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Association between PAX9 or MSX1 gene polymorphism and tooth agenesis risk: A meta-analysis

  • Xiaoyi Zhong , Kaixin Liu , Zhenmin Liu , Cuiping Li and Wenxia Chen EMAIL logo
Published/Copyright: April 10, 2025
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Open Life Sciences
From the journal Open Life Sciences Volume 20 Issue 1

Abstract

Tooth loss represents the most prevalent form of dental agenesis. Anterior tooth loss primarily impacts aesthetics and psychological well-being, whereas posterior tooth loss influences bone growth patterns and masticatory function. Prolonged tooth loss can significantly hinder subsequent restorative procedures. Genetic factors are among the principal contributors to tooth loss, as genes dictate the location, quantity, and morphology of teeth; mutations at specific gene loci may result in underdevelopment or even complete absence of teeth. Investigating the relationship between gene polymorphisms and tooth loss could yield novel insights for future clinical interventions aimed at addressing this issue. Consequently, this study aims to elucidate the correlation between PAX9 and MSX1 gene polymorphisms and instances of tooth loss. We searched Cochrane, PubMed, Web of Science, MEDLINE, EMBASES, and CNKI journal databases for articles up to April 1, 2024 to determine the association of PAX9 and MSX1 genes with the risk of dental development. Used STATA version 11.2 to calculate the odds ratio (OR) and 95% confidence interval (CI). Analyzed meta-regression, sensitivity, and publication bias. Used Bayesian measures of the false positive reporting probability and false discovery probability to examine the reliability of the calculation. Finally, 12 eligible reports were included in this study, including 7 reports on PAX9 rs2073247, with 873 cases of polymorphism and 812 cases of control; 5 reports on PAX9 rs2073244, with 668 cases of polymorphism and 668 cases of control; 7 reports on MSX1 rs12532, with 762 cases of polymorphism and 1,544 cases of control. The ORs and 95% CIs showed a statistically significant relationship between PAX9 rs2073247 or PAX9 rs2073244 polymorphism and tooth agenesis risk. Moreover, there was no association observed for the MSX1 rs12532 polymorphism. In further subgroup analysis of the polymorphisms (PAX9 rs2073247, PAX9 rs2073244), we found an increased risk of tooth loss in the Caucasian and Hungarian groups. This article concludes that the PAX9 rs2073247 and PAX9 rs2073244 polymorphism might help to increase the risk of tooth agenesis. Understanding the mechanisms of genetic mutations will enable clinical physicians and human geneticists to develop new strategies for future therapeutic research and preventive treatments.

Graphical abstract

Keywords: tooth agenesis; gene polymorphism; PAX9 ; MSX1 ; meta-analysis

1 Introduction

Tooth loss is the most common type of dental malformation currently. When fewer than six teeth are missing (excluding the third permanent molar), it is diagnosed as hypodontia. Oligodontia and agenesis are usually referred to as the loss of more than six teeth (excluding the third permanent molar) [1,2]. The absence of front teeth primarily affects aesthetics and mental health, while the loss of rear teeth impacts bone growth patterns and chewing function. Long-term loss of teeth even brings great inconvenience to restoration. As early as six decades ago, scholars pointed out that tooth loss was related to genetics, and more than 300 genes were involved in the development of teeth, including MSX1, PAX9, AXIN2, EDA, SPRY2, TGFA, SPRY4, WNT10A, FGF3, FGF10, FGFR2, and BMP4. These genetic systems determine the position, number, and shape of teeth. Notably, MSX1 and PAX9 exhibit higher mutation frequencies. The simplest and most common form of genome-level DNA variation between individuals is the substitution of one nucleotide for another, known as a single nucleotide polymorphism (SNP) [3]. The probability of allelic variation or mutation in individuals is about 0.1%. Paired box transcription factors (PAX) and their homologs, ubiquitous in vertebrates and invertebrates, play a key role in various stages of embryonic development. MSX1 is the first gene identified to be associated with human edentulous, and its regulatory role is critical in inducing nonsyndromic edentulous by interfering with DNA binding. It is known that PAX9 and MSX1 genes are not only related to the tooth development of the posterior teeth [4–10], but also play an indispensable role in craniofacial development and differentiation [11–14]. At the same time, studies of mouse tooth development have also shown a molecular relationship between PAX9 and MSX1, which are co-expressed in the tooth mesenchyme and appear to be critical for mouse tooth morphogenesis. Ogawa et al. [15] observed that in mice with simple co-mutations of MSX1 and PAX9, the expression of bone morphogenetic proteins (BMP4) was significantly reduced in the periodontal mesenchyme, and they also observed that in mice with homozygous ineffective mutants of MSX1 and PAX9, tooth development stalled at the bud stage. As an effector molecule, BMP4 is known to be involved in downstream signaling events that induce enamel junctions [16]. The enamel junction is a transient signaling center located in the epithelial depression of the central part of the tooth embryo during the cap stage. It controls the shape, height, position, and number of tooth cusps. Therefore, the enamel junction is called the regulatory center of tooth embryo morphogenesis and regulates the occurrence of tooth embryo morphogenesis. MSX1 and PAX9 are among the most extensively studied dental mesenchymal transcription factors, with over 50 mutations linked to various types of dental hypoplasia and other inherited dental defects or variations [17]. The SNPs that have been found to be most relevant to the expression level of BMP4 are the single thymine nucleotide insertion/deletion variants in PAX9 (T > C) and the single guanosine nucleotide insertion/deletion variants in MSX1 (G > A). These variants may play key regulatory roles in the early stages of tooth formation [18]. Mutations in PAX9 and MSX1 may lead to defects in protein–protein interactions that disrupt the expression of BMP4, a normal downstream function essential for tooth morphogenesis, resulting in defects in tooth development. Numerous case–control studies have reported the correlation between PAX9 rs2073247, PAX9 rs2073244, and MSX1 rs12532 gene polymorphisms and tooth agenesis. Therefore, we conducted this meta-analysis to further explore the association between these gene polymorphisms and tooth agenesis.

2 Materials and methods

2.1 Strategy for retrieving qualified articles in the database

The relationship between PAX9 rs2073247, PAX9 rs2073244 and MSX1 rs12532 gene polymorphisms and susceptibility to tooth agenesis was studied using Cochrane, PubMed, MEDLINE, WEB OF SCIENCE, and CNKI. The search terms and keywords were (1) “PAX9”/“MSX1 “(Mesh), (2) “SNPs” or “SNP” or “polymorphism” or “genetic” (Mesh), (3) “hypodontia” or “oligodontia” or “tooth agenesis” (Mesh). The search will end on April 1, 2024. In order to prevent the omission of relevant important articles, we conducted a careful manual search of all eligible articles, review articles, and other relevant studies and repeated them several times. Unpublished articles will not be discussed.

