Introduction

Cancer remains a major public health problem in the world. The complex etiology of this disease is not yet fully elucidated. Identifying risk factors for cancer is critically important to develop potential interventions and to expand our understanding of the biology of this disease. Cancer is a disease resulting from complex interactions between environmental and genetic factors [1]–[3]. An increasing number of cancers also involve an infectious agent. Infection with the bacterium Helicobacter pylori predisposes to gastric cancer. Hepatitis B virus and hepatitis C virus are associated with liver cancer, and Epstein-Barr virus is associated with lymphoma and nasopharyngeal cancer. Human papillomavirus is a major cause of cervical, anal, penile, and oropharyngeal cancer. It is well known that the number of infection-related cancers is estimated at nearly 2 million cases per year, accounting for almost 20% of all cancer case. Genetic factors, including sequence alterations and organization aberrations of the cellular genome that range from single-nucleotide substitutions to gross chromosomal changes, could modulate several important biological activities and cancer susceptibility. Single nucleotide polymorphisms (SNPs) are the most common source of human genetic variation, and they may contribute to an individual’s susceptibility to cancer. In an attempt to clarify the exact mechanism by which genetic variation influences an individual’s susceptibility to cancer, many extensive studies have been performed worldwide.

Folate is a water-soluble B vitamin involved in one-carbon metabolism that plays an essential role in the synthesis, repair, and methylation of DNA [4]–[6]. Three main molecular mechanisms that link folate deficiency to tumor formation have been proposed. Folate deficiency can decrease global DNA methylation, which is associated with genetic instability and tumor formation. The second potential tumorigenic pathway of folate deficiency is increased uracil misincorporation during DNA replication. The third potential tumorigenic pathway of folate deficiency is enzymatic cytosine deamination at sites of DNA methylation. Folate metabolism provides one-carbon units necessary for the synthesis of nucleic acid bases and enables the conversion of methionine into S-adenosylmethionine (SAM), via its ability to methylate homocysteine. SAM is the universal methyl group donor in the majority of biochemical reactions including DNA methylation [7], [8]. If folate availability is continuously limited, an uncontrolled repair cycle can cause frequent breaks in DNA molecules and chromosomal damage [9]. All of these mechanisms contribute to genetic instability and may facilitate carcinogenesis, thus leading to the hypothesis that imbalances in folate metabolism can influence cancer risk.

MTHFD1 is the NADP-dependent tri-functional enzyme acting as 5,10-methylenetetrahydrofolate dehydrogenase; 5,10-methenyltetrahydrofolate cyclohydrolase; and 10-formylotetrahydrofolate synthetase [10]. MTHFD1, in three sequential reactions, provides 1-carbon derivatives of tetrahydrofolate that are substrates for biosynthesis of thymidylate, purinenucleotides, and methionine [10], [11]. Methionine is formed duringmethylation of homocysteine by methionine synthase, which uses methyl-tetrahydrofolate as a methyl donor [12]. Methionine adenosyltransferase, using methionine and ATP, induces formation of SAM [13]. Several polymorphisms in the MTHFD1 gene have been reported, including two common SNPs: G1958A (R653Q, rs2236225) and G401A (R134K, rs1950902). The MTHFD1 G1958A polymorphism is located within the 10-formyltetrahydrofolate synthetase domain and may modulate biosynthesis of thymidylate, purine nucleotides, and methionine effecting DNA methylation [11], [14], [15], [16]. The G401A SNP changes an arginine to a lysine in the dehydrogenase/cyclohydrolase domain of MTHFD1 and may affect these activities. However, no studies have investigated the functional consequences of this SNP.

A variety of molecular epidemiological studies have focused on the associations between MTHFD1 polymorphisms and cancer susceptibility. These studies have shown that the MTHFD1 polymorphisms occur in different types of cancer, but the results are inconclusive, partially because of the possible small effect of the polymorphism on cancer risk and the relatively small sample size in each of published studies. To solve the problem of inadequate statistical power and controversial results, it is necessary to carry out a systematic review and meta-analysis including subgroup analysis from all eligible studies to assess the association of the MTHFD1 polymorphisms with cancer risk.

Materials and Methods

Results

Meta-analysis

The main results of the meta-analysis of the association between MTHFD1 polymorphisms and cancer risk are shown in Table 1. We first analyzed the association in the overall population. Then in order to obtain the exact consequence of the relationship between MTHFD1 polymorphisms and cancer susceptibility, stratified analyses by ethnicity and cancer type were performed. When the Q-test of heterogeneity was not significant, we conducted analyses using the fixed effect models. The random effect models were conducted when we detected significant between-study heterogeneity.

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Table 1. Meta-Analysis of MTHFD1 Gene Polymorphisms and Cancer.

https://doi.org/10.1371/journal.pone.0069366.t001

Discussion

SNP is the most common form of human genetic variation, and may contribute to individual’s susceptibility to cancer. However, the underlying molecular mechanism is unknown. Genetic polymorphisms in the folate pathway have been investigated in a wide variety of diseases, such as neural tube defects [41], pancreatic cancer [42], and congenital heart defects [43]. In recent years, many studies have been conducted to investigate the association between MTHFD1 polymorphisms and cancer risk in humans across different countries [21]–[40]. However, these studies have appeared in the literature either supporting or negating the significant association. Some reviewed studies are limited by their sample size and subsequently suffer from too low power to detect effects that may exist. But the pool ORs generated from much larger population can increase the statistical power. Meta-analysis has great power for elucidating genetic factors in cancer. For better understanding of the association between this polymorphism and cancer risk, a pooled analysis with a large sample, subgroup analysis performed, and heterogeneity explored is necessary.

