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Carcinogenesis Advance Access originally published online on March 19, 2007
Carcinogenesis 2007 28(7):1504-1509; doi:10.1093/carcin/bgm061
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Polymorphisms of one-carbon-metabolizing genes and risk of breast cancer in a population-based study

Xinran Xu1, Marilie D. Gammon5, Heping Zhang6, James G. Wetmur2, Manlong Rao1, Susan L. Teitelbaum1, Julie A. Britton1, Alfred I. Neugut7,8, Regina M. Santella9 and Jia Chen1,3,4,*

1 Department of Community and Preventive Medicine, Mount Sinai School of Medicine, New York, NY 10029, USA
2 Department of Microbiology, Mount Sinai School of Medicine, New York, NY 10029, USA
3 Department of Pediatrics, Mount Sinai School of Medicine, New York, NY 10029, USA
4 Department of Oncological Science, Mount Sinai School of Medicine, New York, NY 10029, USA
5 Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA
6 Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520, USA
7 Department of Epidemiology, Columbia University, New York, NY 10032, USA
8 Department of Medicine, Columbia University, New York, NY 10032, USA
9 Department of Environmental Health Sciences, Columbia University, New York, NY 10032, USA

* To whom correspondence for reprints should be addressed. Tel: +1 212 241 7519; Fax: +1 212 360 6965; Email: jia.chen{at}mssm.edu


   Abstract
Top
 Abstract
Introduction
 Materials and methods
 Results
 Discussion
 References
 
One-carbon metabolism facilitates the crosstalk between genetic and epigenetic processes and plays critical roles in both DNA methylation and DNA synthesis, making it a good candidate for studying the risk of breast cancer. We previously reported that polymorphisms in methylenetetrahydrofolate reductase (MTHFR) in one-carbon pathway were associated with breast cancer risk in the population-based Long Island Breast Cancer Study Project. Herein, we systematically investigated putatively functional polymorphisms of seven other one-carbon-metabolizing genes in relation to the breast cancer risk in the same population. Except for a slight indication of increased risk of breast cancer associated with the double repeat (2R) allele in the thymidylate synthase (TYMS) 5'-untranslated region (UTR) (P, trend = 0.07), polymorphisms in the other six genes did not substantially modify the risk of breast cancer, or did they modify the risk associated with dietary intakes of folate and related B vitamins. However, we observed a significant multiplicative interaction between the MTHFR 677C>T and the TYMS 5'-UTR polymorphisms (P = 0.02). We used a recursive partitioning method, RTREE, in an attempt to tease out important or rate-limiting genes encoding these intricately related enzymes. Results from RTREE analyses indicate that MTHFR and TYMS are the two leading rate-limiting enzymes in the pathway, consistent with our epidemiological findings. Our findings underscore the importance of one-carbon metabolism in breast cancer etiology. Although the pathway is a network of interrelated enzymes, redundancy exists; evaluating the rate-limiting enzyme and its interaction with environment and other genes within the same pathway is critical in assessing breast cancer risk.

Abbreviations: BHMT, betaine-homocysteine methyltransferase; cSHMT, cytosolic serine hydroxymethyltransferase; DHFR, dihydrafolate reductase; LIBCSP, Long Island Breast Cancer Study Project; MTHFR, methylenetetrahydrofolate reductase; MTR, methionine synthase; MTRR, methionine synthase reductase; OR, odds ratio; RFC1, reduced folate carrier 1; TYMS, thymidylate synthase; UTR, untranslated region


   Introduction
Top
 Abstract
 Introduction
Materials and methods
 Results
 Discussion
 References
 
One-carbon metabolism (Figure 1) is a network of interrelated biological reactions in which a one-carbon moiety is transferred among a series of folate-derived compounds. It facilitates the crosstalk between DNA synthesis (genetics) and DNA methylation (epigenetics), both of which are essential processes in breast cancer etiology (1). It provides essential cofactors in the production of S-adenosylmethionine, which is the primary methyl donor for methylation of DNAs as well as RNAs and proteins. It also supplies the methyl group in methylation of deoxyuridine monophosphate to deoxythymidine monophosphate for DNA synthesis. Perturbations of the one-carbon metabolism pathway may play an important role in neoplastic development and growth due to its effects on gene expression through DNA methylation and on genome integrity through DNA synthesis and repair (2,3). For example, it has been shown that folate depletion alone is a sufficient perturbing force to diminish the methyl pool (4).