2.2 Selection criteria of eligible articles and quality assessment

Inclusion criteria included: (1) the association of at least one of the three gene polymorphisms (PAX9 rs2073247, PAX9 rs2073244, MSX1 rs12532) with the risk of tooth agenesis was evaluated; (2) the study must be a case–control study; (3) all patients should have been diagnosed with dental dysplasia through panoramic radiographs and thorough clinical examination; (4) alleles or gene frequencies for both case and control group must be extracted; and (5) published in English or Chinese.

Exclusion criteria included: (1) insufficient data to calculate odds ratio (OR) and 95% confidence interval (95% Cl) and (2) meta-analyses, reviews, letters, and editorials.

2.3 Data extraction

Two independent researchers (Xiaoyi Zhong) carefully selected relevant studies according to the inclusion criteria. Another researcher (Cuiping Li) examined the extraction of raw data. For those where there is still any debate, all the studies are discussed and consensus is reached. Data collected from each article included the first author’s last name, when the paper was published, race, country, source of control, and number of genotypes. Ethnic groups are often classified as Caucasian, Asian, Hungarian, and Black.

2.4 Data calculation

The correlations of PAX9 rs2073244, MSX1 rs12532, and PAX9 rs2073247 gene polymorphisms with the tooth loss risk were estimated using 95% CI and OR. The combined OR was evaluated using four genetic models, including homozygote comparison (GG/AA; TT/CC), heterozygote comparison (AG/AA; CT/CC), dominant (AG + GG/AA; CT + TT/CC), and recessive (GG/AA + AG; TT/CT + CC) models in PAX9 rs2073244, MSX1 rs12532, and PAX9 rs2073247. The I 2 and p-values were used to calculate and analyze the heterogeneity. Heterogeneity was calculated and analyzed by using I 2 statistics and p value. The random effects model was used when there were statistical differences found in heterogeneity (p < 0.05, I 2 > 50%), or else the fixed effects model was conducted (p > 0.05, I 2 < 50%).

The probability of false positive reports (FPRP) is calculated to evaluate meaningful findings. We set the threshold for FPRP at 0.2% and specified prior probabilities of 0.25, 0.1, 0.01, 0.001, and 0.0001 to detect an OR of 1.5 associated with cancer risk in the study. Only if the FPRP value is less than 0.2 is the result considered noteworthy [19]. A spreadsheet was calculated using Excel to estimate the probability of Bayesian Error Finding (BFDP) to assess the confidence of a statistically significant association. A BFDP below 0.8 is considered a noteworthy [20].

3 Results

3.1 Study characteristics

According to the search criteria (Figure 1), a total of 372 potentially relevant articles were screened out after eliminating duplicates through online database search. After reading the title and abstract of each article in detail, a total of 310 unrelated studies were removed. A total of 69 potential reports were collated against the reference criteria mentioned above. After reading the full text, 60 articles were deleted (51 articles without proper data + 9 reviews). Three articles were manually found from references and related literature. A total of 12 articles were retrieved, and the flow chart of the whole selection process is shown in Figure 1. There were seven studies of PAX9 rs2073247 (873 cases and 812 control samples) [4,21–26]. Five studies on PAX9 rs2073244 were conducted (668 cases and 668 control samples) [21,23–26]. Seven studies on MSX1 rs12532 were found (762 cases and 1,544 control samples) [4,23,27–31].

Figure 1 A flow chart of the entire selection document.
Figure 1

A flow chart of the entire selection document.

For PAX9 rs2073247 polymorphisms, four hospital-based studies and three population-based studies were among the seven case–control studies. There were three studies of Caucasians, two studies of Asians, and one study of Hungarian ethnicity. For PAX9 rs2073244 polymorphism, two of the five case–control studies were hospital-based and three were population-based. In terms of ethnicity, there were three studies of Caucasians, one study of Asians, and one study of Hungarians. Regarding MSX1 rs12532 polymorphism, seven of the seven case–control studies were hospital-based. In terms of ethnicity there were three studies on Caucasians, two on Asians, one on Hungarians, and one on Blacks. The main characteristics of PAX9 rs2073244, PAX9 rs2073247, and MSX1 rs12532 polymorphisms in the included studies are shown in Table 1.

Table 1

Main characteristics of the eligible studies for PAX9 rs2073244, PAX9 rs2073247, and MSX1 rs12532 polymorphisms

No. Author Year Ethnicity Country Disease type Source of control Sample size of case Sample size of control CaAA CaAG CaGG ConAA ConAG ConGG MAF Genotyping method
PAX9 rs2073244
1 Ahmed 2018 1 Jordan Non-syndromic hypodontia 2 72 72 27 35 10 43 28 1 0.36 PCR
2 Gabriella 2013 3 Hungary Non-syndromic hypodontia 1 192 260 54 106 32 98 121 41 0.44 PCR
3 Isman 2013 1 Turkey Non-syndromic hypodontia 2 200 114 15 109 76 12 57 45 0.4 PCR
4 Peres 2005 1 Brazil Non-syndromic hypodontia 2 102 106 36 56 10 56 47 3 0.37 PCR
5 Yong chu Pan 2008 2 China Non-syndromic hypodontia 1 102 116 27 46 29 33 55 28 0.5 PCR
Total             668 668
MSX1 rs12532   PCR
6 Clarissa 2020 1 Brazil Non-syndromic hypodontia 1 23 479 9 11 3 176 227 76 0.42 PCR
7 WANG H 2010 2 China Non-syndromic hypodontia 1 198 207 31 101 66 40 110 57 0.46 PCR
8 Yu-Jin Seo 2013 2 Korea Tooth agenesis 1 81 45 11 42 28 3 17 25 0.4 PCR
9 Gabriella 2013 3 Hungary Non-syndromic hypodontia 1 192 260 98 85 9 120 111 29   PCR
10 Azza H. Al-Ani 2021 4 New Zealand Non-syndromic hypodontia 1 61 297 27 24 10 134 142 21 0.38 PCR
11 Krisztina Mártha 2019 1 America Non-syndromic hypodontia 1 97 99 50 37 10 45 42 12 0.37 PCR
12 Melissa Lancia 2020 1 Brazil Non-syndromic hypodontia 1 110 157 43 52 15 75 61 21 0.39 PCR
Total             762 1544
PAX9 rs2073247 CaCC CaCT CaTT ConCC ConCT ConTT
3 Isman 2013 1 Turkey Non-syndromic hypodontia 2 200 114 19 98 83 14 53 47 0.4 PCR
1 Ahmed 2018 1 Jordan Non-syndromic hypodontia 2 72 72 3 1 68 3 24 45 0.18 PCR
4 Peres 2005 1 Brazil Non-syndromic hypodontia 2 102 106 43 46 13 60 43 3 0.35 PCR
4 Liu H 2012 2 China Non-syndromic hypodontia 1 124 99 32 57 35 36 48 15 0.47 PCR
2 Gabriella 2013 3 Hungary Non-syndromic hypodontia 1 192 260 51 111 30 98 121 41 0.44 PCR
8 Yu-Jin Seo 2013 2 Korea Tooth agenesis 1 81 45 18 44 19 15 14 16 0.49 PCR
5 Yong chu Pan 2008 2 China Non-syndromic hypodontia 1 102 116 27 40 35 26 57 33 0.48 PCR
Total             873 812

Abbreviations and numbers: Ethnicity: 1 = Caucasian, 2 = Asian, 3 = Magyarok, 4 = Black race. Source of control: 1 = HB, hospital-based; 2 = PB, population-based; MAF, minor allele frequency.

