Abstract
Purpose
Previous research on the correlation between methionine synthase (MTR) and methionine synthase reductase (MTRR) gene polymorphisms and the susceptibility to glioma has yielded varied results. This study aims to elucidate the potential impact of MTR and MTRR polymorphisms as contributing factors in the development of glioma.
Patients and methods
A comprehensive review of the relevant literature was conducted across several major databases, encompassing records from their inception through April 2025. The data were then synthesized using meta-analysis techniques.
Results
No significant associations between the MTR rs1805087 polymorphism and glioma risk were identified under any genetic model across all populations (all p > 0.05). However, we found that MTRR rs1801394 polymorphism was significantly associated with the glioma risk for Asian population (all p < 0.05).
Conclusion
In conclusion, our study indicates that the MTRR rs1801394 polymorphism serves as a protective factor in India populations but does not affect glioma risk in Caucasian population and Chinese population, which can be used as biomarkers for predicting glioma risk and can serve as targets for personalized treatments of glioma.
Supplementary Information
The online version contains supplementary material available at 10.1007/s12672-025-03471-6.
Keywords: Glioma, MTR, MTRR, Gene polymorphism, Meta-analysis
Introduction
Glioma is the most common primary intracranial tumor in adults, accounting for approximately 80% of primary CNS malignancies [1–6]. It is a malignant tumor of the central nervous system originating from glial cells [7–10], characterized by its heterogeneity, invasiveness, and poor prognosis [11, 12]. However, in recent decades, the incidence of malignant brain tumors in the Chinese population has risen rapidly [13]. Based on the China Health Statistical Yearbook 2009, the annual mortality rate for glioma in 2004 and 2005 was approximately 3.13 per 100,000 [14]. Additionally, glioblastoma multiforme, the most common and most malignant astrocytoma, has a median survival of only 12–15 months under current treatment standards [15]. The results of the epidemiological investigation show that the incidence rate of glioma accounts for approximately half of all primary brain tumors, and the incidence rate have been showing a gradually increasing trend in recent years [16, 17]. Currently, glioblastoma is mainly treated through surgical operations, radiotherapy and chemotherapy. However, clinical data shows that the average survival time for patients with glioblastoma is only 14 months [18]. Many environmental and lifestyle factors are strongly associated with glioma susceptibility, including occupation, ionizing radiation, cell phone radiation, smoking, and diet [19]. However, not all individuals exposed to high doses of ionizing radiation and other glioma risk factors develop the disease, suggesting that genetic factors may act a more significant role for gliomas.
Genome-wide association studies (GWAS) have identified that abnormalities in certain gene loci are associated with glioma, such as 5p13.33 (TERT), 8q24.21 (CCDC26), 9p21.3 (CDKN2A/B), 11q23.3 (PHLDB1), and 20q13.33 (RTEL1) [20–22]. Disruptions in biological processes such as DNA repair, apoptosis, inflammation, and folic acid metabolism can contribute to the development of glioma. Additionally, gene mutations in promoter or coding regions can lead to abnormal expression or methylation of related proteins, thereby influencing the onset of glioma.
Folic acid is an essential nutrient for the human body, primarily functioning as a methyl donor in intracellular methylation and DNA synthesis. Studies have shown that folic acid deficiency can contribute to carcinogenic processes by interfering with DNA methylation and biosynthesis. Folic acid must be activated through a series of physiological processes before it can be utilized by the body. The level of active folate is influenced by factors such as individual folate intake, other B vitamins, alcohol consumption, smoking, and notably, key enzymes involved in folate metabolism, including methionine synthase (MTR), methionine synthase reductase (MTRR) [23–28]. Polymorphisms in genes encoding these folate metabolism-related enzymes can lead to reduced enzyme activity, affecting normal folate metabolism, intracellular DNA methylation, and the biosynthesis of deoxynucleotide triphosphates, thereby influencing the development of tumors, including glioma. Among these enzymes, MTR and MTRR are particularly important. Several investigations have been conducted to explore the topic, but their results have been inconsistent. Therefore, the current study aims to explore this relationship with a larger sample size through meta-analysis.
Materials and methods
Literature retrieval
Major databases, including PubMed and Embase were searched using the keywords “MTR,” “MTRR,” “polymorphism,” and “glioma.” This search was supplemented by manually collecting research literature on the relationship between MTR or MTRR gene polymorphisms and glioma risk published from January 1991 to April 2025.
Inclusion criteria and exclusion criteria
This meta-analysis includes only independent case-control studies. Other types of research, such as reviews, pathology reports, bioinformatics analyses, and web pharmacology papers, were excluded. The literature data needed to be complete, or the data required for the analysis should be inferable from the reported results. If results from different populations were reported in the same article, each population was included in the analysis as a separate study.