The results indicate that MTHFD1 G1958A and G401A are not risk factors for developing cancer in the overall study populations. The potential explanation is that the effect of a single polymorphism might have a limited impact on cancer susceptibility. This is in accordance with the hypothesis that cancer is a multifactorial disease that results from complex interactions between environmental and genetic factors. Nevertheless, considering that these 2 polymorphisms may play different roles in different cancer susceptibility among different ethnic subgroups and the frequencies of these 2 polymorphisms polymorphism might be different among different ethnic groups, we further conducted subgroup analysis by ethnicity and cancer type in current meta-analysis.

In this meta-analysis, when stratifying by ethnicity we found that the association between MTHFD1 G1958A polymorphism a decreased risk of cancer was significant only in Asians, not in the Caucasian population. Although the reasons for this difference remain controversial, there are several studies showing that it depends on a combination of differences in polymorphism distributions with nongenetic factors. Therefore, the difference of this polymorphism of MTHFD1 in Caucasians and Asians may result from interactions with environmental and social factors. On the other hand, the sample size and numbers of studies in Asian group were not adequate to evaluate the association. Other factors such as selection bias and different matching criteria may also play a role. In addition, considering the multistage character of cancer, genetic factors may play a role at specific stages only, which may vary between populations. Therefore, larger-scale studies and combined analysis are warranted to further confirm the effect of ethnic difference in this polymorphism on cancer risks. For G1958A, in the stratified analysis by cancer type, our results demonstrated that no significant associations were found in any genetic model among studies of colorectal cancer, head and neck cancer, and prostate cancer. For acute lymphoblastic leukemia, significantly decreased risk was observed in dominant model, and allelic model. With respect to other cancers, the results indicated that MTHFD1 G1958A was significantly associated with a decreased other cancers risk under recessive model. For G401A, MTHFD1 G401A polymorphism was associated with a decreased colon cancer risk under dominant model. One factor that would contribute to the discrepancy among different studies is that these 2 polymorphisms might play a different role in different cancer sites. However, even at the same cancer site, considering the possible small effect size of these 2 polymorphisms to cancer risk and the relatively small sample size in some studies, the discrepancy will become apparent since some of these studies may be underpowered to detect a small but real association. For acute lymphoblastic leukemia, there were only two studies included in the analysis with limited sample sizes, therefore, the results should be interpreted with caution. Considering the limited studies and population numbers of “other cancers” included in the meta-analysis, this may increase the risk of false negative findings. It is well recognized that there is individual susceptibility to the same kind of cancer even with the same environmental exposure. Host factors, including polymorphisms of genes involved in carcinogenesis, may have accounted for this difference. The discrepancy may have resulted from a differential effect of selection criteria in different cancers, which was dictated by the sample size in our meta-analysis, as well as the weight of each study. Other factors such as matched criteria may also have conferred an effect. The above differences may contribute to the inconsistent results. Therefore, it is very important to determine the unified selection criteria and to choose larger sample population studies.

Heterogeneity is a potential problem that might affect the interpretation of the results. Additionally, heterogeneity may influence the results of meta-analyses. For G401A, evident heterogeneity between studies was observed in overall comparisons and also in certain subgroup analyses. In overall analysis, significant between-study heterogeneity existed in allelic model comparison and dominant model comparison. After subgroup analyses by cancer type, the heterogeneity was removed in colon cancer. However, significant heterogeneity existed in allelic and dominant models when stratified according to cancer type. There are some factors that could have contributed toward the heterogeneity. First, the reason might be that different genetic backgrounds and the environment existed among different individuals. Second, one possibility involves differences in the matching status. However, we cannot confirm this possibility because no detailed information was provided. Third, we attempted to determine if the heterogeneity might also be explained by other variables such as smoking status, drinking status, and environmental factors included in the different studies, but are unable to provide a reliable answer to this question because we did not have access to individual level data for these variables.

Some limitations of this meta-analysis should be acknowledged. Firstly, only studies published were included in this meta-analysis, and nonsignificant or negative findings may be unpublished [44]–[46]. Hence, some inevitable publication biases might exist in the results. Secondly, controls were not uniformly defined. Healthy populations as well as non-cancer patients were included. Some individuals in the control group are likely to develop cancer in subsequent years though they had no clinical symptoms at the time of investigation. So selection bias may occur and they may not be representative of the general population. Thirdly, our result was based on unadjusted estimates, while a more precise analysis should be conducted adjusted by other factors like age, smoking status, drinking status, and environmental factors. Lack of information for data analysis may cause serious confounding bias. Fourthly, the meta-analysis was limited by a relatively small number of available studies. It is difficult to perform subgroup analysis for every type of cancer. Fifthly, in the subgroup analyses by ethnicity and cancer type, the sample size of studies among Asians and among several cancer types is small and limited, and the statistical power was so low that caution should be taken in interpreting these results. Sixthly, the association between G401A polymorphisms and ovarian cancer were based solely on the results of the Kelemen et al study which has been published previously, no additional information to the previously published study was found. In addition, lacking of the original data of the reviewed studies limited our further evaluation of potential interactions, because the interactions among gene-gene, gene-environment and even different polymorphic locis of the same gene may modulate cancer risk. Further investigations of the haplotypic effect of a gene and the study of multiple polymorphisms in different genes are needed.

Supporting Information