3.2 Quantitative synthesis

In the analysis encompassing five studies on PAX9 rs2073244 [21,23–26], seven studies on MSX1 rs12532 [4,23,27–31], and seven analyses on PAX9 rs2073247 [4,21–26], we found that partial models of PAX9 rs2073247 and PAX9 rs2073244 gene polymorphisms, along with race and origin, were associated with susceptibility to tooth loss.

Table 2 and Figure 2 record the overall OR and 95% CI of the relationship between PAX9 rs2073247 polymorphism and the risk of tooth loss. When analyzing the seven studies on PAX9 rs2073247 in all four models, we found that two model of the polymorphism increased the susceptibility to tooth loss (TT vs CC: OR (95% CI) = 1.556 (1.147–2.112), p = 0.005; CT + TT vs CC: OR (95% CI) = 1.479 (1.176–1.862), p = 0.001). In the stratified analysis by ethnicity, we found that the Hungarian ethnic group had increased susceptibility to tooth loss with PAX9 rs2073247 polymorphism (CT vs CC: OR (95% CI) = 1.763 (1.152–2.697), p = 0.009; CT + TT vs CC: OR (95% CI) = 1.672 (1.114–2.512), p = 0.013), while only one polymorphism model was associated with increased susceptibility to tooth loss in the Caucasian ethnic group (CT + TT vs CC: OR (95% CI) = 1.561 (1.023–2.381), p = 0.039). In the groups based on hospital origin and population origin, we found that the CT + TT vs CC model was associated with tooth loss in both (CT + TT vs CC: OR (95% CI) = 1.447 (1.100–1.903), p = 0.008; CT + TT vs CC: OR (95% CI) = 1.561 (1.023–2.381), p = 0.039).

Table 2

Stratified analyses of the PAX9 rs2073247 polymorphism on tooth agenesis risk

Comparative model No. Z p OR (95% CI) Heterogeneity Z Begg's test t Egger's test FPRP statistical power BEDP prior probability
Heterogeneity chi-squared p I 2 FPRP p-value FPRP statistical power 0.250 0.100 0.010 0.001 0.0001 0.010 0.001 1 × 1006
TT/CC
Overall 7 2.840 0.005 1.556 (1.147–2.112) 8.370 0.212 28.30% 0.600 0.548 0.900 0.408 0.005 0.407 0.033 0.092 0.526 0.918 0.991 0.898 0.989 1.000
Ethnicity
Caucasian 3 1.520 0.128 2.143 (0.802–5.721) 4.000 0.135 50.10% 0.000 1.000 0.640 0.636 0.128 0.238 0.617 0.829 0.982 0.998 1.000 0.987 0.999 1.000
Asian 3 1.540 0.123 1.429 (0.908–2.249) 3.810 0.149 47.40% 0.000 1.000 –0.210 0.870 0.123 0.583 0.387 0.655 0.954 0.995 1.000 0.989 0.999 1.000
Magyarok 1 1.150 0.249 1.406 (0.787–2.511) 0.000 . .% 0.249 0.587 0.561 0.793 0.977 0.998 1.000 0.992 0.999 1.000
Source of control
HB 4 1.930 0.054 1.420 (0.994–2.030) 3.810 0.283 21.20% −0.340 1.000 −0.150 0.895 0.054 0.618 0.209 0.442 0.897 0.989 0.999 0.984 0.998 1.000
PB 3 1.520 0.128 2.143 (0.802–5.721) 4.000 0.135 50.10% 0.000 1.000 0.640 0.636 0.987 0.999 1.000
CT/CC
Overall 7 1.200 0.231 1.293 (0.849–1.970 ) 14.750 0.022 59.30% 0.900 0.368 −1.760 0.139 0.232 0.755 0.479 0.734 0.968 0.997 1.000 0.993 0.999 1.000
Ethnicity
Caucasian 3 0.190 0.852 0.903 (0.312–2.616 ) 7.180 0.028 72.20% 1.040 0.296 −4.920 0.128 0.851 0.712 0.782 0.915 0.992 0.999 1.000 0.993 0.999 1.000
Asian 3 0.660 0.510 1.267 (0.626–2.565) 5.730 0.057 65.10% 0.000 1.000 0.780 0.579 0.511 0.681 0.692 0.871 0.987 0.999 1.000 0.994 0.999 1.000
Magyarok 1 2.610 0.009 1.763 (1.152–2.697) 0.000 . .% 0.009 0.228 0.105 0.261 0.795 0.975 0.997 0.934 0.993 1.000
Source of control
HB 4 1.330 0.184 1.397 (0.853–2.290) 7.430 0.059 59.60% −0.340 1.000 −0.190 0.869 0.185 0.611 0.476 0.731 0.968 0.997 1.000 0.991 0.999 1.000
PB 3 0.190 0.852 0.903 (0.312–2.616) 7.180 0.028 72.20% 1.040 0.296 −4.920 0.128 0.851 0.712 0.782 0.915 0.992 0.999 1.000 0.993 0.999 1.000
CT+TT/CC
Overall 7 3.340 0.001 1.479 (1.176−1.862) 5.140 0.525 0.00% 1.200 0.230 −0.810 0.453 0.001 0.548 0.005 0.014 0.135 0.612 0.940 0.704 0.960 1.000
Ethnicity
Caucasian 3 2.060 0.039 1.561 (1.023−2.381) 0.700 0.705 0.00% 1.040 0.296 −1.700 0.338 0.039 0.427 0.214 0.449 0.900 0.989 0.999 0.977 0.998 1.000
Asian 3 1.300 0.195 1.279 (0.882–1.855) 3.480 0.176 42.50% 0.000 1.000 0.250 0.843 0.195 0.800 0.422 0.687 0.960 0.996 1.000 0.993 0.999 1.000
Magyarok 1 2.480 0.013 1.672 (1.114–2.512) 0.000 . .% 0.013 0.301 0.117 0.285 0.814 0.978 0.998 0.951 0.995 1.000
Source of control
HB 4 2.650 0.008 1.447 (1.100–1.903) 4.360 0.225 31.20% 1.020 0.308 −0.420 0.717 0.008 0.602 0.039 0.109 0.574 0.932 0.993 0.938 0.994 1.000
PB 3 2.060 0.039 1.561 (1.023–2.381) 0.700 0.705 0.00% 1.040 0.296 −1.700 0.338 0.039 0.427 0.214 0.449 0.900 0.989 0.999
TT/CT+CC
Overall 7 1.740 0.081 1.630 (0.941–2.826) 26.520 0.000 77.40% 1.500 0.133 2.100 0.089 0.082 0.384 0.390 0.657 0.955 0.995 1.000 0.985 0.998 1.000
Ethnicity
Caucasian 3 1.520 0.128 3.487 (0.697–17.431) 17.690 0.000 88.70% 0.000 1.000 2.910 0.211 0.128 0.152 0.717 0.884 0.988 0.999 1.000 0.988 0.999 1.000
Asian 3 0.520 0.606 1.207 (0.590–2.470) 6.700 0.035 70.20% 0.000 1.000 –0.680 0.619 0.607 0.724 0.715 0.883 0.988 0.999 1.000 0.994 0.999 1.000
Magyarok 1 0.040 0.967 0.989 (0.592–1.652) 0.000 . .% 0.966 0.934 0.756 0.903 0.990 0.999 1.000 0.996 1.000 1.000
Source of control
HB 4 0.560 0.577 1.150 (0.704–1.877) 7.300 0.063 58.90% −0.340 1.000 −0.260 0.817 0.576 0.856 0.669 0.858 0.985 0.999 1.000 0.995 1.000 1.000
PB 3 1.520 0.128 3.487 (0.697–17.431) 17.690 0.000 88.70% 0.000 1.000 2.910 0.211 0.128 0.152 0.717 0.884 0.988 0.999 1.000 0.988 0.999 1.000