Data extraction and quality assessment
The first author and second author independently collected the data, resolving any disagreements through discussion. The data extracted included the first author, journal name, date of publication, country, ethnic origin, study design, experimental method, genotype data, and NOS score. The detailed procedure followed those established in previous meta-analyses [29–32].
Statistical analysis
The OR value and 95% confidence interval were used to measure the effect of each study result. Genetic polymorphism was assessed using five different genetic models: recessive, dominant, allelic, homozygous, and heterozygous. Heterogeneity between studies was examined using the Q statistic, with a significance level of 0.1 due to low statistical power. If there was no significant heterogeneity between studies, the fixed effects model was used to combine the OR values. If there was significant heterogeneity, a random effects model was employed. The I² statistic was further used to test the heterogeneity between the studies. The size of the Q value depends on the pooled variance, the degree of dispersion of the effect values, and the number of references included in the study. Since the number of studies varies, the I² statistic corrects for the influence of the number of studies on the Q value using degrees of freedom. Therefore, the heterogeneity test results revealed by I² statistics are more robust and reliable, especially for stratified analyses with a small number of studies. When I² = 0, it indicates that the variation between studies is caused only by sampling error. When 0 < I² < 25%, it is considered to have mild heterogeneity; when 25 < I² < 50%, it is considered to have moderate heterogeneity; when I² >50%, it is considered to have high heterogeneity. In addition, meta-regression analysis was used to investigate the possible heterogeneity of three factors: population, study design, and tumor type. A random linear weighted meta-regression method was conducted with the OR value of each study as the response variable and the corresponding population, study design, and tumor type as the independent variables to investigate the effects of these factors on the OR value, thereby revealing possible sources of heterogeneity. Meta-regression uses the restricted maximum likelihood method to estimate the weight coefficient, integrating differences within and between studies. The funnel plot was used to assess publication bias. A scatter plot is made with the standard error of Log (OR) as the abscissa and Log (OR) as the ordinate. If the distribution of the scatter plot is symmetrically distributed around a vertical line combining OR values, there is no publication bias; otherwise, there is publication bias. The t-test proposed by Egger was used to evaluate the symmetry of the funnel plot quantitatively. The standard deviation of the Log (OR) for each study is regressed against the accuracy of the corresponding Log (OR), and the size of the longitudinal intercept of the linear regression represents the degree of asymmetry of the funnel plot. If the regression line passes through the origin, meaning the longitudinal intercept is 0, the funnel plot is symmetric. If the intercept deviates from the origin, the funnel plot is more asymmetric, indicating publication bias. STATA was used for all statistical analyses. These analyses included OR value merging, Q statistics, I² statistics, subgroup analysis, regression analysis, and funnel plots, all performed using STATA 15.0.
Results
Literature search
A literature search identified three studies examining the relationship between the MTR rs1805087 polymorphism and glioma susceptibility [33–35]. One European paper included five separate studies from different regions, resulting in seven studies enrolled in the current meta-analysis for MTR rs1805087 polymorphism. Additionally, two studies on the relationship between MTRR rs1801394 polymorphism and glioma susceptibility were collected [30, 31]. For similar reasons, six studies were included for the MTRR rs1801394 polymorphism analysis. The search process is depicted in Fig. 1, and the details of each independent study are depicted in Table 1. The quality assessment of Newcastle-Ottawa Scale is depicted in Table 2.
Fig. 1.
Flow diagram of study selection for the MTR and MTRR polymorphism and glioma risk
Table 1.
General information of eligible studies enrolled in the meta-analysis
| Literature | Ethnics | Genotyping method | Control origin | Sample capacity | Matching standard | HWE conformity | NOS |
|---|---|---|---|---|---|---|---|
| Kumawat (2018-India) | Asian | ARMS-PCR | HB | 108/104 | Age, sex | Yes | 8 |
| Bethke (2008-UK-Nouth) | Caucasian | Illumina GoldenGate Arrays | HB | 370/368 | Age, sex, ethnicity | Yes | 9 |
| Bethke(2008-UK-Southeast) | Caucasian | Illumina GoldenGate Arrays | HB | 211/214 | Age, sex, ethnicity | Yes | 8 |
| Bethke(2008-Sweden) | Caucasian | Illumina GoldenGate Arrays | HB | 197/197 | Age, sex, ethnicity | Yes | 8 |
| Bethke(2008-Denmark) | Caucasian | Illumina GoldenGate Arrays | HB | 99/100 | Age, sex, ethnicity | Yes | 8 |
| Bethke(2008-Finland) | Caucasian | Illumina GoldenGate Arrays | HB | 128/131 | Age, sex, ethnicity | Yes | 8 |
| Semmler(2006-Germany) | Caucasian | PCR-RFLP | HB | 328/400 | Age, sex, ethnicity | Yes | 9 |
PB population-based, HB hospital-based, HWE Hardy-Weinberg equilibrium, NOS Newcastle-Ottawa Scale
Table 2.
