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International Journal of Clinical and Experimental Medicine logoLink to International Journal of Clinical and Experimental Medicine
. 2015 Nov 15;8(11):20146–20156.

MTHFR gene A1298C polymorphisms are associated with breast cancer risk among Chinese population: evidence based on an updated cumulative meta-analysis

Yadong Wang 1,2,*, Haiyan Yang 3,*, Guangcai Duan 2
PMCID: PMC4723772  PMID: 26884927

Abstract

Objectives: Published studies on the association between methylenetetrahydrofolate reductase (MTHFR) gene A1298C polymorphisms and breast cancer risk among Chinese population have yielded conflicting results. The purpose of this study was to clarify the association between MTHFR gene A1298C polymorphisms and breast cancer risk among Chinese population. Methods: Systematic searches were performed through the database of Medline/PubMed, Science Direct, Elsevier, CNKI and Wanfang Medical Online. Results: Overall, a significantly increased risk of breast cancer was observed among the subjects carrying MTHFR gene A1298C AC+CC genotype (odds ratio [OR]=1.05 with 95% confidence interval [CI]: 1.01-1.10) as compared to those carrying AA genotype among total Chinese population. We did not observe any significant association between MTHFR gene A1298C polymorphisms and the risk of breast cancer under the additional genetic models of AC vs. AA, CC vs. AA and C-allele vs. A-allele (OR=1.00 with 95% CI: 0.97-1.02, OR=1.01 with 95% CI: 1.00-1.02 and OR=1.00 with 95% CI: 0.99-1.02, respectively). The cumulative meta-analysis showed similar results. In subgroup analysis, we observed subjects carrying AC+CC genotype had an increased breast cancer risk compared with those carrying AA genotype among the studies of sample size less than 1000. We did not observe any significant association between MTHFR gene A1298C polymorphisms and breast cancer risk in additional subgroup analyses. Conclusions: Our results suggest that MTHFR gene A1298C AC+CC genotype may be a risk factor for the development of breast cancer among Chinese population. Well-designed studies with a large sample size are needed to further confirm our findings.

Keywords: MTHFR, polymorphism, breast cancer, risk, Chinese

Introduction

Methylenetetrahydrofolate reductase (MTHFR) is a folate-dependent enzyme that plays a pivotal role in the conversion of homocysteine to methionine by converting 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate [1,2]. The MTHFR gene is mapped to chromosome 1 location p36.3 in humans. To date, more than forty single nucleotide polymorphisms (SNPs) have been identified in the MTHFR gene. Among them, the polymorphic site of rs1801131 in exon 7 (A1298C, Glu429Ala) is one of the most extensively studied polymorphisms, which is related to the reduction of MTHFR activity [3,4].

Breast cancer is the most commonly diagnosed cancer in women worldwide, and it has become the second leading cause of cancer related death in Chinese females [5]. The etiology of breast cancer is complicated and poorly understood. Some recognized risk factors for breast cancer include age, ethnicity, lifestyle, reproductive events, exogenous hormones, bone density, as well as genetic factors [6]. It has been proposed that low-penetrance susceptibility genes combining with environmental factors may be of importance in the development of breast cancer.

To the best of our knowledge, a growing number of epidemiological studies have focused on to explore the association between MTHFR gene A1298C polymorphisms and breast cancer risk among Chinese population [7-20]. However, the results remained conflicting rather than consistent. The discrepancies among these studies might attribute to the relatively small sample size in each study. Meta-analysis is considered as a powerful tool for summarizing the contradicting results from different studies with more statistical power. We set out to clarify the association between MTHFR gene A1298C polymorphisms and breast cancer risk among Chinese population by performing a systematic meta-analysis on the most updated data in the literatures.

Materials and methods

Search strategy, inclusion criteria and exclusion criteria

A comprehensive search was carried out through the database of Medline/PubMed, Science Direct, Elsevier, China National Knowledge Infrastructure (CNKI) and Wanfang Medical Online with a combination of the following terms: “breast cancer”, “breast neoplasm” or “breast carcinoma” and “Methylenetetrahydrofolate reductase” or “MTHFR” or “rs180-1131”. Last search was updated on April 30, 2015. The references cited in the publications and review articles were also manually searched.

Data inclusion criteria: (a) The papers should include breast cancer risk and MTHFR gene A1298C polymorphisms; (b) Case-control studies or cohort studies; (c) Sufficient data to estimate the odds ratio (OR) and 95% confidence interval (95% CI). For overlapping or repeated studies, the publication including more information was selected. Accordingly, reviews and papers lacking essential information were excluded.