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

Figure 2 Forest plot of the statistical correlation between PAX9 rs2073247 polymorphism and tooth agenesis susceptibility in all models: (a) TT vs CC, (b) CT vs CC, (c) CT + TT vs CC, and (d) TT vs CT + CC. Data were pooled ORs with 95% CIs determined using random-effects models or fixed-effects models according to I 2 values.
Figure 2

Forest plot of the statistical correlation between PAX9 rs2073247 polymorphism and tooth agenesis susceptibility in all models: (a) TT vs CC, (b) CT vs CC, (c) CT + TT vs CC, and (d) TT vs CT + CC. Data were pooled ORs with 95% CIs determined using random-effects models or fixed-effects models according to I 2 values.

Table 3 and Figure 3 record the overall OR and 95% CI of the relationship between PAX9 rs2073244 polymorphism and the risk of tooth loss in general. When analyzing the five studies on PAX9 rs2073244 in all four models, we found that three of the models increased the susceptibility to tooth loss (GG vs AA: OR (95% CI) = 1.911 (1.046–3.493), p = 0.035; AG vs AA: OR (95% CI) = 1.569 (1.209–2.036), p = 0.001; AG + GG vs AA: OR (95% CI) = 1.637 (1.276–2.100), p = 0.000). In the AG vs AA and AG + GG vs AA models, we found that the risk of tooth loss was higher in the Caucasian race (AG vs AA: OR (95% CI) = 1.819 (1.234–2.679), p = 0.002; AG + GG vs AA: OR (95% CI) = 2.018 (1.387–2.938), p = 0.000) and the Hungarian race (AG vs AA: OR (95% CI) = 1.590 (1.042–2.425), p = 0.031; AG + GG vs AA: OR (95% CI) = 1.546 (1.034–2.312), p = 0.034). No model was found to be related to the risk of tooth loss in the hospital source. Two models were found to be related to the risk of tooth loss in the population source (AG vs AA: OR (95% CI) = 1.819 (1.234–2.679), p = 0.002; AG + GG vs AA: OR (95% CI) = 2.018 (1.387–2.938), p = 0.000).

Table 3

Stratified analyses of the PAX9 rs2073244 polymorphism on tooth agenesis risk

Comparative model No. Z p OR (95% CI) Heterogeneity Z Begg's test t Egger's test FPRP statistical power BEDP prior probability
Heterogeneity chi-squared p I 2 FPRP p-value FPRP statistical power 0.250 0.100 0.010 0.001 1 × 10−4 0.010 0.001 0.000001
GG/AA
Overall 5 2.110 0.035 1.911 (1.046–3.493) 8.320 0.080 51.90% 1.710 0.086 4.080 0.027 0.035 0.216 0.330 0.596 0.942 0.994 0.999 0.974 0.997 1.000
Ethnicity
Caucasian 3 1.880 0.061 3.808 (0.942–15.404) 6.290 0.043 68.20% 1.040 0.296 6.020 0.105 0.061 0.096 0.656 0.851 0.984 0.998 1.000 0.985 0.998 1.000
Asia 1 0.640 0.525 1.266 (0.612–2.619) 0.000 . . 0.525 0.676 0.700 0.875 0.987 0.999 1.000 0.994 0.999 1.000
Magyarok 1 1.200 0.231 1.416 (0.802–2.503) 0.000 . . 0.231 0.579 0.545 0.783 0.975 0.998 1.000 0.992 0.999 1.000
Source of control
HB 2 1.330 0.182 1.357 (0.867–2.124) 0.060 0.811 0.00% 0.000 1.000 . . 0.182 0.669 0.449 0.710 0.964 0.996 1.000 0.992 0.999 1.000
PB 3 1.880 0.061 3.808 (0.942–15.404) 6.290 0.043 68.20% 1.040 0.296 6.020 0.105 0.061 0.096 0.656 0.851 0.984 0.998 1.000 0.985 0.998 1.000
AG/AA
Overall 5 3.390 0.001 1.569 (1.209–2.036) 2.500 0.644 0.00% –0.240 1.000 –0.140 0.894 0.001 0.368 0.006 0.017 0.159 0.656 0.950 0.652 0.950 0.999
Ethnicity
Caucasian 3 3.030 0.002 1.819 (1.234–2.679) 0.240 0.887 0.00% 0.000 1.000 –0.830 0.559 0.002 0.164 0.043 0.118 0.596 0.937 0.993 0.834 0.981 1.000
Asia 1 0.070 0.946 1.022 (0.538–1.942) 0.000 . .% 0.947 0.879 0.764 0.906 0.991 0.999 1.000 0.995 0.999 1.000
Magyarok 1 2.150 0.031 1.590 (1.042–2.425) 0.000 . .% 0.031 0.393 0.193 0.417 0.887 0.988 0.999 0.973 0.997 1.000
Source of control
HB 2 1.840 0.065 1.392 (0.979–1.979) 1.270 0.260 21.20% 0.000 1.000 . . 0.065 0.661 0.229 0.471 0.907 0.990 0.999 0.986 0.999 1.000
PB 3 3.030 0.002 1.819 (1.234–2.679) 0.240 0.887 0.00% 0.000 1.000 –0.830 0.559 0.002 0.164 0.043 0.118 0.596 0.937 0.993 0.834 0.981 1.000
AG+GG/AA
Overall 5 3.880 0.000 1.637 (1.276–2.100) 3.920 0.417 0.00% 0.240 0.806 0.230 0.832 0.000 0.246 0.001 0.004 0.041 0.299 0.810 0.259 0.779 0.997
Ethnicity
Caucasian 3 3.670 0.000 2.018 (1.387–2.938) 1.010 0.603 0.00% 0.000 1.000 –0.680 0.620 0.000 0.061 0.012 0.035 0.288 0.803 0.976 0.429 0.884 0.999
Asia 1 0.330 0.744 1.104 (0.608–2.006) 0.000 . .% 0.745 0.843 0.726 0.888 0.989 0.999 1.000 0.995 1.000 1.000
Magyarok 1 2.120 0.034 1.546 (1.034–2.312) 0.000 . .% 0.034 0.442 0.187 0.408 0.884 0.987 0.999 0.975 0.998 1.000
Source of control
HB 2 1.950 0.051 1.393 (0.998–1.943) 0.840 0.360 0.00% 0.000 1.000 . . 0.051 0.669 0.186 0.407 0.883 0.987 0.999 0.984 0.998 1.000
PB 3 3.670 0.000 2.018 (1.387–2.938) 1.010 0.603 0.00% 0.000 1.000 –0.680 0.620 0.000 0.061 0.012 0.035 0.288 0.803 0.976 0.429 0.884 0.999
GG/AG+AA
Overall 5 1.250 0.211 1.365 (0.838–2.225) 8.700 0.069 54.00% 2.200 0.027 20.130 0.000 0.212 0.647 0.496 0.747 0.970 0.997 1.000 0.992 0.999 1.000
Ethnicity
Caucasian 3 1.320 0.186 2.674 (0.622–11.496) 8.620 0.013 76.80% 1.040 0.296 40.830 0.016 0.186 0.219 0.719 0.885 0.988 0.999 1.000 0.989 0.999 1.000
Asia 1 0.720 0.472 1.249 (0.682–2.286) 0.000 . .% 0.471 0.724 0.661 0.854 0.985 0.998 1.000 0.994 0.999 1.000
Magyarok 1 0.260 0.798 1.068 (0.645–1.771) 0.000 . .% 0.799 0.906 0.726 0.888 0.989 0.999 1.000 0.996 1.000 1.000
Source of control
HB 2 0.660 0.510 1.139 (0.773–1.678) 0.150 0.698 0.00% 0.000 1.000 . . 0.510 0.918 0.625 0.833 0.982 0.998 1.000 0.996 1.000 1.000
PB 3 1.320 0.186 2.674 (0.622–11.496) 8.620 0.013 76.80% 1.040 0.296 40.830 0.016 0.186 0.219 0.719 0.885 0.988 0.999 1.000 0.989 0.999 1.000