Quality assessment of the case–control studies according to the Newcastle-Ottawa scale
| Literature | Selection of enrolled study subjects | Between-group comparability | Exposure outcomes and factors | Total |
|---|---|---|---|---|
| Kumawat (2018-India) | 2 | 3 | 3 | 8 |
| Bethke (2008-UK-Nouth) | 3 | 3 | 3 | 9 |
| Bethke (2008-UK-Southeast) | 2 | 3 | 3 | 8 |
| Bethke (2008-Sweden) | 2 | 3 | 3 | 8 |
| Bethke (2008-Denmark) | 2 | 3 | 3 | 8 |
| Bethke (2008-Finland) | 2 | 3 | 3 | 8 |
| Semmler (2006-Germany) | 3 | 3 | 3 | 9 |
| Average | 2.3 | 3.0 | 3.0 | 8.3 |
Association between MTR and MTRR polymorphisms and glioma risk
No significant associations between the MTR rs1805087 polymorphism and glioma risk were identified under any genetic model across all populations (all p > 0.05) (Figs. 2, 3, 4, 5 and 6). However, a significant association was found for the MTRR rs1801394 polymorphism in the Asian population (all p < 0.05) (Figs. 7, 8, 9, 10 and 11). The detailed data are presented in Tables 3 and 4.
Fig. 2.
Forest plot for the associations between MTR rs1805087 polymorphism and glioma risk through allele contrast (G vs. A). OR odds ratio, CI confidence interval
Fig. 3.
Forest plot for the associations between MTR rs1805087 polymorphism and glioma risk through homozygote comparison (GG vs. AA). OR odds ratio; CI confidence interval
Fig. 4.
Forest plot for the associations between MTR rs1805087 polymorphism and glioma risk through heterozygous comparison (GA vs. AA). OR odds ratio; CI confidence interval
Fig. 5.
Forest plot for the associations between MTR rs1805087 polymorphism and glioma risk through recessive genetic model (GG vs. GA/AA). OR odds ratio, CI confidence interval
Fig. 6.
Forest plot for the associations between MTR rs1805087 polymorphism and glioma risk through dominate genetic model (GG/GA vs. AA). OR odds ratio, CI confidence interval
Fig. 7.
Forest plot for the associations between MTRR rs1801394 polymorphism and glioma risk through allele contrast (G vs. A). OR odds ratio, CI confidence interval
Fig. 8.
Forest plot for the associations between MTRR rs1801394 polymorphism and glioma risk through homozygote comparison (GG vs. AA). OR odds ratio, CI confidence interval
Fig. 9.
Forest plot for the associations between MTRR rs1801394 polymorphism and glioma risk through heterozygous comparison (GA vs. AA). OR odds ratio, CI confidence interval
Fig. 10.
Forest plot for the associations between MTRR rs1801394 polymorphism and glioma risk through recessive genetic model (GG vs. GA/AA). OR odds ratio, CI confidence interval
Fig. 11.
Forest plot for the associations between MTRR rs1801394 polymorphism and glioma risk through dominate genetic model (GG/GA vs. AA). OR odds ratio, CI confidence interval
Table 3.
