value FPRP statistical power FPRP prior probability BEDP prior probability
Heterogeneity chi-squared p I 2 0.25 0.10 0.01 0.001 0.0001 0.010 0.001 0.000
GG/AA
Overall 15 1.60 0.109 1.182(0.963–1.451) 20.09 0.065 40.30% 2.99 0.003 2.78 0.018 0.110 0.989 0.250 0.500 0.917 0.991 0.999 0.994 0.999 1.000
Ethnicity
Caucasian 9 0.72 0.470 1.166(0.769–1.769) 17.35 0.027 53.90% 1.56 0.118 2.04 0.080 0.470 0.882 0.615 0.828 0.981 0.998 1.000 0.996 1.000 1.000
Asian 6 1.25 0.212 1.571(0.773–3.191) 1.99 0.575 0.00% 1.02 0.308 3.09 0.091 0.212 0.449 0.586 0.809 0.979 0.998 1.000 0.991 0.999 1.000
Source of control
HB 6 1.36 0.175 1.269(0.899–1.792) 4.94 0.293 19.10% 1.22 0.221 1.33 0.276 0.176 0.829 0.389 0.657 0.955 0.995 1.000 0.993 0.999 1.000
PB 9 0.71 0.477 1.214(0.711–2.073) 14.52 0.043 51.80% 1.86 0.063 2.21 0.069 0.477 0.781 0.647 0.846 0.984 0.998 1.000 0.995 0.999 1.000
Cancer types
Laryngeal SCC 2 1.29 0.195 1.305(0.872–1.951) 1.43 0.233 29.80% 0.00 1.000 . . 0.194 0.751 0.437 0.700 0.962 0.996 1.000 0.993 0.999 1.000
Oral SCC 5 3.98 0.000 19.093(4.475–81.451) 1.17 0.556 0.00% 0.00 1.000 0.44 0.734 0.000 0.000 0.408 0.674 0.958 0.996 1.000 0.873 0.986 1.000
Cervical SCC 3 0.74 0.459 0.896(0.670–1.199) 0.54 0.765 0.00% 1.04 0.296 −3.59 0.173 0.460 0.977 0.586 0.809 0.979 0.998 1.000 0.997 1.000 1.000
Esophageal SCC 3 0.29 0.771 0.913(0.495–1.686) 1.14 0.566 0.00% 1.04 0.296 44.50 0.014 0.771 0.842 0.733 0.889 0.989 0.999 1.000 0.995 0.999 1.000
Genotyping method
PCR 10 1.42 0.157 1.171(0.941–1.458) 13.47 0.097 40.60% 2.19 0.029 2.240 0.06 0.158 0.987 0.325 0.591 0.941 0.994 0.999 0.995 0.999 1.000
PCR-RFLP 4 1.29 0.199 2.670(0.597–11.936) 4.38 0.112 54.30% 1.04 0.296 4.690 0.134 0.199 0.225 0.726 0.888 0.989 0.999 1.000 0.989 0.999 1.000
AG/AA
Overall 15 1.68 0.094 1.226(0.990–1.556) 54.14 0.000 74.10% 0.40 0.692 0.27 0.792 0.094 0.951 0.228 0.470 0.907 0.990 0.999 0.992 0.999 1.000
Ethnicity
Caucasian 9 1.31 0.190 1.277(0.886–1.840) 48.04 0.000 83.30% 0.31 0.754 0.28 0.790 0.189 0.806 0.414 0.679 0.959 0.996 1.000 0.993 0.999 1.000
Asian 6 1.17 0.243 1.116(0.928–1.342) 5.18 0.395 3.40% 0.00 1.000 0.11 0.921 0.243 0.999 0.422 0.687 0.960 0.996 1.000 0.997 1.000 1.000
Source of control
HB 6 1.23 0.220 1.282(0.862–1.905) 27.41 0.000 81.80% 0.00 1.000 0.01 0.991 0.219 0.781 0.457 0.716 0.965 0.996 1.000 0.993 0.999 1.000
PB 9 1.06 0.291 1.183(0.866–1.616) 25.35 0.001 68.40% 0.10 0.917 0.49 0.638 0.291 0.932 0.484 0.737 0.969 0.997 1.000 0.995 1.000 1.000
Cancer types
Laryngeal SCC 2 2.17 0.030 1.414(1.035–1.931) 1.64 0.201 39.00% 0.00 1.000 . . 0.029 0.645 0.120 0.291 0.818 0.978 0.998 0.976 0.998 1.000
Oral SCC 5 2.12 0.034 1.757(1.045–2.954) 14.70 0.005 72.80% 1.71 0.086 −3.83 0.031 0.033 0.275 0.267 0.523 0.923 0.992 0.999 0.973 0.997 1.000
Cervical SCC 3 1.79 0.073 0.823(0.666–1.018) 0.09 0.958 0.00% 1.04 0.296 −2.96 0.207 0.073 0.974 0.183 0.401 0.881 0.987 0.999 0.991 0.999 1.000
Esophageal SCC 3 0.65 0.513 1.073(0.869–1.325) 0.14 0.932 0.00% 0.00 1.000 −0.36 0.782 0.513 0.999 0.606 0.822 0.981 0.988 1.000 0.998 1.000 1.000
Genotyping method
PCR 10 0.98 0.325 1.119(0.894–1.400) 23.43 0.005 61.60% 0.89 0.371 0.79 0.455 0.325 0.995 0.495 0.746 0.970 0.997 1.000 0.997 1.000 1.000
PCR-RFLP 4 1.22 0.222 1.555(0.766–3.158) 19.37 0.000 84.50% −0.34 1.000 −0.3 0.791 0.221 0.460 0.591 0.813 0.979 0.998 1.000 0.991 0.999 1.000
AG+GG/AA
Overall 15 1.91 0.057 1.266(0.993–1.614) 60.24 0.000 76.80% 0.99 0.322 0.51 0.619 0.057 0.914 0.157 0.359 0.860 0.984 0.998 0.988 0.999 1.000
Ethnicity
Caucasian 9 1.51 0.131 1.328(0.919–1.917) 53.02 0.000 84.90% 0.52 0.602 0.58 0.582 0.130 0.742 0.344 0.612 0.945 0.994 0.999 0.991 0.999 1.000
Asian 6 1.40 0.162 1.138(0.950–1.363) 6.33 0.276 21.00% 0.38 0.707 0.16 0.879 0.160 0.999 0.325 0.591 0.941 0.994 0.999 0.996 1.000 1.000
Source of control
HB 6 1.27 0.204 1.291(0.870–1.916) 28.60 0.000 82.50% 0.00 1.000 0.08 0.940 0.205 0.772 0.443 0.705 0.963 0.996 1.000 0.993 0.999 1.000
PB 9 1.31 0.190 1.266(0.993–1.614) 30.76 0.000 74.00% 0.73 0.466 0.67 0.523 0.057 0.914 0.157 0.359 0.860 0.984 0.998 0.988 0.999 1.000
Cancer types
Laryngeal SCC 2 0.25 0.802 1.202(0.286–5.053) 12.77 0.000 92.20% 0.00 1.000 . . 0.802 0.619 0.795 0.921 0.992 0.999 1.000 0.992 0.999 1.000
Oral SCC 5 2.42 0.016 1.374(1.062–1.777) 6.12 0.191 34.60% 1.71 0.086 −2.21 0.114 0.015 0.748 0.058 0.157 0.672 0.954 0.995 0.964 0.996 1.000
Cervical SCC 3 1.12 0.263 0.892(0.730–1.090) 1.09 0.579 0.00% 1.04 0.296 1.31 0.416 0.264 0.998 0.442 0.704 0.963 0.996 1.000 0.997 1.000 1.000
Esophageal SCC 3 3.58 0.000 0.482(0.323–0.719) 5.29 0.071 62.20% 1.04 0.296 −4.80 0.131 0.000 0.056 0.018 0.053 0.381 0.861 0.984 0.505 0.911 0.999
Genotyping method
PCR 10 1.32 0.187 1.171(0.926–1.481) 27.73 0.001 67.50% 1.79 0.074 1.11 0.3 0.187 0.981 0.365 0.633 0.950 0.995 0.999 0.995 1.000 1.000
PCR-RFLP 4 1.26 0.209 1.591(0.772–3.280) 21.03 0.000 85.70% −0.34 1.000 −0.23 0.838 0.208 0.437 0.589 0.811 0.979 0.998 1.000 0.990 0.999 1.000
GG/AA+AG
Overall 15 1.44 0.149 1.136(0.955–1.352) 15.65 0.208 23.30% 2.01 0.044 2.37 0.037 0.151 0.999 0.312 0.576 0.937 0.993 0.999 0.996 1.000 1.000
Ethnicity
Caucasian 9 1.21 0.228 1.117(0.933–1.336) 13.10 0.109 38.90% 0.94 0.348 1.66 0.140 0.226 0.999 0.404 0.670 0.957 0.996 1.000 0.997 1.000 1.000
Asian 6 1.12 0.263 1.496(0.739–3.031) 1.94 0.584 0.00% 1.02 0.308 3.23 0.084 0.264 0.503 0.611 0.825 0.981 0.998 1.000 0.992 0.999 1.000
Source of control
HB 6 0.60 0.548 1.096(0.813–1.476) 3.08 0.545 0.00% 1.22 0.221 2.15 0.121 0.546 0.981 0.626 0.834 0.982 0.998 1.000 0.997 1.000 1.000
PB 9 1.34 0.179 1.158(0.935–1.434) 12.67 0.081 44.70% 1.36 0.174 1.50 0.184 0.179 0.991 0.351 0.619 0.947 0.994 0.999 0.995 1.000 1.000
Cancer types
Laryngeal SCC 2 0.48 0.633 1.085(0.777–1.514) 1.58 0.209 36.50% 0.00 1.000 . . 0.631 0.972 0.661 0.854 0.985 0.998 1.000 0.997 1.000 1.000
Oral SCC 5 3.53 0.000 12.702(3.093–52.156) 1.94 0.380 0.00% 0.00 1.000 0.49 0.709 0.000 0.002 0.454 0.714 0.965 0.996 1.000 0.923 0.992 1.000
Cervical SCC 3 0.13 0.895 1.016(0.800–1.290) 0.74 0.691 0.00% 1.04 0.296 −3.83 0.163 0.896 0.999 0.729 0.890 0.989 0.999 1.000 0.998 1.000 1.000
Esophageal SCC 3 0.51 0.610 0.863(0.489–1.521) 1.21 0.545 0.00% 1.04 0.296 5.60 0.112 0.610 0.814 0.692 0.871 0.987 0.999 1.000 0.995 0.999 1.000
Genotyping method
PCR 10 1.45 0.148 1.145(0.953–1.376) 1.88 0.209 26.50% 1.15 0.251 2.13 0.071 0.148 0.998 0.309 0.573 0.937 0.993 0.999 0.995 1.000 1.000
PCR-RFLP 4 1.32 0.18 1.696(0.774–3.717) 2.93 0.231 31.70% 0.00 1.000 16.96 0.037 0.186 0.380 0.596 0.816 0.980 0.998 1.000 0.990 0.999 1.000