Data extraction

Data were independently extracted and tabulated by two investigators and an electronic database was established. The following extracted information from each eligible study was included in the database: authors’ name, publishing year, region, source of controls, genotype frequency of cases and controls. Quality of the studies was evaluated according to the “methodological quality assessment scale” [21].

Statistical analysis

The Cochrane Q statistics test was used for the assessment of heterogeneity among studies. A fixed-effects model or a random-effects model was used to estimate the combined effects depending on the results of the heterogeneity test [22]. The fixed-effects model is used while the effects are assumed to be homogenous; otherwise, the random-effects model is used. Begg’s test and Egger’s test were applied to assess the publication bias [23,24]. The χ2 test was applied to check whether the genotype frequencies of the controls were in agreement with Hardy-Weinberg equilibrium (HWE) or not. A sensitivity analysis was performed by deleting one study each time. A cumulative meta-analysis was performed, which accumulated the studies in the light of the publishing year. All of the statistical analyses were performed by using STATA10.0 software package (STATA Corporation, College Station, TX, USA). Statistical significance was determined as a 2-sided P-value less than 0.05.

Results

Study description

In total, nineteen published studies were identified. All of the papers were reviewed using the criteria defined above; two reviews and three overlapping articles were excluded. Figure 1 showed the flow diagram of selection process. At last, a total of fourteen studies with 6125 cases and 6936 controls were included in this study. Table 1 listed the essential information including first author, year of publication, source of controls, region, number of cases and controls, quality scores and P value of HWE.

Figure 1.

Figure 1

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Flow diagram of articles selection process.

Table 1.

General information of the selected articles in this study

Author Year Region Source of controls Cases Controls P value of HWE Quality scores
Total AA AC CC Total AA AC CC
Qi J [7] 2004 Beijing PB 217 155 58 4 218 144 71 3 0.076801 6
Shrubsole M [8] 2004 Shanghai PB 1121 768 311 42 1208 824 344 40 0.578416 8
Kan X [13] 2007 Yunnan PB 125 70 41 14 101 61 32 8 0.207067 6
Cheng C [9] 2008 Taiwan HB 351 207 125 19 534 310 207 17 0.011603 7
Inoue M [12] 2008 Singapore PB 380 225 139 16 662 387 234 41 0.481608 7
Gao C [10] 2009 Nanjing PB 624 446 169 9 624 425 188 11 0.056474 7
Lin J [14] 2010 Guangdong PB 65 45 14 6 143 98 35 10 0.011193 5
Hua Z [11] 2011 Yunnan PB 95 50 42 3 90 55 32 3 0.522201 6
Wu X [15] 2012 Yunnan HB 75 37 32 6 75 42 28 5 0.909277 5
Liu Y [18] 2013 Guangdong HB 435 206 176 53 435 214 172 49 0.111545 6
Qiao J [20] 2014 Henan HB 535 258 235 42 673 351 280 42 0.158451 7
He J [16] 2014 Henan HB 310 138 132 40 381 173 155 53 0.058963 6
Huang C [17] 2014 Taiwan HB 1232 787 386 59 1232 796 391 45 0.723857 8
Lu [19] 2015 Shanghai HB 560 369 172 19 560 352 185 23 0.831991 8
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PB: Population-based control; HB: Hospital-based control; HWE: Hardy-Weinberg equilibrium.

Test of heterogeneity

There are three MTHFR gene A1298C genotypes/polymorphisms present, namely, AA, AC and CC. The heterogeneity was analyzed for MTHFR gene A1298C AC vs. AA, CC vs. AA, AC+CC vs. AA and C-allele vs. A-allele. There was heterogeneity identified in the group of MTHFR gene A1298C AC+CC vs. AA for the analysis of total population study, population-based study, hospital-based study, HWE in controls, publishing year before 2010 and sample size >1000. Therefore, we calculated the pooled odds ratios for the analyses using the random-effects model. The fixed-effects model was used to calculate the pooled odds ratios for the rest.