Meta-analysis of the MTR rs1805087 polymorphism and glioma risk
| Comparison | Population | N | Test of association | Mode | Test of heterogeneity | ||||
|---|---|---|---|---|---|---|---|---|---|
| OR | 95%CI | P | χ2 | P | I2 | ||||
| G vs. A | Overall | 7 | 0.93 | 0.74–1.17 | 0.532 | Random | 17.32 | 0.008 | 65.4 |
| Asian | 1 | 1.14 | 0.73–1.77 | 0.572 | – | – | – | – | |
| Caucasian | 6 | 0.91 | 0.71–1.16 | 0.438 | Random | 15.92 | 0.007 | 68.6 | |
| GG vs. AA | Overall | 7 | 0.69 | 0.47–1.02 | 0.066 | Random | 5.72 | 0.455 | 0 |
| Asian | 1 | 0.44 | 0.08–2.39 | 0.344 | – | – | – | – | |
| Caucasian | 6 | 0.71 | 0.47–1.07 | 0.099 | Fixed | 5.41 | 0.368 | 7.6 | |
| GA vs. AA | Overall | 7 | 0.98 | 0.74–1.30 | 0.907 | Random | 17.90 | 0.006 | 66.5 |
| Asian | 1 | 1.48 | 0.85–2.58 | 0.165 | – | – | – | – | |
| Caucasian | 6 | 0.93 | 0.69–1.24 | 0.612 | Random | 14.55 | 0.012 | 65.6 | |
| GG vs. GA/AA | Overall | 7 | 0.71 | 0.48–1.06 | 0.091 | Fixed | 4.51 | 0.609 | 0 |
| Asian | 1 | 0.37 | 0.07–1.97 | 0.246 | – | – | – | – | |
| Caucasian | 6 | 0.74 | 0.50–1.11 | 0.151 | Fixed | 3.85 | 0.571 | 0 | |
| GG/GA vs. AA | Overall | 7 | 0.96 | 0.72–1.27 | 0.758 | Random | 19.25 | 0.004 | 68.8 |
| Asian | 1 | 1.36 | 0.79–2.34 | 0.262 | – | – | – | – | |
| Caucasian | 6 | 0.91 | 0.68–1.23 | 0.539 | Random | 16.52 | 0.006 | 69.7 | |
OR odds ratio, CI confidence interval
Table 4.
Meta-analysis of the MTRR rs1801394 polymorphism and glioma risk
| Comparison | Population | N | Test of association | Mode | Test of heterogeneity | ||||
|---|---|---|---|---|---|---|---|---|---|
| OR | 95%CI | P | χ2 | P | I2 | ||||
| G vs. A | Overall | 6 | 0.90 | 0.72–1.12 | 0.353 | Random | 15.24 | 0.009 | 67.2 |
| Asian | 1 | 0.51 | 0.33–0.79 | 0.002 | – | – | – | – | |
| Caucasian | 5 | 1.00 | 0.85–1.17 | 0.996 | Fixed | 6.20 | 0.185 | 35.5 | |
| GG vs. AA | Overall | 6 | 0.83 | 0.50–1.35 | 0.445 | Random | 14.89 | 0.011 | 66.4 |
| Asian | 1 | 0.08 | 0.01–0.66 | 0.019 | – | – | – | – | |
| Caucasian | 5 | 0.95 | 0.62–1.44 | 0.802 | Random | 9.30 | 0.054 | 57.0 | |
| GA vs. AA | Overall | 6 | 0.95 | 0.76–1.19 | 0.660 | Fixed | 7.08 | 0.215 | 29.3 |
| Asian | 1 | 0.50 | 0.29–0.87 | 0.015 | – | – | – | – | |
| Caucasian | 5 | 1.04 | 0.86–1.27 | 0.669 | Fixed | 1.12 | 0.891 | 0 | |
| GG vs. GA/AA | Overall | 6 | 0.58 | 0.32–1.06 | 0.075 | Random | 28.01 | 0 | 82.1 |
| Asian | 1 | 0.11 | 0.01–1.18 | 0.041 | – | – | – | – | |
| Caucasian | 5 | 0.64 | 0.35–1.18 | 0.156 | Random | 25.58 | 0 | 84.4 | |
| GG/GA vs. AA | Overall | 6 | 1.05 | 0.58–1.91 | 0.865 | Random | 55.35 | 0 | 91.0 |
| Asian | 1 | 0.45 | 0.26–0.77 | 0.004 | – | – | – | – | |
| Caucasian | 5 | 1.24 | 0.69–2.23 | 0.464 | Random | 38.21 | 0 | 89.5 | |
OR odds ratio, CI confidence interval
Evaluation of between-study heterogeneity
Analysis of the overall population revealed significant heterogeneity across the five genetic models. To identify the source of this heterogeneity, subgroup analyses and meta-regression were performed. The results of these subgroup analyses identified ethnicity and sample size as the primary sources of heterogeneity (p < 0.05).
Sensitivity analysis and publication bias
The stability of the results was assessed through sensitivity analysis, which involved excluding each study one by one. The overall findings remained consistent, underscoring the robustness of our meta-analysis. Additionally, the funnel plot did not reveal any evident asymmetry, and Egger’s test confirmed the reliability of these findings (P = 0.560), indicating no significant publication bias.