Abbreviations: OR, odds ratio; CI, confidence interval; PB, population-based; HB, hospital-based; SCC, squamous cell carcinoma; FPRP, false positive report probability; BFDP, Bayesian false discovery probability. The results in bold represented that there was statistically significant noteworthiness at 0.2 level by FPRP or 0.8 level by BFDP calculations.

Figure 2 
                  Statistical relationship between IL-10 rs1800896 gene polymorphism and squamous cell carcinoma susceptibility in four models: (a) GG vs AA, (b) AG vs AA, (c) AG + GG vs AA, and (d) GG vs AA + AG. Abbreviations: OR: odds ratio; CI: confidence interval.
Figure 2

Statistical relationship between IL-10 rs1800896 gene polymorphism and squamous cell carcinoma susceptibility in four models: (a) GG vs AA, (b) AG vs AA, (c) AG + GG vs AA, and (d) GG vs AA + AG. Abbreviations: OR: odds ratio; CI: confidence interval.

Table 5 summarizes the relationship between the IL-10 rs1800872 gene polymorphism and the SCC risk. The overall analysis revealed that the IL-10 rs1800872 gene polymorphism and the SCC risk were significantly associated in both models (CC/AA: OR = 0.59, 95% CI: 0.44–0.81, p = 0.001; CC/AA + AC: OR = 0.71, 95% CI: 0.59–0.86, p = 0.000; Figure 3). However, the IL-10 rs1800872 gene polymorphism and the SCC risk showed no significant association in the other two models (AC/AA: OR = 0.87, 95% CI: 0.69–1.11, p = 0.266; AC + CC/AA: OR = 0.78, 95% CI: 0.60–1.00, p = 0.050; Figure 3). The subgroup analysis by ethnicity revealed a significantly increased SCC risk in Caucasians in all models (CC/AA: OR = 0.48, 95% CI: 0.31–0.74, p = 0.001; AC/AA: OR = 0.68, 95% CI: 0.54–0.86, p = 0.001; AC + CC/A: OR = 0.58, 95% CI: 0.46–0.72, p = 0.000; CC/AA + AC: OR = 0.68, 95% CI: 0.52–0.89, p = 0.004). Similarly, Asians showed an increased risk (CC/AA + AC: OR = 0.80, 95% CI: 0.63–1.00, p = 0.049). The subgroup analysis by control source revealed a significantly increased SCC risk among population-based controls in both models (CC/AA: OR = 0.55, 95% CI: 0.41–0.73, p = 0.000; CC/AA + AC: OR = 0.73, 95% CI: 0.61–0.87, p = 0.000) and a significantly increased risk among hospital-based controls in only one model (CC/AA + AC: OR = 0.69, 95% CI: 0.48–0.98, p = 0.036). The subgroup analysis by cancer type revealed a significantly increased oral SCC risk (CC/AA: OR = 0.28, 95% CI: 0.17–0.49, p = 0.000; AC/AA: OR = 0.39, 95% CI: 0.24–0.62, p = 0.000; AC + CC/AA: OR = 0.36, 95% CI: 0.23–0.57, p = 0.000; CC/AA + AC: OR = 0.63, 95% CI: 0.44–0.89, p = 0.000). The risk of cervical SCC was also significantly increased (CC/AA: OR = 0.46, 95% CI: 0.25–0.84, p = 0.011; AC + CC/AA: OR = 0.62, 95% CI: 0.46–0.83, p = 0.001; CC/AA + AC: OR = 0.54, 95% CI: 0.35–0.83, p = 0.005). All gene models showed heterogeneity (CC/AA: I 2 = 61.40%, p = 0.002; AC/AA: I 2 = 65.10%, p = 0.001; AC + CC/AA: I 2 = 72.20%, p = 0.000; CC/AA + AC: I 2 = 51.80%, p = 0.023).

Table 5

Stratified analyses of the IL-10 rs1800872 polymorphism on squamous cell carcinoma risk