Figure 1
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  		Fig. 1. Schematic illustration of one-carbon metabolism pathway. Key genes involved in one-carbon metabolism include MTHFR, TYMS, MTR, MTRR, cSHMT, DHFR and BHMT. Reduced folate carrier 1 (RFC1) transports the dietary polyglutamyl folate (the predominant form of folate in diet) in intestinal absorption. Hcy, homocysteine; SAM, S-adenosylmethionine; SAH, adenosylhomocystein; THF, tetrahydrofolate; DHF, dihydrofolate; dUMP, deoxyuridine monophosphate and dUTP, deoxythymidine monophosphate.

 
Epidemiological studies support the importance of methyl supply in breast cancer pathogenesis, but results are not always consistent. Several large prospective epidemiological studies have found that increased folate intake may be protective against breast cancer, especially among alcohol drinker (5–7). However, no effects of folate consumption on risk of breast cancer were observed in two other cohorts including the American Cancer Society's Cancer Prevention Study (8) and the Melbourne Collaborative Cohort (9). In a recent meta-analysis of 13 case–control studies and 9 cohort studies (10), no significant association was found between folate intake and risk of breast cancer. Making the field more controversial is the report from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial, in which an increase of breast cancer risk was associated with folic acid supplemental use of ≥400 µg/day among post-menopausal women (11).

The physiological supply of methyl groups may be determined by interplay of environmental and genetic factors, and the one-carbon metabolism pathway is a good candidate to study gene–environmental interactions. In our previous report on the Long Island Breast Cancer Study Project (LIBCSP), a population-based case–control study, we found inverse associations between B vitamin intake and breast cancer risk among non-supplement users (12). While a functional polymorphism, 677C>T in methylenetetrahydrofolate reductase (MTHFR), a key one-carbon-metabolizing gene, was independently associated with risk of breast cancer, a significant interaction between this polymorphism and folate intake was also apparent (12). Nevertheless, few studies have systematically assessed the contribution of a group of one-carbon metabolism genes in concert with dietary intake to breast carcinogenesis. Using the same LIBCSP population, we systematically investigated the risk of breast cancer associated with putatively functional polymorphisms of seven additional key genes involved in one-carbon metabolism, namely: thymidylate synthase (TYMS), methionine synthase (MTR), methionine synthase reductase (MTRR), cytosolic serine hydroxymethyltransferase (cSHMT) and dihydrofolate reductase (DHFR), betaine–homocysteine methyltransferase (BHMT) and reduced folate carrier 1 (RFC1) (Figure 1). We also investigate whether these polymorphisms interact with dietary consumption of B vitamin in modifying breast cancer risk.


   Materials and methods
Top
 Abstract
 Introduction
 Materials and methods
Results
 Discussion
 References
 
Study population
The LIBCSP has been described in detail previously (13–15). Briefly, cases were women residing in Nassau and Suffolk counties of Long Island, NY, who were newly diagnosed with in situ or invasive breast cancer between 1 August 1996 and 31 July 1997. Controls were matched by frequency to the expected age distribution of the cases; they were identified through random digit dialing for those younger than 65 years and through the Center for Medicare and Medicaid Services rosters for those ≥65 years. About 93% of the study populations are Caucasians.