Discussion
Glioma is a malignant tumor arising from brain glial cells and is the most common primary intracranial tumor globally, accounting for 81% of brain malignancies. Although primary brain tumors and other neurological tumors represent only 2% of all cancer incidences, they contribute significantly to the social burden in terms of morbidity and mortality. Gliomas encompass a variety of histological subtypes, the most common being astrocytoma, glioblastoma, and other gliomas. Glioblastoma, the most prevalent histological type of glioma, has a relative survival rate of only 5%. Despite rapid advancements in chemotherapy, radiation, and surgical treatments in recent decades, the 5-year survival rate for gliomas remains below 30%. Therefore, early diagnosis is crucial for the effective treatment of this disease, and identifying potential biomarkers for glioma is essential to further improve its prognosis.
Genetic polymorphisms are DNA sequence variations that occur in healthy individuals with a frequency of about 1%. Common genetic polymorphisms include single nucleotide polymorphisms (SNPs), insertions, and deletions, with SNPs accounting for about 90% of DNA polymorphisms. SNPs are single nucleotide variants that occur at specific locations in the genome and can impact protein structure, gene splicing, transcription factor binding, messenger RNA degradation, or non-coding RNA sequences. Functional SNPs in gene regulatory or coding sequences can alter gene expression or affect protein function, thereby influencing various biological processes.
We reviewed a large number of studies on MTR and MTRR gene polymorphisms and glioma susceptibility and found inconsistent results. We suspect that these inconsistencies may be due to various factors, including ethnic groups, study techniques, control sources, and sample sizes. The best tool to resolve these differences is meta-analysis. Using meta-analysis, we determined that MTR gene polymorphisms do not affect glioma susceptibility. However, MTRR polymorphism appears to be a protective factor in Asian populations, but does not influence glioma susceptibility in Caucasian populations. To our knowledge, this is the first meta-analysis to examine this topic. Our research underscores that race remains one of the most important factors influencing results, a finding supported by previous studies. In addition, taking into account race has a huge impact on the results of genetic polymorphisms. Since all the control groups included in the literature were from hospitals, this resulted in very low heterogeneity in our final results. This, in turn, further supports the reliability of the meta-analysis results.
The Hardy-Weinberg equilibrium (HWE) holds significant importance in both genetics and evolutionary biology. Firstly, it provides a theoretical benchmark model for population genetics. By comparing the actual genotype frequencies of a population with those under ideal conditions, the effects of evolutionary forces (such as natural selection, genetic drift, or gene flow) can be quickly identified. Secondly, in the field of medical genetics, the HWE formula (p² + 2pq + q² = 1) can efficiently estimate the frequency of recessive genetic disease carriers. For example, by using the known proportion of patients (aa), the proportion of heterozygotes (Aa) can be calculated, providing quantitative evidence for genetic counseling and disease screening. Finally, the deviation from the HWE phenomenon is often used as a test standard for the quality of genotyping data. If the sample significantly deviates from the equilibrium state, it may indicate the presence of population stratification, consanguineous mating, or genotyping errors, which are technical issues. This study has verified that all the control group samples have been tested, and all the control group samples have met the HW balance criteria. This study has verified that all the control group samples have been tested, and all the control group samples have met the HWE.
Admittedly, the current study has some limitations. Firstly, the number of included studies is not large, which may weaken the statistical efficiency to some extent. Secondly, this meta-analysis included only Asian and European populations, excluding individuals from Africa, North America, South America, Oceania, and other regions. Lastly, bioinformatics plays a pivotal role in the development of medicine [36, 37]. This study did not have any interaction with bioinformatics.
In conclusion, our study indicates that the MTRR rs1801394 polymorphism serves as a protective factor in India populations but does not affect glioma risk in Caucasian population and Chinese population, which can be used as biomarkers for predicting glioma risk and can serve as targets for personalized treatments of glioma.
Supplementary Information
Acknowledgements
Thanks some students for searching relevant literatures and public databases.
Author contributions
Jun Qiu and Xi Wang conceived study design and conceived the content concept; Xu Chen, Yanping Yao performed the data collection, extraction and analyzed the data. Chen, Yanping Yao interpreted and reviewed the data and drafts. Jun Qiu and Xi Wang reviewed the final draft. All authors were involved in literature search, writing the paper and had final approval of the submitted and published versions.
Funding
The present study was supported by Suzhou “Science and Education Revitalize Health” Youth Science and Technology Project (Grant/Award Number: KJXW2023009).
Data availability
All data generated or analyzed during this study are included in this published article.
Declarations
Ethics approval and consent to participate
All data in this study are from public databases, so no ethical approval or informed consent is required.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Xu Chen and Yanping Yao contributed equally to this work and they should be considered as co-first authors.
Contributor Information
Xi Wang, Email: ci49802974@163.com.
Jun Qiu, Email: 3313666417@qq.com.
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Data Availability Statement
All data generated or analyzed during this study are included in this published article.