Comparative model No. Z p OR (95% CI) Heterogeneity Z Begg's test t Egger's test FPRP p-value FPRP Statistical power FPRP prior probability BEDP prior probability
Heterogeneity chi-squared p I 2 0.250 0.10 0.01 0.001 0.0001 0.01 0.001 0.000001
CC/AA
Overall 11 3.34 0.001 0.595(0.438–0.807) 27.83 0.002 61.40% 0.93 0.350 −0.83 0.428 0.001 0.232 0.011 0.032 0.264 0.783 0.973 0.677 0.955 1.000
Ethnicity
Caucasian 7 3.35 0.001 0.481(0.314–0.738) 16.55 0.011 63.80% 0.00 1.000 0.14 0.895 0.001 0.068 0.035 0.097 0.541 0.923 0.992 0.676 0.955 1.000
Asian 4 1.77 0.076 0.807(0.636–1.023) 2.57 0.462 0.00% −0.34 1.000 0.01 0.991 0.076 0.943 0.196 0.422 0.889 0.988 0.999 0.991 0.999 1.000
Source of control
HB 5 1.93 0.054 0.635(0.401–1.007) 16.71 0.002 76.10% 0.73 0.462 −0.73 0.518 0.054 0.418 0.278 0.536 0.927 0.992 0.999 0.981 0.998 1.000
PB 6 4.12 0.000 0.550(0.413–0.730) 9.85 0.080 49.20% 0.38 0.707 0.14 0.895 0.000 0.091 0.001 0.003 0.036 0.276 0.792 0.114 0.566 0.992
Cancer types
Laryngeal SCC 2 0.67 0.505 0.852(0.533–1.363) 0.01 0.924 0.00% 0.00 1.000 0.504 0.847 0.641 0.843 0.983 0.998 1.000 0.995 1.000 1.000
Oral SCC 2 4.61 0.000 0.284(0.167–0.485) 0.38 0.538 0.00% 0.00 1.000 0.000 0.001 0.013 0.039 0.310 0.819 0.978 0.040 0.298 0.977
Cervical SCC 3 2.54 0.011 0.458(0.250–0.836) 6.60 0.037 69.70% 0.00 1.000 −0.15 0.906 0.011 0.111 0.229 0.472 0.908 0.990 0.999 0.945 0.994 1.000
Esophageal SCC 4 1.63 0.102 0.817(0.640–1.041) 2.90 0.408 0.00% −0.34 1.000 0.40 0.727 0.102 0.950 0.244 0.492 0.914 0.991 0.999 0.992 0.999 1.000
Genotyping method
PCR 7 2.60 0.009 0.613(0.423–0.887) 19.63 0.003 69.40% 1.50 0.133 −1.19 0.287 0.009 0.328 0.079 0.206 0.740 0.966 0.997 0.938 0.993 1.000
PCR-RFLP 3 2.1 0.036 0.490(0.252–0.953) 5.54 0.063 63.90% 0.00 1.000 −0.07 0.953 0.035 0.182 0.369 0.637 0.951 0.995 0.999 0.974 0.997 1.000
AC/AA
Overall 11 1.11 0.266 0.872(0.686–1.110) 28.64 0.001 65.10% 0.93 0.350 −0.84 0.423 0.266 0.985 0.447 0.708 0.964 0.996 1.000 0.996 1.000 1.000
Ethnicity
Caucasian 7 3.24 0.001 0.679(0.537–0.858) 9.85 0.131 39.10% 0.60 0.548 −0.16 0.880 0.001 0.561 0.006 0.019 0.173 0.678 0.955 0.757 0.969 1.000
Asian 4 0.77 0.438 1.126(0.835–1.518) 10.38 0.016 71.10% 1.02 0.308 1.37 0.304 0.436 0.970 0.574 0.802 0.978 0.998 1.000 0.996 1.000 1.000
Source of control
HB 5 0.43 0.670 0.946(0.734–1.220) 8.51 0.075 53.00% −0.24 1.000 −0.37 0.736 0.669 0.996 0.668 0.858 0.985 0.999 1.000 0.998 1.000 1.000
PB 6 0.94 0.349 0.792(0.487–1.289) 19.28 0.002 74.10% 0.38 0.707 −0.47 0.663 0.348 0.756 0.580 0.806 0.979 0.998 1.000 0.994 0.999 1.000
Cancer types
Laryngeal SCC 2 0.73 0.463 1.298(0.647–2.603) 3.07 0.080 67.40% 0.00 1.000 0.463 0.658 0.678 0.863 0.986 0.999 1.000 0.993 0.999 1.000
Oral SCC 2 3.95 0.000 0.387(0.241–0.620) 0.24 0.624 0.00% 0.00 1.000 0.000 0.012 0.020 0.056 0.397 0.869 0.985 0.258 0.778 0.997
Cervical SCC 3 1.32 0.188 0.808(0.588–1.110) 2.10 0.350 4.60% 1.04 0.296 1.57 0.361 0.188 0.882 0.390 0.658 0.955 0.995 1.000 0.994 0.999 1.000
Esophageal SCC 4 0.15 0.878 0.988(0.852–1.146) 5.54 0.136 45.80% 0.34 0.734 0.21 0.850 0.873 1.000 0.724 0.887 0.989 0.999 1.000 0.999 1.000 1.000
Genotyping method
PCR 7 0.94 0.347 0.889(0.695–1.137) 13.24 0.039 54.70% 0.60 0.548 −0.92 0.402 0.348 0.989 0.514 0.760 0.972 0.997 1.000 0.997 1.000 1.000