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

Figure 3 Forest plot of the statistical correlation between PAX9 rs2073244 polymorphism and tooth agenesis susceptibility in all models: (a) GG vs AA, (b) AG vs AA, (c) AG + GG vs AA, and (d) GG vs AA + AG. Data were pooled ORs with 95% CIs determined using random-effects models or fixed-effects models according to I 2 value.
Figure 3

Forest plot of the statistical correlation between PAX9 rs2073244 polymorphism and tooth agenesis susceptibility in all models: (a) GG vs AA, (b) AG vs AA, (c) AG + GG vs AA, and (d) GG vs AA + AG. Data were pooled ORs with 95% CIs determined using random-effects models or fixed-effects models according to I 2 value.

Table 4 and Figure 4 record the overall OR and 95% CIs for the association of MSX1 rs12532 polymorphism with tooth loss risk. In the analysis of seven studies on MSX1 rs12532 in all four models, we did not find any model associated with increased sensitivity to tooth loss, but in the analysis based on race, we found that Hungarian race was associated with increased tooth loss risk in two models (GG vs AA: OR (95% CI) = 0.380 (0.172–0.841), p = 0.017; GG vs AG + AA: OR (95% CI) = 0.392 (0.181–0.848), p = 0.017). Only one model was found to be associated with tooth loss in Black race (GG vs AG + AA: OR (95% CI) = 2.577 (1.146–5.794), p = 0.022).

Table 4

Stratified analyses of the MSX1 rs12532 polymorphism on tooth agenesis risk

Comparative model No. Z p OR (95% CI) Heterogeneity Z Begg's test t Egger's test FPRP statistical power BEDP prior probability
Heterogeneity chi-squared p I 2 FPRP p-value FPRP statistical power 0.250 0.100 0.010 0.001 0.0001 0.010 0.001 0.000001
GG/AA    
Overall 0.320 0.746 0.916 (0.538–1.560) 15.080 0.020 60.20% 0.900 0.368 −1.230 0.275 0.747 0.879 0.718 0.884 0.988 0.999 1.000 0.995 1.000 1.000
Ethnicity    
Caucasian 0.120 0.906 0.968 (0.566–1.656) 0.820 0.663 0.00% 0.000 1.000 –1.040 0.486 0.905 0.913 0.748 0.899 0.990 0.999 1.000 0.996 1.000 1.000
Asian 0.340 0.737 0.768 (0.165–3.580) 4.290 0.038 76.70% 0.000 1.000 . . 0.737 0.571 0.795 0.921 0.992 0.999 1.000 0.992 0.999 1.000
Magyarok 2.390 0.017 0.380 (0.172–0.841) 0.000 . .%   0.017 0.083 0.381 0.649 0.953 0.995 1.000 0.963 0.996 1.000
Black race 1.960 0.050 2.363 (1.001–5.580) 0.000 . .%   0.050 0.150 0.499 0.749 0.970 0.997 1.000 0.980 0.998 1.000
Source of control    
HB 0.320 0.746 0.916 (0.538–1.560) 15.080 0.020 60.20% 0.900 0.368 –1.230 0.275 0.747 0.879 0.718 0.884 0.988 0.999 1.000 0.995 1.000 1.000
PB    
   