Data collection
In-person interviews were completed for 82.1% of cases (n = 1508) and 62.8% of controls (n = 1556). Of those who completed an interview, 73.1% of cases (1102) and 73.3% of controls (1141) donated a blood sample (13). As previously reported (13), an increased risk of breast cancer was found to be associated with lower parity, late age at first birth, little or no breastfeeding, a family history of breast cancer and increasing income and education. Associations were similar when the analyses were restricted to respondents who donated blood (13,14). Dietary intake in the year prior to the interview was assessed from a modified Block food frequency questionnaire (15). The frequency and portion size data were translated to daily intakes of nutrients from both dietary and supplement sources using the National Cancer Institute's DietSys version 3. Habitual use of multivitamin supplements was also obtained from the Block food frequency questionnaire. The study protocol was approved by the Institutional Review Boards of the collaborating institutions.

Genotyping
DNA was isolated from blood specimens using the methods described previously (14). Genotyping for the RFC1 (A80G, rs1051266) and cSHMT (C1420T, rs1979277) polymorphisms was ascertained by polymerase chain reaction amplification followed by restriction enzyme digestion as described elsewhere (16,17). TYMS 5'-UTR tandem repeat and DHFR 19 bp deletion polymorphisms were ascertained by polymerase chain reaction amplification and gel size fractionation as described elsewhere (18,19). Genotyping of MTR (A2756G, rs1805087), MTRR (A66G, rs1801394) and BHMT (G742A, rs3733890) was conducted at BioServe Biotechnologies (Laurel, MD) using high-throughput matrix-assisted laser desorption/ionization time-of-flight (http://www.bioserve.com). The mean call rate was 96%; the main reason for genotypes not ascertained was insufficient DNA. About 10% of the study population were random duplicates and included as quality control samples; the concordance rate was higher than 98% for all polymorphisms in this study. All laboratory personnel were blinded to the case–control as well as quality control status of the specimens.

Statistical analysis
The Hardy–Weinberg equilibrium was tested with the Pearson's goodness of fit statistic (20) among the controls for all the polymorphisms in the study. Bivariate analyses were done to compare distributions of covariates among cases and controls. Unconditional logistic regression was used to estimate odds ratios (ORs) and corresponding 95% confidence intervals for the association between the polymorphisms and breast cancer risk adjusting for the frequency-matching variable age (21,22). Dominant, recessive and additive models were examined to identify the relationship between allele combinations and the additive model results were presented. Tests for trend were performed by including genotypes as an ordinal variable in regression models.

The effects were also estimated separately by multivitamin use (yes/no), menopausal status (pre-/post-menopausal) and breast cancer type (invasive/in situ). To evaluate gene–gene and gene–environment interactions on a multiplicative scale, likelihood ratio tests were used to compare the difference of log likelihood statistics for a model with or without a cross-product term for two main effect variables (22).

We evaluated the age-adjusted models for potential confounders including the following: family history of breast cancer in a first-degree relative, history of benign breast disease, education, body mass index at age 20, body mass index at diagnosis, alcohol drinking, parity, lactation history, use of contraceptives, use of hormone replacement therapy, age at menarche, age at first birth and race. If adding a covariate to the logistic regression model changed the effect estimate by ≥10%, the covariate was considered a confounder (21). None of the covariates tested met such criterion, thus only the results of the age-adjusted model are presented. All statistical analyses were performed using SAS version 9.1 (SAS Institute, Cary, NC).

As an attempt to identify key genes in the one-carbon metabolic pathway, we also applied a non-parametric statistical method, RTREE (http://peace.med.yale.edu), which is a recursive partitioning method using splitting rules to stratify data into groups with homogenous risk (23). Splits in the tree are selected after examining all possible binary splits for each variable (genotypes) in order to maximally discriminate disease status in the two resulting groups. A variable producing the greatest discrimination is chosen first to partition the entire dataset (root node) into two offspring nodes. The process is recursively applied to each offspring node to produce further splits and increase the homogeneity, i.e. the breast cancer case, resulting in an initial tree. The order and the level of each split represented the relative importance of each polymorphism and the interactions among them. In the next step, i.e. pruning, splits that may be ‘superficial’ or based on an unreliably small sample is removed from the bottom-up based on a pre-set P value. In our case, a split is regarded unnecessary if the {chi}2 tests from this split as well as its further splits are not significant at P = 0.01 level. So the final tree we presented in the Results section was pruned at P = 0.01 level. The OR presented with each split was calculated from the two-way contingency table suggested by the program. A detailed technical description for constructing classification trees can be found in Zhang et al. (23).