PCR-RFLP 3 0.44 0.658 0.835(0.376–1.853) 15.33 0.000 86.90% 0.00 1.000 −0.26 0.835 0.657 0.710 0.735 0.893 0.989 0.999 1.000 0.994 0.999 1.000
AC+CC/AA
Overall 11 1.96 0.050 0.775(0.601–1.000) 35.98 0.000 72.20% 1.09 0.276 −1.17 0.272 0.050 0.877 0.146 0.339 0.850 0.983 0.998 0.986 0.999 1.000
Ethnicity
Caucasian 7 4.83 0.000 0.579(0.463–0.722) 10.94 0.090 45.20% 0.30 0.764 0.29 0.784 0.000 0.105 0.000 0.000 0.001 0.011 0.104 0.006 0.058 0.861
Asian 4 0.36 0.717 1.052(0.799–1.384) 9.77 0.021 69.30% 0.34 0.734 1.02 0.415 0.717 0.994 0.684 0.867 0.986 0.999 1.000 0.997 1.000 1.000
Source of control
HB 5 1.17 0.241 0.822(0.592–1.141) 15.91 0.003 74.90% −0.24 1.000 −0.78 0.495 0.241 0.895 0.447 0.708 0.964 0.996 1.000 0.995 0.999 1.000
PB 6 1.38 0.167 0.724(0.457–1.144) 18.78 0.002 73.40% 0.00 1.000 −0.46 0.669 0.166 0.638 0.439 0.701 0.963 0.996 1.000 0.991 0.999 1.000
Cancer types
Laryngeal SCC 2 0.60 0.548 1.182(0.685–2.041) 2.15 0.143 53.50% 0.00 1.000 0.549 0.804 0.972 0.860 0.985 0.999 1.000 0.995 0.999 1.000
Oral SCC 2 4.37 0.000 0.358(0.226–0.567) 0.27 0.606 0.00% 0.00 1.000 0.000 0.004 0.009 0.026 0.227 0.748 0.967 0.069 0.426 0.987
Cervical SCC 3 3.20 0.001 0.615(0.457–0.829) 3.44 0.179 41.80% 1.04 0.296 1.59 0.357 0.001 0.298 0.014 0.041 0.320 0.826 0.979 0.767 0.971 1.000
Esophageal SCC 4 0.20 0.843 0.975(0.762–1.249) 6.44 0.092 53.40% 0.34 0.734 0.30 0.793 0.841 0.999 0.716 0.883 0.988 0.999 1.000 0.998 1.000 1.000
Genotyping method
PCR 7 1.78 0.076 0.767(0.573–1.028) 21.01 0.002 71.40% 0.60 0.548 −1.37 0.229 0.075 0.826 0.216 0.453 0.901 0.989 0.999 0.989 0.999 1.000
PCR-RFLP 3 0.75 0.451 0.751(0.357–1.581) 14.70 0.001 86.40% 0.00 1.000 −0.36 0.778 0.451 0.623 0.685 0.867 0.986 0.999 1.000 0.993 0.999 1.000
CC/AA+AC
Overall 11 3.59 0.000 0.711(0.590–0.857) 20.75 0.023 51.80% 0.00 1.000 −0.72 0.487 0.000 0.750 0.001 0.004 0.043 0.314 0.821 0.539 0.922 0.999
Ethnicity
Caucasian 7 2.85 0.004 0.677(0.518–0.885) 18.33 0.005 67.30% 0.00 1.000 −0.64 0.553 0.004 0.545 0.023 0.067 0.440 0.888 0.988 0.900 0.989 1.000
Asian 4 1.97 0.049 0.796(0.634–0.999) 1.66 0.647 0.00% 0.34 0.734 −0.87 0.474 0.049 0.937 0.136 0.320 0.838 0.981 0.998 0.987 0.999 1.000
Source of control
HB 5 2.09 0.036 0.687(0.484–0.976) 16.55 0.002 75.80% 0.24 0.806 −1.15 0.332 0.036 0.567 0.161 0.365 0.863 0.985 0.998 0.978 0.998 1.000
PB 6 3.54 0.000 0.729(0.612–0.868) 4.19 0.522 0.00% 0.75 0.452 −0.05 0.961 0.000 0.842 0.001 0.004 0.043 0.314 0.821 0.575 0.932 0.999
Cancer types
Laryngeal SCC 2 0.73 0.463 0.904(0.690–1.184) 0.95 0.329 0.00% 0.00 1.000 0.463 0.987 0.585 0.809 0.979 0.998 1.000 0.997 1.000 1.000
Oral SCC 2 2.62 0.000 0.625(0.440–0.888) 0.01 0.920 0.00% 0.00 1.000 0.009 0.359 0.068 0.179 0.706 0.960 0.996 0.935 0.993 1.000
Cervical SCC 3 2.79 0.005 0.538(0.348–0.831) 8.23 0.016 75.70% 0.00 1.000 −3.11 0.198 0.005 0.167 0.085 0.219 0.755 0.969 0.997 0.902 0.989 1.000
Esophageal SCC 4 1.34 0.181 0.860(0.689–1.073) 2.91 0.406 0.00% 0.34 0.734 0.48 0.678 0.182 0.988 0.355 0.623 0.948 0.995 0.999 0.995 1.000 1.000
Genotyping method
PCR 7 2.93 0.003 0.700(0.551–0.888) 16.81 0.01 64.30% 0.30 0.764 −1.10 0.321 0.003 0.656 0.015 0.043 0.332 0.834 0.980 0.883 0.987 1.000
PCR-RFLP 3 2.98 0.003 0.625(0.459–0.852) 0.01 0.996 0.00% 0.00 1.000 0.67 0.626 0.003 0.342 0.025 0.072 0.461 0.896 0.989 0.859 0.984 1.000