AG/AA    
Overall 0.070 0.943 1.008 (0.811–1.253) 3.890 0.692 0.00% 0.600 0.548 –0.600 0.575 0.943 1.000 0.739 0.895 0.989 0.999 1.000 0.998 1.000 1.000
Ethnicity    
Caucasian 0.510 0.610 1.098 (0.766–1.575) 2.510 0.284 20.50% 0.000 1.000 –0.510 0.699 0.612 0.955 0.658 0.852 0.984 0.998 1.000 0.996 1.000 1.000
Asian 0.360 0.721 1.095 (0.664–1.807) 0.550 0.460 0.00% 0.000 1.000 . . 0.723 0.891 0.709 0.879 0.988 0.999 1.000 0.996 1.000 1.000
Magyarok 0.320 0.745 0.938 (0.636–1.383) 0.000 . .%   0.747 0.958 0.701 0.875 0.987 0.999 1.000 0.996 1.000 1.000
Black race 0.580 0.565 0.839 (0.461–1.526) 0.000 . .%   0.565 0.774 0.686 0.868 0.986 0.999 1.000 0.995 0.999 1.000
Source of control    
HB 0.070 0.943 1.008 (0.811–1.253 ) 3.890 0.692 0.00% 0.600 0.548 –0.600 0.575 0.943 1.000 0.739 0.895 0.989 0.999 1.000 0.998 1.000 1.000
PB    
GG+AG/AA    
Overall 0.120 0.904 0.987 (0.804–1.213) 6.100 0.413 1.60% 0.900 0.368 –0.600 0.576 0.901 1.000 0.730 0.890 0.989 0.999 1.000 0.998 1.000 1.000
Ethnicity    
Caucasian 0.360 0.716 1.065 (0.759–1.495) 2.620 0.269 23.80% 0.000 1.000 –0.540 0.683 0.716 0.976 0.688 0.868 0.986 0.999 1.000 0.997 1.000 1.000
Asian 0.160 0.871 0.924 (0.355–2.402) 2.050 0.152 51.30% 0.000 1.000 . . 0.871 0.748 0.777 0.913 0.991 0.999 1.000 0.993 0.999 1.000
Magyarok 1.030 0.304 0.822 (0.566–1.194) 0.000 . .%   0.303 0.864 0.513 0.760 0.972 0.997 1.000 0.995 0.999 1.000
Black race 0.120 0.903 1.035 (0.595–1.802) 0.000 . .%   0.903 0.905 0.750 0.900 0.990 0.999 1.000 0.995 1.000 1.000
Source of control    
HB 0.120 0.904 0.987 (0.804–1.213) 6.100 0.413 1.60% 0.900 0.368 –0.600 0.576 0.901 1.000 0.730 0.890 0.989 0.999 1.000 0.998 1.000 1.000
PB    
   
GG/AG+AA    
Overall 0.480 0.632 0.888 (0.544–1.447) 17.930 0.006 66.50% 0.300 0.764 –0.790 0.465 0.633 0.875 0.685 0.867 0.986 0.999 1.000 0.996 1.000 1.000
Ethnicity    
Caucasian 0.340 0.732 0.915 (0.552–1.518) 0.180 0.912 0.00% 0.000 1.000 –1.730 0.333 0.731 0.890 0.711 0.881 0.988 0.999 1.000 0.996 1.000 1.000
Asian 0.440 0.658 0.778 (0.257–2.362) 6.730 0.009 85.20% 0.000 1.000 . . 0.658 0.607 0.765 0.907 0.991 0.999 1.000 0.993 0.999 1.000
Magyarok 2.380 0.017 0.392 (0.181–0.848) 0.000 . .%   0.017 0.089 0.370 0.638 0.951 0.995 0.999 0.963 0.996 1.000
Black race 2.290 0.022 2.577 (1.146–5.794) 0.000 . .%   0.022 0.095 0.410 0.675 0.958 0.996 1.000 0.968 0.997 1.000
Source of control    
HB 0.480 0.632 0.888 (0.544–1.447) 17.930 0.006 66.50% 0.300 0.764 –0.790 0.465 0.633 0.875 0.685 0.867 0.986 0.999 1.000 0.996 1.000 1.000
PB                                          

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

Figure 4 Forest plot of the statistical correlation between MSX1 rs12532 polymorphism and tooth agenesis susceptibility in all models: (a) GG vs AA, (b) AG vs AA, (c) AG + GG vs AA, and (d) GG vs AA + AG. Data were pooled ORs with 95% CIs determined using random-effects models or fixed-effects models according to I 2 values.
Figure 4

Forest plot of the statistical correlation between MSX1 rs12532 polymorphism and tooth agenesis susceptibility in all models: (a) GG vs AA, (b) AG vs AA, (c) AG + GG vs AA, and (d) GG vs AA + AG. Data were pooled ORs with 95% CIs determined using random-effects models or fixed-effects models according to I 2 values.

3.3 Publication bias

Begg’s test and Egger’s test were used to assess potential publication bias. Partial results indicated publication bias in the two models of PAX9 rs2073244 (GG vs AA: p = 0.027) and (GG vs AG + AA: p = 0.000) (Table 3 and Figure 5). However, polymorphic results of PAX9 rs2073247 and MSX1 rs12532 showed no evidence of publication bias (Table 2, Figure 6 and Table 4, Figure 7).

Figure 5 Begg’s funnel plot assessed the risk of PAX9 rs2073244 publication bias in the meta-analysis: (a) GG vs AA, (b) AG vs AA, (c) GG + AG vs AA, and (d) GG vs AG + AA.
Figure 5

Begg’s funnel plot assessed the risk of PAX9 rs2073244 publication bias in the meta-analysis: (a) GG vs AA, (b) AG vs AA, (c) GG + AG vs AA, and (d) GG vs AG + AA.

Figure 6 Begg’s funnel plot assessed the risk of PAX9 rs2073247 publication bias in the meta-analysis: (a) TT vs CC, (b) CT vs CC, (c) CT + TT vs CC, and (d) TT vs CT + CC.
Figure 6

Begg’s funnel plot assessed the risk of PAX9 rs2073247 publication bias in the meta-analysis: (a) TT vs CC, (b) CT vs CC, (c) CT + TT vs CC, and (d) TT vs CT + CC.

Figure 7 Begg’s funnel plot assessed the risk of MSX1 rs12532 publication bias in the meta-analysis: (a) GG vs AA, (b) AG vs AA, (c) GG + AG vs AA, and (d) GG vs AG + AA.
Figure 7

Begg’s funnel plot assessed the risk of MSX1 rs12532 publication bias in the meta-analysis: (a) GG vs AA, (b) AG vs AA, (c) GG + AG vs AA, and (d) GG vs AG + AA.

3.4 Sensitivity analysis

We did not find any change in the results of PAX9 rs2073247, PAX9 rs2073244, and MSX1 rs12532 in the sensitivity analysis comparison. This suggests that the results of PAX9 rs2073247, PAX9 rs2073244, and MSX1 rs12532 were statistically stable and reliable. Meta-regression analysis found that the relationship among publication time, race, and source of control did not affect the stability of the comprehensive results (Figures 8–10).

Figure 8 Sensitivity analysis of the association between polymorphism of PAX9 rs2073247 and risk of tooth agenesis: (a) TT vs CC, (b) CT vs CC, (c) CT + TT vs CC, and (d) TT vs CT + CC.
Figure 8

Sensitivity analysis of the association between polymorphism of PAX9 rs2073247 and risk of tooth agenesis: (a) TT vs CC, (b) CT vs CC, (c) CT + TT vs CC, and (d) TT vs CT + CC.

Figure 9 Sensitivity analysis of the association between polymorphism of PAX9 rs2073244 and risk of tooth agenesis: (a) GG vs AA, (b) AG vs AA, (c) GG + AG vs AA, and (d) GG vs AG + AA.
Figure 9

Sensitivity analysis of the association between polymorphism of PAX9 rs2073244 and risk of tooth agenesis: (a) GG vs AA, (b) AG vs AA, (c) GG + AG vs AA, and (d) GG vs AG + AA.