Quantitative data synthesis

The combined odds ratios of the association between MTHFR gene A1298C polymorphisms and the risk of breast cancer among Chinese population were listed in Table 2. Overall, a significantly increased risk of breast cancer was observed among the subjects carrying MTHFR gene A1298C AC+CC genotype (OR=1.05 with 95% CI: 1.01-1.10) as compared to those carrying AA genotype among the total Chinese population (Figure 2A). We did not observe any significant association between MTHFR gene A1298C polymorphisms and the risk of breast cancer under the additional genetic models of AC vs. AA, CC vs. AA and C-allele vs. A-allele (OR=1.00 with 95% CI: 0.97-1.02, OR=1.01 with 95% CI: 1.00-1.02 and OR=1.00 with 95% CI: 0.99-1.02, respectively) (Figure 2B-D). Limiting the analysis to the studies with controls in agreement with HWE, we did not observe any significant association between MTHFR gene A1298C polymorphisms and the risk of breast cancer (OR=1.00 with 95% CI: 0.97-1.03 for AC vs. AA, OR=1.01 with 95% CI: 0.99-1.02 for CC vs. AA, OR=1.04 with 95% CI: 0.99-1.06 for AC+CC vs. AA and OR=1.00 with 95% CI: 0.99-1.02 for C-allele vs. A-allele, respectively) (Table 2).

Table 2.

The summary odds ratio for the association of MTHFR gene A1298C polymorphisms with breast cancer risk among Chinese population

Genetic model Cases/controls Heterogeneity test Analysis model Summary OR (95% CI) Hypothesis test df Begg’s test Egger’s test
Q P Z P Z P t P
Total
    CA vs. AA 5793/6586 8.15 0.833 Fixed-effects model 1.00 (0.97-1.02) 0.37 0.710 13 0.66 0.511 0.69 0.506
    CC vs. AA 4093/4582 10.29 0.670 Fixed-effects model 1.01 (1.00-1.02) 1.35 0.178 13 0.33 0.743 0.07 0.949
    CA+CC vs. AA 6125/6936 28.85 0.007 Random-effects model 1.06 (1.01-1.10) 2.24 0.025 13 1.20 0.228 0.82 0.429
    C vs. A 12250/13872 10.24 0.674 Fixed-effects model 1.00 (0.99-1.02) 0.46 0.644 13 0.22 0.827 0.36 0.723
Stratification by HWE
    Yes
        CA vs. AA 5402/5936 7.77 0.734 Fixed-effects model 1.00 (0.97-1.03) 0.15 0.879 11 1.17 0.244 1.01 0.335
        CC vs. AA 3816/4147 8.30 0.687 Fixed-effects model 1.01 (0.99-1.02) 0.96 0.335 11 0.62 0.537 0.41 0.690
        CA+CC vs. AA 5709/6259 21.82 0.026 Random-effects model 1.04 (0.99-1.02) 1.54 0.123 11 0.75 0.451 0.14 0.893
        C vs. A 11418/12518 10.11 0.520 Fixed-effects model 1.00 (0.99-1.02) 0.37 0.709 11 0.07 0.945 0.29 0.780
Stratification by source of control
    Population-based control
        CA vs. AA 2533/2930 4.12 0.660 Fixed-effects model 0.98 (0.95-1.02) 0.84 0.402 6 0.30 0.764 0.46 0.664
        CC vs. AA 1853/2110 3.58 0.733 Fixed-effects model 1.00 (0.99-1.01) 0.02 0.984 6 0.00 1.000 0.45 0.670
        CA+CC vs. AA 2627/3046 15.16 0.019 Random-effects model 1.05 (0.98-1.12) 1.36 0.173 6 0.60 0.548 0.39 0.715
        C vs. A 5254/6092 4.19 0.651 Fixed-effects model 0.99 (0.98-1.01) 0.68 0.499 6 0.30 0.764 0.82 0.450
    Hospital-based control
        CA vs. AA 3260/3656 3.47 0.745 Fixed-effects model 1.00 (0.97-1.04) 0.22 0.823 6 0.60 0.548 0.73 0.498
        CC vs. AA 2240/2472 4.28 0.639 Fixed-effects model 1.02 (1.00-1.04) 1.64 0.102 6 0.00 1.000 0.06 0.952
        CA+CC vs. AA 3498/3890 13.68 0.033 Random-effects model 1.06 (0.99-1.13) 1.67 0.095 6 0.60 0.548 0.74 0.491
        C vs. A 6996/7780 4.38 0.625 Fixed-effects model 1.01 (0.99-1.03) 1.12 0.261 6 0.30 0.764 0.06 0.952
Stratification by publishing year
    Before 2010
        CA vs. AA 2714/3227 2.32 0.803 Fixed-effects model 0.98 (0.94-1.01) 1.23 0.217 5 0.00 1.000 0.25 0.814
        CC vs. AA 1975/2271 5.52 0.356 Fixed-effects model 1.00 (0.99-1.02) 0.44 0.658 5 0.00 1.000 0.07 0.949
        CA+CC vs. AA 2818/3347 16.71 0.005 Random-effects model 1.05 (0.98-1.13) 1.40 0.161 5 0.00 1.000 0.48 0.656
        C vs. A 5636/6694 3.28 0.656 Fixed-effects model 0.99 (0.98-1.01) 0.72 0.469 5 0.38 0.707 0.30 0.779
    After 2010
        CA vs. AA 3079/3359 4.50 0.721 Fixed-effects model 1.01 (0.97-1.05) 0.63 0.526 7 0.62 0.536 0.37 0.587
        CC vs. AA 2118/2311 3.56 0.829 Fixed-effects model 1.01 (0.99-1.03) 1.34 0.181 7 0.62 0.536 0.91 0.399
        CA+CC vs. AA 3307/3589 11.97 0.101 Fixed-effects model 1.04 (1.00-1.08) 1.72 0.085 7 1.11 0.266 0.53 0.615
        C vs. A 6614/7178 5.00 0.660 Fixed-effects model 1.01 (0.99-1.03) 1.23 0.217 7 0.37 0.711 0.11 0.916
Stratification by sample size
    <1000
        CA vs. AA 1528/1829 4.86 0.677 Fixed-effects model 1.00 (0.95-1.06) 0.05 0.960 7 0.37 0.711 0.71 0.505
        CC vs. AA 1053/1245 2.00 0.960 Fixed-effects model 1.02 (0.99-1.05) 1.25 0.211 7 0.12 0.902 0.08 0.942
        CA+CC vs. AA 1673/1977 7.70 0.360 Fixed-effects model 1.07 (1.01-1.14) 2.36 0.018 7 0.12 0.902 0.57 0.589
        C vs. A 3346/3954 3.49 0.837 Fixed-effects model 1.01 (0.98-1.04) 0.81 0.416 7 0.62 0.536 0.42 0.688
    >1000
        CA vs. AA 4265/4757 3.29 0.656 Fixed-effects model 0.99 (0.96-1.02) 0.47 0.639 5 0.00 1.000 0.20 0.851
        CC vs. AA 3040/3337 6.82 0.234 Fixed-effects model 1.00 (0.99-1.02) 0.71 0.476 5 1.13 0.260 2.78 0.050
        CA+CC vs. AA 4452/4959 19.30 0.002 Random-effects model 1.04 (0.98-1.11) 1.33 0.183 5 0.38 0.707 0.67 0.540
        C vs. A 8904/9918 6.25 0.283 Fixed-effects model 1.00 (0.99-1.01) 0.01 0.994 5 1.50 0.133 1.87 0.135
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HWE: Hardy-Weinberg equilibrium; OR: odds ratio; 95% CI: 95% confidence interval.