Abbreviations: OR, odds ratio; CI, confidence interval; PB, population-based; HB, hospital-based; SCC, squamous cell carcinoma; FPRP, false positive report probability; BFDP, Bayesian false discovery probability. The results in bold represented that there was statistically significant noteworthiness at 0.2 level by FPRP or 0.8 level by BFDP calculations.

Figure 3 
                  Statistical relationship between IL-10 rs1800872 gene polymorphism and squamous cell carcinoma susceptibility in four models: (a) CC vs AA, (b) AC vs AA, (c) AC + CC vs AA, and (d) CC vs AA + AC.
Figure 3

Statistical relationship between IL-10 rs1800872 gene polymorphism and squamous cell carcinoma susceptibility in four models: (a) CC vs AA, (b) AC vs AA, (c) AC + CC vs AA, and (d) CC vs AA + AC.

Table 6 shows the association between the IL-6 rs1800795 gene polymorphism and the SCC risk. The association between the IL-6 rs1800795 gene polymorphism and the SCC risk was explored in all models (CC/GG: OR = 1.11, 95% CI: 0.66–1.87, p = 0.702; GC/GG: OR = 1.13, 95% CI: 0.74–1.73, p = 0.58; GC + CC/GG: OR = 1.09, 95% CI: 0.70–1.70, p = 0.697; CC/GG + GC: OR = 1.01, 95% CI: 0.67–1.53, p = 0.958; Figure 4). The subgroup analysis by ethnicity showed no significant association in any model. The subgroup analysis by control source revealed a significantly increased risk among hospital-based controls in both models (CC/GG: OR = 1.43, 95% CI: 1.09–1.88, p = 0.009; GC + CC/GG: OR = 1.24, 95% CI: 1.01–1.53, p = 0.044). The subgroup analysis by cancer type revealed a significantly increased laryngeal SCC risk in one model (GC + CC/GG: OR = 1.38, 95% CI: 1.02–1.86, p = 0.035). All gene models showed heterogeneity (CC/GG: I 2 = 73.40%, p = 0.000; GC/GG: I 2 = 79.40%, p = 0.000; GC + CC/GG: I 2 = 83.40%, p = 0.000; CC/GG + GC: I 2 = 68.00%, p = 0.003). No other results were statistically significant.

Table 6

Stratified analyses of the IL-6 rs1800795 polymorphism on squamous cell carcinoma risk