Figure 10 Sensitivity analysis of the association between polymorphism of MSX1 rs12532 and risk of tooth agenesis: (a) GG vs AA, (b) AG vs AA, (c) GG + AG vs AA, and (d) GG vs AG + AA.
Figure 10

Sensitivity analysis of the association between polymorphism of MSX1 rs12532 and risk of tooth agenesis: (a) GG vs AA, (b) AG vs AA, (c) GG + AG vs AA, and (d) GG vs AG + AA.

3.5 FPRP and BFDP test

The FPRP values of statistical power for significant findings about the PAX9 rs2073247, PAX9 rs2073244, and MSX1 rs12532 polymorphism are shown in Tables 2–4. In Table 2, based on FPRP analyses, almost all models of PAX9 rs2073247 polymorphism were found to be not significant by FPRP estimation at the OR of 1.5 with the prior probability of 0.25 and 0.1, and were not significant by BFDP test at the OR of 1.5 with the prior probability of 0.01, 0.001, and 0.000001. In Table 3, FPRP analyses revealed that half of the models of PAX9 rs2073244 polymorphism were found to be noteworthy at an OR of 1.5 with the prior probability of 0.25 and 0.1. However, most of PAX9 rs2073244 polymorphism models did not meet the criteria for significance according to BFDP test at OR of 1.5 with the prior probability of 0.01, 0.001, and 0.000001. In Table 4, half of the models of MSX1 rs12532 polymorphism were found to be noteworthy by FPRP estimation at an OR of 1.5 with the prior probability of 0.25 and 0.1, but were not noteworthy by BFDP test at an OR of 1.5 with the prior probability of 0.01, 0.001, and 0.000001.

4 Discussion

Tooth loss is a significant oral health issue that can affect individuals of all ages. It can result from various factors such as dental decay, periodontal disease, trauma, or developmental disorders. These factors include trauma-induced injury to the tooth bud, viral infections, and maternal exposure to rubella or chemical drugs during pregnancy [32]. However, with the deepening understanding of molecular genetics, numerous studies have indicated a significant genetic component to tooth loss. The PAX gene family is named for its conserved paired domains. Based on the composition of the domains and the homology of the sequences, the gene family comprises four subfamilies: PAX1/9 (PAX1, PAX9), PAX2/5/8 (PAX2, PAX5, PAX8), PAX3/7 (PAX3, PAX7), and PAX4/6 (PAX4, PAX6). Among these, PAX9 is crucial in regulating the development of the maxillofacial region, tail, limb buds, esophagus, teeth, and pharynx, playing an upstream regulatory role in tooth development. Studies have suggested that mutations in PAX9 and MSX1 during the bud stage can prevent the mesenchymal cells from properly aggregating around the epithelial tooth bud, leading to impaired tooth development [33,34]. Meanwhile, in vitro biochemical assays have shown that MSX1 and PAX9 cooperate to activate the BMP4 gene promoter, and the BMP4 signal in mesenchymal cells is involved in the induction of the enamel knot, a transient signaling center in epithelial tissue that guides the next stage of tooth development. Therefore, it is hypothesized that polymorphisms in PAX9 and MSX1 may increase the risk of tooth agenesis in individuals. A review of the literature revealed that two similar studies have been previously published [35,36]. Compared to these studies, our research includes the following additions: (1) several articles published post-2014 have been incorporated and (2) compared and analyzed the subgroups of ethnic origin and control source. For this study, the results of judging the correlation between PAX9, MSX1 and tooth loss risk through comprehensive analysis of all eligible case–control studies are controversial.

In the five studies on PAX9 rs2073244, seven studies on MSX1 rs12532, and seven studies on PAX9 rs2073247, we found that PAX9 rs2073244 and PAX9 rs2073247 were statistically associated with the risk of tooth loss. The G allele of PAX9 rs2073244 and G carriers (AG + GG) may increase the risk of tooth loss and may be a risk factor for tooth loss. Similarly, the T allele of PAX9 rs2073247 and T carriers (CT + TT) were positively associated with the risk of tooth loss, but we did not observe an association between MSX1 rs12532 and tooth loss.

In our current meta-analysis, focusing on ethnic level analysis of PAX9 rs2073244 and PAX9 rs2073247, we observed an increases risk of tooth loss among G carriers of PAX9 rs2073244 in Caucasian and Hungarian populations, as well as a heightened risk of tooth loss among T carriers of PAX9 rs2073247 in the same population. The risk of tooth loss increases in both populations in the same model, which may be due to the following reasons: (1) PAX9 rs2073247 and PAX9 rs2073244 are both polymorphic mutation sites of PAX9, which mainly functions in tooth development, and the mutated sites are mainly related to the position and number of tooth loss [37]; (2) the Caucasian population, being widely distributed, has historically undergone genetic intermixture with the early Hungarian population [38], so PAX9 mutations may have similar regulatory effects on the development of tooth germ in these two populations; (3) some objective factors, such as sample size, selection bias, and inclusion or exclusion criteria, may also have some influence. Therefore, confirming this phenomenon requires a large-scale population study and multiple analyses to further prove the relationship between PAX9 rs2073244, PAX9 rs2073247 gene polymorphisms and the risk of tooth loss.

In this meta-analysis, several limitations should be acknowledged. First, despite comprehensive database searches, the body of research concerning oral tooth loss remains limited, with sample sizes still relatively small, particularly regarding PAX9 rs2073244 and MSX1 rs12532. Second, mutations at different sites of PAX9 can cause dental agenesis with different manifestations, including the location and number of missing teeth, as well as other conditions such as maxillofacial deformities and cancer. Variations in diagnosis by different clinicians may lead to heterogeneity. For example, whether to include the loss of the third molars into the scope of tooth loss, corresponding subgroup analysis can be conducted for further study in order to eliminate the heterogeneity caused by clinical diagnostic criteria. Moreover, the control source for MSX1 rs12532 is relatively singular precluding that cross-sectional comparisons between control sources cannot be achieved. Finally, MSX1 can interact with PAX9 to regulate tooth development [39], and research on MSX1 and PAX9 may provide a deeper understanding of the impact of genetic polymorphisms on the risk of tooth loss.

5 Conclusion

Understanding the mechanisms of gene mutations will enable clinicians and human geneticists to open up new lines of thought for alternative treatment research in the future. Gene polymorphisms of PAX9 rs2073247 (TT vs CC, CT + TT vs CC) and PAX9 rs2073244 (GG vs AA, AG vs AA, AG + GG vs AA) may increase the risk of non-syndromic hypodontia, and the susceptibility is more obvious in Caucasians and Hungarians. However, no statistically significant association was found between MSX1 rs12532 and the risk of tooth loss. PAX9 polymorphisms might be involved in the pathogenesis of tooth loss. Due to the limited number of studies published in this field, the available evidence is still limited, so we emphasize the need for large studies with sufficient case numbers and appropriate control of confounding factors to yield robust findings.