Figure 2.

Figure 2

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Forest plots of the odds ratio for the association between MTHFR gene A1298C polymorphisms and breast cancer risk among Chinese population (A: AC+CC vs. AA; B: AC vs. AA; C: CC vs. AA and D: C-allele vs. A-allele).

In the subgroup analysis by source of controls, we did not observe any significant association between MTHFR gene A1298C polymorphisms and the risk of breast cancer among subjects on the basis of population-based controls and hospital-based controls (Table 2). When stratified by publishing year, we did not observe any significant association between MTHFR gene A1298C polymorphisms and the risk of breast cancer among the studies published before 2010 and after 2010 (Table 2). When stratified by sample size, we observed that subjects carrying AC+CC genotype had an increased breast cancer risk compared with those carrying AA genotype among the studies of sample size less than 1000 (Table 2). We did not observe any significant association between MTHFR gene A1298C polymorphisms and the risk of breast cancer in additional subgroup analyses stratified by sample size (Table 2).

The cumulative meta-analysis showed that there was still a significant association between MTHFR gene A1298C polymorphisms and the risk of breast cancer under the genetic model of AC+CC vs. AA, and the cumulative OR was 1.14 with 95% CI: 1.02-1.29 (Figure 3A). Once again, There was still not any significant association between MTHFR gene A1298C polymorphisms and the risk of breast cancer under the additional genetic models of AC vs. AA, CC vs. AA and C-allele vs. A-allele in the cumulative meta-analysis (OR=0.99 with 95% CI: 0.92-1.06, OR=1.12 with 95% CI: 0.95-1.32 and OR=1.01 with 95% CI: 0.96-1.08, respectively) (Figure 3B-D).

Figure 3.

Figure 3

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Forest plots of the cumulative odds ratio for the association between MTHFR gene A1298C polymorphisms and breast cancer risk among Chinese population (A: AC+CC vs. AA; B: AC vs. AA; C: CC vs. AA and D: C-allele vs. A-allele).