Comparative model No. Z p OR (95% CI) Heterogeneity Z Begg’s test t Egger’s test FPRP p-value FPRP statistical power FPRP prior probability BEDP prior probability
Heterogeneity chi-squared p I 2 0.25 0.10 0.01 0.001 0.0001 0.01 0.001 0.000001
CC/GG
Overall 8 0.38 0.702 1.107(0.657–1.867) 26.31 0.000 73.40% 0.12 0.902 −0.75 0.479 0.703 0.873 0.707 0.879 0.988 0.999 1.000 0.995 1.000 1.000
Ethnicity
Caucasian 6 0.61 0.54 1.244(0.619–2.497) 17.88 0.003 72.00% 0.00 1.000 −0.2 0.849 0.539 0.701 0.698 0.874 0.987 0.999 1.000 0.994 0.999 1.000
Asian 2 0.36 0.717 0.759(0.171–3.368) 8.39 0.004 88.10% 0.00 1.000 0.717 0.568 0.791 0.919 0.992 0.999 1.000 0.992 0.999 1.000
Source of control
HB 2 2.6 0.009 1.433(1.093–1.878) 0.20 0.654 0.00% 0.00 1.000 0.009 0.630 0.042 0.115 0.589 0.935 0.993 0.944 0.994 1.000
PB 6 0.08 0.938 0.963(0.374–2.479) 23.75 0.000 78.90% 0.75 0.452 0.07 0.944 0.938 0.777 0.784 0.916 0.992 0.999 1.000 0.993 0.999 1.000
Cancer types
Oral SCC 3 0.03 0.974 0.969(0.150–6.274) 21.24 0.000 90.60% 0.00 1.000 0.13 0.916 0.974 0.653 0.817 0.931 0.993 0.999 1.000 0.991 0.999 1.000
Laryngeal SCC 2 1.71 0.087 1.377(0.955–1.985) 0.13 0.723 0.00% 0.00 1.000 0.086 0.677 0.277 0.535 0.927 0.992 0.999 0.988 0.999 1.000
Genotyping method
PCR 5 0.54 0.587 1.258(0.549–2.884) 17.81 0.001 77.50% −0.24 1.000 −0.15 0.892 0.587 0.661 0.727 0.889 0.989 0.999 1.000 0.993 0.999 1.000
PCR-RFLP 2 0.36 0.717 0.759(0.171–3.368) 8.39 0.004 88.10% 0.00 1.000 0.72 0.568 0.791 0.919 0.992 0.999 1.000 0.988 0.999 1.000
GC/GG
Overall 8 0.55 0.584 1.127(0.735–1.730) 34.02 0.000 79.40% −0.12 1.000 −0.48 0.647 0.585 0.904 0.660 0.853 0.985 0.998 1.000 0.996 1.000 1.000
Ethnicity
Caucasian 6 0.87 0.386 1.282(0.731–2.250) 22.44 0.000 77.70% 0.38 0.707 −0.93 0.404 0.387 0.708 0.621 0.831 0.982 0.998 1.000 0.994 0.999 1.000
Asian 2 0.65 0.516 0.814(0.438–1.514) 4.06 0.044 75.30% 0.00 1.000 0.516 0.736 0.678 0.863 0.986 0.999 1.000 0.994 0.999 1.000
Source of control
HB 2 1.41 0.158 1.174(0.940–1.467) 0.89 0.345 0.00% 0.00 1.000 0.158 0.984 0.325 0.591 0.941 0.994 0.999 0.995 0.999 1.000
PB 6 0.15 0.880 1.059(0.503–2.230) 32.88 0.00 84.80% 0.00 1.000 −1.14 0.319 0.880 0.820 0.763 0.906 0.990 0.999 1.000 0.997 1.000 1.000
Cancer types
Oral SCC 3 0.36 0.718 1.276(0.339–4.807) 25.62 0.00 92.20% 0.00 1.000 −0.38 0.769 0.719 0.594 0.784 0.916 0.992 0.999 1.000 0.992 0.999 1.000
Laryngeal SCC 2 1.95 0.051 1.371(0.998–1.884) 0.15 0.702 0.00% 0.00 1.000 0.052 0.710 0.179 0.396 0.878 0.986 0.999 0.984 0.998 1.000
Genotyping method
PCR 5 1.63 0.103 1.554(0.915–2.640) 15.12 0.004 73.50% 0.24 0.806 −0.33 0.763 0.103 0.448 0.408 0.674 0.958 0.996 1.000 0.987 0.999 1.000
PCR-RFLP 2 0.65 0.516 0.814(0.438–1.514) 4.06 0.044 75.30% 0.00 1.000 0.515 0.736 0.678 0.863 0.986 0.999 1.000 0.994 0.999 1.000
GC + CC/GG
Overall 8 0.39 0.697 1.092(0.701–1.700) 42.10 0.000 83.40% 0.62 0.536 −0.66 0.536 0.697 0.920 0.694 0.872 0.989 0.999 1.000 0.996 1.000 1.000
Ethnicity
Caucasian 6 0.68 0.494 1.225(0.685–2.191) 27.83 0.000 82.00% 0.75 0.452 −0.96 0.393 0.494 0.753 0.663 0.855 0.985 0.998 1.000 0.994 0.999 1.000
Asian 2 0.55 0.584 1.092(0.701–1.700) 8.15 0.004 87.70% 0.00 1.000 0.697 0.920 0.694 0.872 0.987 0.999 1.000 0.996 1.000 1.000
Source of control
HB 2 2.01 0.044 1.241(1.006–1.532) 0.35 0.556 0.00% 0.00 1.000 0.045 0.961 0.122 0.294 0.821 0.979 0.998 0.987 0.999 1.000
PB 6 0.00 0.997 1.002(0.461–2.178) 41.69 0.000 88.00% 0.38 0.707 −0.99 0.380 0.996 0.846 0.779 0.914 0.991 0.999 1.000 0.994 0.999 1.000
Cancer types
Oral SCC 3 0.12 0.906 1.093(0.250–4.792) 35.92 0.000 94.40% 0.00 1.000 −0.42 0.747 0.906 0.663 0.804 0.925 0.993 0.999 1.000 0.992 0.999 1.000
Laryngeal SCC 2 2.11 0.035 1.380(1.024–1.860) 0.2 0.655 0.00% 0.00 1.000 0.034 0.708 0.127 0.304 0.828 0.980 0.998 0.979 0.998 1.000
Genotyping method
PCR 5 1.10 0.269 1.408(0.767–2.586) 22.43 0.000 82.20% 0.24 0.806 −0.52 0.639 0.269 0.581 0.582 0.807 0.979 0.998 1.000 0.992 0.999 1.000
PCR-RFLP 2 0.55 0.584 0.794(0.348–1.811) 8.15 0.004 87.70% 0.00 1.000 0.583 0.661 0.726 0.888 0.989 0.999 1.000 0.993 0.999 1.000
CC/GG + GC
Overall 8 0.05 0.958 1.011(0.667–1.532) 21.84 0.003 68.00% 0.12 0.902 −0.60 0.570 0.959 0.969 0.748 0.899 0.990 0.999 1.000 0.996 1.000 1.000
Ethnicity
Caucasian 6 0.19 0.846 1.055(0.614–1.811) 14.49 0.013 65.50% 0.00 1.000 0.07 0.945 0.846 0.899 0.738 0.894 0.989 0.999 1.000 0.995 1.000 1.000
Asian 2 0.30 0.765 0.825(0.233–2.921) 6.48 0.011 84.60% 0.00 1.000 0.766 0.629 0.785 0.916 0.992 0.999 1.000 0.992 0.999 1.000
Source of control
HB 2 1.94 0.053 1.247(0.997–1.559) 1.51 0.219 33.70% 0.00 1.000 0.053 0.948 0.143 0.334 0.846 0.982 0.998 0.988 0.999 1.000
PB 6 0.29 0.769 0.899(0.441–1.832) 16.70 0.005 70.10% 0.75 0.452 1.12 0.327 0.769 0.795 0.744 0.897 0.990 0.999 1.000 0.994 0.999 1.000
Cancer types
Oral SCC 3 0.35 0.728 0.802(0.232–2.774) 12.71 0.002 84.30% 1.04 0.296 0.66 0.629 0.727 0.615 0.780 0.914 0.992 0.999 1.000 0.992 0.999 1.000
Laryngeal SCC 2 0.77 0.441 1.120(0.839–1.494) 0.17 0.683 0.00% 0.00 1.000 0.441 0.977 0.575 0.802 0.978 0.998 1.000 0.997 1.000 1.000
Genotyping method
PCR 5 0.06 0.951 0.981(0.531–1.813) 13.62 0.009 70.60% −0.24 1.000 −0.13 0.9204 0.951 0.891 0.762 0.906 0.991 0.999 1.000 0.995 1.000 1.000
PCR-RFLP 2 0.3 0.765 0.825(0.233–2.921) 6.48 0.011 84.60% 0.00 1.000 0.765 0.629 0.785 0.916 0.992 0.999 1.000 0.992 0.999 1.000