# These authors contributed equally to this work.

  1. Funding information: This study was supported by the Guangxi Natural Science Foundation (2024GXNSFAA010369) and the Youth Science Foundation of Guangxi Medical University (GXMUYSF202358).

  2. Author contributions: The authors thank all the participants for their contributions to this study. Kaixin Liu and Zhenmin Liu collected the data; Xiaoyi Zhong and Cuiping Li checked the data; Xiaoyi Zhong and Kaixin Liu calculated the data; Xiaoyi Zhong, Kaixin Liu, Zhenmin Liu, and Wenxia Chen 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: Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Received: 2024-07-01
Revised: 2024-09-15
Accepted: 2024-09-23
Published Online: 2025-04-10

© 2025 the author(s), published by De Gruyter

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

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https://doi.org/10.1515/biol-2022-0987
Keywords for this article
tooth agenesis; gene polymorphism; PAX9; MSX1; meta-analysis
Creative Commons
BY 4.0

Articles in the same Issue

  1. Biomedical Sciences
  2. Mechanism of triptolide regulating proliferation and apoptosis of hepatoma cells by inhibiting JAK/STAT pathway
  3. Maslinic acid improves mitochondrial function and inhibits oxidative stress and autophagy in human gastric smooth muscle cells
  4. Comparative analysis of inflammatory biomarkers for the diagnosis of neonatal sepsis: IL-6, IL-8, SAA, CRP, and PCT
  5. Post-pandemic insights on COVID-19 and premature ovarian insufficiency
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  15. SIK1 inhibits IL-1β-stimulated cartilage apoptosis and inflammation in vitro through the CRTC2/CREB1 signaling
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  50. Interaction of MTHFR polymorphism with PAX1 methylation in cervical cancer
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  54. Small cell lung cancer with small intestinal metastasis: Case report and literature review
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  107. Experimental study on salmon demineralized bone matrix loaded with recombinant human bone morphogenetic protein-2: In vitro and in vivo study
  108. A case of IgA nephropathy treated with a combination of telitacicept and half-dose glucocorticoids
  109. Analgesic and toxicological evaluation of cannabidiol-rich Moroccan Cannabis sativa L. (Khardala variety) extract: Evidence from an in vivo and in silico study
  110. Wound healing and signaling pathways
  111. Combination of immunotherapy and whole-brain radiotherapy on prognosis of patients with multiple brain metastases: A retrospective cohort study
  112. To explore the relationship between endometrial hyperemia and polycystic ovary syndrome
  113. Ecology and Environmental Science
  114. Optimization and comparative study of Bacillus consortia for cellulolytic potential and cellulase enzyme activity
  115. The complete mitochondrial genome analysis of Haemaphysalis hystricis Supino, 1897 (Ixodida: Ixodidae) and its phylogenetic implications
  116. Epidemiological characteristics and risk factors analysis of multidrug-resistant tuberculosis among tuberculosis population in Huzhou City, Eastern China
  117. Indices of human impacts on landscapes: How do they reflect the proportions of natural habitats?
  118. Genetic analysis of the Siberian flying squirrel population in the northern Changbai Mountains, Northeast China: Insights into population status and conservation
  119. Diversity and environmental drivers of Suillus communities in Pinus sylvestris var. mongolica forests of Inner Mongolia
  120. Agriculture
  121. Integrated analysis of transcriptome, sRNAome, and degradome involved in the drought-response of maize Zhengdan958
  122. Variation in flower frost tolerance among seven apple cultivars and transcriptome response patterns in two contrastingly frost-tolerant selected cultivars
  123. Heritability of durable resistance to stripe rust in bread wheat (Triticum aestivum L.)
  124. Animal Science
  125. Effect of sex ratio on the life history traits of an important invasive species, Spodoptera frugiperda
  126. Plant Sciences
  127. Hairpin in a haystack: In silico identification and characterization of plant-conserved microRNA in Rafflesiaceae
  128. Widely targeted metabolomics of different tissues in Rubus corchorifolius
  129. The complete chloroplast genome of Gerbera piloselloides (L.) Cass., 1820 (Carduoideae, Asteraceae) and its phylogenetic analysis
  130. Field trial to correlate mineral solubilization activity of Pseudomonas aeruginosa and biochemical content of groundnut plants
  131. Correlation analysis between semen routine parameters and sperm DNA fragmentation index in patients with semen non-liquefaction: A retrospective study
  132. Plasticity of the anatomical traits of Rhododendron L. (Ericaceae) leaves and its implications in adaptation to the plateau environment
  133. Effects of Piriformospora indica and arbuscular mycorrhizal fungus on growth and physiology of Moringa oleifera under low-temperature stress
  134. Effects of different sources of potassium fertiliser on yield, fruit quality and nutrient absorption in “Harward” kiwifruit (Actinidia deliciosa)
  135. Comparative efficiency and residue levels of spraying programs against powdery mildew in grape varieties
  136. The DREB7 transcription factor enhances salt tolerance in soybean plants under salt stress
  137. Food Science
  138. Phytochemical analysis of Stachys iva: Discovering the optimal extract conditions and its bioactive compounds
  139. Review on role of honey in disease prevention and treatment through modulation of biological activities
  140. Computational analysis of polymorphic residues in maltose and maltotriose transporters of a wild Saccharomyces cerevisiae strain
  141. Optimization of phenolic compound extraction from Tunisian squash by-products: A sustainable approach for antioxidant and antibacterial applications
  142. Liupao tea aqueous extract alleviates dextran sulfate sodium-induced ulcerative colitis in rats by modulating the gut microbiota
  143. Toxicological qualities and detoxification trends of fruit by-products for valorization: A review
  144. Polyphenolic spectrum of cornelian cherry fruits and their health-promoting effect
  145. Optimizing the encapsulation of the refined extract of squash peels for functional food applications: A sustainable approach to reduce food waste
  146. Advancements in curcuminoid formulations: An update on bioavailability enhancement strategies curcuminoid bioavailability and formulations
  147. Impact of saline sprouting on antioxidant properties and bioactive compounds in chia seeds
  148. The dilemma of food genetics and improvement
  149. Bioengineering and Biotechnology
  150. Impact of hyaluronic acid-modified hafnium metalorganic frameworks containing rhynchophylline on Alzheimer’s disease
  151. Emerging patterns in nanoparticle-based therapeutic approaches for rheumatoid arthritis: A comprehensive bibliometric and visual analysis spanning two decades
  152. Application of CRISPR/Cas gene editing for infectious disease control in poultry
  153. Preparation of hafnium nitride-coated titanium implants by magnetron sputtering technology and evaluation of their antibacterial properties and biocompatibility
  154. Preparation and characterization of lemongrass oil nanoemulsion: Antimicrobial, antibiofilm, antioxidant, and anticancer activities
  155. Corrigendum
  156. Corrigendum to “Utilization of convolutional neural networks to analyze microscopic images for high-throughput screening of mesenchymal stem cells”
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