Bias diagnosis

The shape of the funnel plots did not reveal any evidence of obvious asymmetry among the total population (Figure 4A-D), suggesting that no potential publication bias exists. Begg’s test and Egger’s test showed that publication bias might not have significant effects on the pooled results (Table 2).

Figure 4.

Figure 4

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Funnel plot analysis to detect publication bias for the association between MTHFR gene A1298C polymorphisms and breast cancer risk among Chinese population (A: AC+CC vs. AA, B: AC vs. AA, C: CC vs. AA and D: C-allele vs. A-allele).

Sensitivity analysis

A sensitivity analysis was done to test the effects of the individual dataset on the pooled odds ratio by sequentially removing each eligible study. The pooled effects were not significantly altered with such manipulation for AC+CC vs. AA among the total population (Figure 5).

Figure 5.

Figure 5

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Sensitivity analysis on the association between MTHFR gene A1298C polymorphisms and breast cancer risk among Chinese population (AC+CC vs. AA).

Discussion

In 2004, Shrubsole et al., for the first time, investigated the association of MTHFR gene A1298C polymorphisms with breast cancer risk among Chinese population [8]. Thereafter, a number of papers have tested the association between MTHFR gene A1298C polymorphisms and breast cancer risk among Chinese population (Data listed in Table 1). The results remained inconsistent. To the best of our knowledge, two meta-analysis studies have been done regarding the association between MTHFR gene A1298C polymorphisms and breast cancer risk among Chinese population [25,26]. However, careful examinations of the data reported by the two previous meta-analyses reveal several key issues that are worth noticing. For example, the data reported by Liang et al. [25] for Gao et al.’s study [10] did not seem in line with the data reported by Gao et al. [10]. For overlapping data [9,27], one study reported by Chou et al. [27] with smaller sample size of cases and controls was included in Liang et al.’s study [25] and Song et al.’s study [26] instead of an eligible study reported by Cheng et al. [9] with larger sample size of cases and controls. Three eligible publications [12-14] were missing in Song et al.’s study [26]. Therefore, the conclusions by the previous studies are not entirely credible. Moreover, five studies [16-20] with large sample size have emerged since then. This urged us to clarify the association between MTHFR gene A1298C polymorphisms and breast cancer risk among Chinese population.

By carefully combing through the data from fourteen eligible studies, including 6125 cases and 6936 controls, we provided here a more comprehensive and precise assessment of the association between MTHFR gene A1298C polymorphisms and breast cancer risk among Chinese population using up to date published data with the largest case number thus far. We found that the subjects carrying MTHFR gene A1298C AC+CC genotype had an increased risk of breast cancer among Chinese population as comparing to those bearing AA genotype, which is highly statistically significant. Our results are not consistent with these of the previous studies that did not observe any significant association. The discrepancies between our results and theirs may be owing to that our study included comprehensive and precise data. Moreover, a cumulative meta-analysis was carried out to test the tendency by accumulating studies year by year to determine the necessity for new relevant studies. The results remained stable when the studies were accumulated.

Publication bias is always a concern when dealing with pooled data from the literatures. To address this issue, Egger’s test and Begg’s test were performed. Our results showed that the likelihood of key publication bias was negligible in this current study. In addition, each study had different eligibility criteria for participants and source of controls, which should be taken into consideration when expounding the pooled estimates. When the subgroup analysis was performed by source of controls, we did not observe any association between MTHFR gene A1298C polymorphisms and breast cancer risk based on population-based study and hospital-based study.

It is widely acknowledged that the results of genetic association studies might be spurious if the distribution frequency of genotypes in the controls deviated from HWE [28]. In order to address this issue, we performed a subgroup analysis by HWE in controls. When the studies being not in agreement with HWE were excluded from this meta-analysis, we did not observe the association between MTHFR gene A1298C AC+CC vs. AA polymorphism and breast cancer risk among Chinese population.

In summary, our results strongly suggest that MTHFR gene A1298C AC+CC vs. AA polymorphism is associated with an increased risk of breast cancer among Chinese population. Well-designed studies with large sample size are needed to further confirm our results.

Acknowledgements

This work was supported by a grant from the National Natural Science Foundation of China (No. U1404815).

Disclosure of conflict of interest

None.

Abbreviations

CI

confidence interval

OR

odds ratio

HWE

Hardy-Weinberg equilibrium

SNP

single nucleotide polymorphism

MTHFR

methylenetetrahydrofolate reductase

CNKI

China National Knowledge Infrastructure

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