Abbreviations: OR, odds ratio; CI, confidence interval; PB, population-based; HB, hospital-based; SCC, squamous cell carcinoma; FPRP, false positive report probability; BFDP, Bayesian false discovery probability. The results in bold represented that there was statistically significant noteworthiness at 0.2 level by FPRP or 0.8 level by BFDP calculations.

Figure 4 
                  Statistical relationship between IL-10 rs1800795 gene polymorphism and squamous cell carcinoma susceptibility in four models: (a) CC vs GG, (b) GC vs GG, (c) GC + CC vs GG, and (d) CC vs GG + GC.
Figure 4

Statistical relationship between IL-10 rs1800795 gene polymorphism and squamous cell carcinoma susceptibility in four models: (a) CC vs GG, (b) GC vs GG, (c) GC + CC vs GG, and (d) CC vs GG + GC.

3.3 Publication bias

The funnel plot revealed no significant asymmetry in the IL-10 rs1800896 gene polymorphism in any model (Figure S1). However, Egger’s test revealed a publication bias in the IL-10 rs1800896 models (p = 0.018 for GG/AA; p = 0.037 for GG/AA + AG). IL-18 rs1800872 or IL-6 rs1800795 models showed no asymmetry in the funnel shape (Figures S2 and S3). In addition, Egger’s test revealed no publication bias in IL-10 1800872 or IL-6 rs1800795 models.

3.4 Sensitivity analysis

The sensitivity analysis revealed no significant changes in the combination OR corresponding to the IL-10 rs1800896 or rs1800872 or IL-6 rs1800795 gene polymorphism, proving our results to be statistically robust (Figures S4–S6). The meta-regression analysis showed that the publication year, ethnicity, or control source did not affect the stability of the combined results (Table S1).

3.5 FPRP and BFDP tests

Tables S2, S3, and S4 show the FPRPs of IL-10 rs1800896 and rs1800872 and IL-6 rs1800795 gene polymorphisms, respectively. At prior probabilities of 0.25 and 0.1, FPRP and BFDP test results showed statistically significant results in almost all models of rs1800896, rs1800872, and rs1800795 polymorphisms, with an OR of 1.5.

4 Discussion

SCC can be caused by exposure to carcinogens, such as sunlight, tobacco, alcohol, and viral infections. It shows a high percentage of somatic genetic mutations. All SCC cases have similar mutation patterns [4446]. The relationship between the IL-10 and IL-6 gene polymorphisms and the SCC risk has been shown. Gene polymorphisms affect their messenger RNA (mRNA) and protein levels. The IL-6 rs1800795 polymorphism may affect the IL-6 mRNA expression. The G allele of the IL-6 rs1800795 promoter single-nucleotide polymorphism is associated with elevated IL-6 mRNA transcription levels after in vitro endotoxin or IL-1 stimulation [47]. The presence of a variant allele G in tumor tissue is positively associated with elevated IL-10 mRNA levels [48]. Wang et al. analyzed the same polymorphisms and reported significantly higher IL-10 mRNA levels in patients with non-small cell lung cancer with the non-ATA haplotype, showing the association of cytokine IL-10 expression levels with tumor progression [49]. Currently, no relevant reports have been published on the polymorphism of these three genes or proteins. They may indirectly affect the protein expression after affecting the mRNA expression. Associations of IL-10 and IL-6 gene polymorphisms and oral and cervical SCC risks have been frequently demonstrated in meta-analyses [50,51]. However, no meta-analysis has revealed an association of IL-10 or IL-6 gene polymorphisms with the SCC risk. Therefore, we re-examined the relationship between IL-10 and IL-6 gene polymorphisms and the SCC risk from a comprehensive and unified perspective to draw a more accurate conclusion.

We investigated the IL-10 rs1800896 gene polymorphism to find its relationship with the SCC risk. The overall analysis revealed no positive results. However, the subgroup analysis by cancer type revealed positive results, indicating that the IL-10 rs1800896 gene polymorphism was a risk factor for oral SCC. Li et al. [50] also conducted a study on the association of the IL-10 gene polymorphism and the oral cancer risk. They reported that the IL-10 rs1800896 polymorphism increased the risk of oral cancers, including non-SCC, in both dominant and recessive genetic models. Both present and previous studies showed that the IL-10 rs1800896 gene polymorphism increased the oral cancer risk. However, the present study investigated SCC, while previous studies did not differentiate SCC and non-SCC. In addition, the present subgroup analysis showed that other cancer types, such as cervical or esophageal SCC, showed no positive results. These results were consistent with those of Ni et al.’s meta-analysis of eight studies. Furthermore, the IL-10 rs1800896 gene polymorphism affects carcinoma of the uterine cervix [52]. However, the present results were inconsistent with the results of Li et al.’s meta-analysis of seven studies, which showed that the IL-10 rs1800896 gene polymorphism could increase the risk of esophageal cancer, possibly including non-SCC [53]. In the present study, the IL-10 rs1800896 polymorphism showed different impressions in diverse organs, probably because of the IL-10 rs1800896 gene polymorphisms in different parts of the body having different distributions in different cancer types. However, the exact mechanism remains unclear. The subgroup analyses did not include a sufficient sample size. Therefore, we should exercise caution in drawing conclusions. Future, large-scale studies should be conducted. No link between the IL-10 rs1800896 gene polymorphism and the SCC risk was found in hospital- or population-based models.

With the IL-10 rs1800872 gene polymorphism, the SCC risk was low, particularly in oral SCC. Subgroup analyses by ethnicity indicated that the IL-10 rs1800872 polymorphism might be a protective factor in the Caucasian population but a risk factor in the Asian population. The IL-10 rs1800872 gene polymorphism had different effects on the Caucasian and Asian populations. The reason might be that the proportion of the gene expression differed among ethnic groups. These differences may be derived from different genetic backgrounds and environmental exposures, such as the difference in minor allele frequencies in healthy controls among the Caucasian and Asian populations. Therefore, inconsistent associations indicate the possibility of differences in the magnitude of the IL-10 rs1800872 gene polymorphism contribution to the SCC risk across different genetic backgrounds and environmental exposures [54]. The subgroup analysis by cancer type showed no association between the IL-10 rs1800872 polymorphism and the esophageal SCC risk, broadly consistent with the included independent studies [20,24,36,37].

The IL-6 rs1800795 gene polymorphism and the SCC risk showed no association. However, the subgroup analysis by control source revealed that the presence of the IL-6 rs1800795 gene polymorphism increased the SCC risk among hospital-based controls but not among population-based controls. The possible reasons are as follows: (1) some studies included hospital-based controls, which could induce an inherent selection bias because the hospital population does not represent the general population and (2) hospital-based controls may have other diseases that affect the release of interleukins, affecting the present results. Thus, appropriate and representative control populations played roles in assessing the relationship between the gene polymorphism and the disease risk. We found inconsistent results for the same cancer among independent studies. One study showed that the CC genotype of IL-6 rs1800795 may be protective in patients with oral SCC [42]. Another study showed a seven-fold increased risk of oral SCC with the CC genotype [43]. The results of the two studies were directly opposite [42,43]; therefore, we performed a subgroup analysis by cancer type. However, the results showed no significant association between the IL-6 rs1800795 polymorphism and the oral SCC risk, consistent with previous meta-analysis results [55]. A previous meta-analysis of 11 studies conducted by Rezaei et al. also confirmed that the IL-6 rs1800795 gene polymorphism was not associated with the oral cancer risk. Thus, the strength of the association between the IL-6 rs1800795 gene polymorphism and the oral cancer risk could be evaluated using meta-analyses, and the results of this study were more accurate than those of each independent study. All studies that met the eligibility criteria were included in this study, but a larger sample size would increase the reliability of the conclusions.

This meta-analysis has some advantages. First, no studies on the association between the IL-10 rs1800896 and rs1800872 and IL-6 rs1800795 gene polymorphisms and the SCC risk have been reported. Second, this was the most comprehensive study on this topic, with sufficient statistical power. However, several limitations exist. First, many studies had to be excluded because they did not report the relevant cancer type or pathologically diagnosed SCC. Further, some studies had to be excluded because they did not report the proportion of SCC by genotype. Finally, the number of included studies was small. Future, large-scale studies are required to more accurately explain the association between the studied genotypes and the SCC risk. Second, we only included articles published in English or Chinese, which might lead to a language bias. The preponderance of Asians in the original data could also be a bias. Third, the pathogenesis of SCC was affected by various factors, such as environmental changes, diet, age, and sex, which were not accounted for because of the retrospective study design. Fourth, subgroup analyses included only a few studies, reducing the statistical efficiency. Finally, the statistical heterogeneity frequently existed between the IL-10 rs1800872 and IL-6 rs1800795 gene polymorphisms and the SCC risk during statistical calculations. This could be because of the small number of included studies and the considerable heterogeneity among studies. Therefore, caution should be exercised when drawing conclusions.

5 Conclusions

This meta-analysis showed that the IL-10 rs1800872 gene polymorphism reduced the SCC risk, particularly in Caucasians. However, no IL-10 rs1800896 or IL-6 rs1800795 polymorphism was correlated with the SCC risk. Considering the limitations of this study, further carefully designed, large-scale studies are required to evaluate the association of the IL-10 and IL-6 genetic polymorphisms with the SCC risk.


# These authors contributed equally to this work.

tel: +86-771-2705693, fax: +86-771-2705693

  1. Funding information: This work was supported by the Guangxi Science and Technology Base and Talents Special Project (2021AC18031), Nanning Qingxiu District Science and Technology Plan (2021004), and Guangxi Medical and health-suitable technology development and popularization application project (S2021085).

  2. Author contributions: The authors thank all the participants for their contributions to this study. Z.W. and X.S. collected the data. C.L. and X.H. checked the data. Q.H and Y.H. calculated the data. Z.W., X.S, C.L., and X.H. analyzed the data and wrote the manuscript. 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 and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2022-11-06
Revised: 2023-02-01
Accepted: 2023-02-08
Published Online: 2023-04-15

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

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

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  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”
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