   Results
Top
 Abstract
 Introduction
 Materials and methods
 Results
Discussion
 References
 
For all the polymorphisms listed in Table I, all genotype frequencies were in Hardy–Weinberg equilibrium among the controls (P > 0.11). We previously reported significant associations between the two MTHFR polymorphisms and risk of breast cancer (12). For polymorphisms of seven other one-carbon-metabolizing genes, no significant associations were apparent except for an indication of a dose–response relationship between the TYMS 5'-UTR polymorphism and breast cancer risk (P, trend = 0.07). The genotype–breast cancer risk associations did not differ with respect to menopausal status (pre/post) and cancer types (invasive/in situ) (data not shown).


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  		Table I. Overall effect of one-carbon metabolism genes genotype on breast cancer risk

 
We examined the joint effects of genetic variations and consumption of folate and B vitamins in relation to breast cancer risk in our study population. With the exception of significant MTHFR–folate interactions reported previously (12), we did not observe any significant gene–folate interactions. Because vitamins B2, B6 and B12 are cofactors for MTHFR, cSHMT and MTR, respectively, we also examined potential B2–MTHFR, B6–cSHMT and B12–MTR interactions; however, no indication of interactions was found (data not shown).

Considering the intricate interactions among genes depicted in Figure 1, we tested gene–gene interactions between two adjacent genes in the multiplicative scale, i.e. MTHFR–TYMS, BHMT–MTR, MTRR–MTR, MTHFR–MTR and MTHFR–cSHMT. We observed a significant interaction only between the MTHFR 677C>T and the TYMS 5'-UTR tandem repeat polymorphisms (P for interaction = 0.02) (Table II). Relative to individuals with wild-type genotypes at both loci (677CC-3R/3R), significantly increased risks were observed for the MTHFR 677TT genotype combined with TYMS 3R/3R or 3R/2R genotypes as well as TYMS 2R/2R genotype combined with MTHFR 677CC or TT genotypes.


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  		Table II. MTHFR–TYMS interaction and breast cancer risk

 
We employed the RTREE program (23) to identify subgroups of high-risk subjects based on genetic information and to detect higher order interactions among the large number of genes we examined. Figure 2 illustrates the classification tree generated by the RTREE program, which includes six splits and seven terminal nodes. The study population was first split into two groups by the MTHFR 677C>T polymorphism, suggesting that this polymorphism had the most significant impact on breast cancer risk among all enzymes in one-carbon metabolism pathway. In this RTREE analysis, the TT genotype has higher risk compared with the CC and CT genotypes. The OR and 95% confidence interval are 1.33 (1.05–1.68), which is consistent with our previous findings (12). The CC and CT group was further split according to the TYMS 5'-UTR tandem repeat genotypes, suggesting a possible interaction between these two polymorphisms, a finding that is also consistent with the epidemiological observation. The tree presented in Figure 2 was pruned at P = 0.01 level. The tree in Figure 2 suggests the presence of subgroups of individuals defined by genetic variability in the one-carbon metabolism pathway that could have substantially increased risk of breast cancer.


Figure 2
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  		Fig. 2. RTREE for one-carbon-metabolizing gene polymorphisms and breast cancer risk. Nodes are split into two offspring nodes by genotypes. Numbers of case and control are shown for each node. Polymorphism used for splitting was shown under each node. ORs and 95% confidence intervals were calculated from the two-way contingency table suggested by the RTREE. This OR compares two offspring nodes using the right one as the reference group. For example, for the first split, compared with MTHFR 677 CC/CT group, the TT group has an OR of 1.33 with 95% confidence interval of 1.05–1.68. Refer to Figure 1 for abbreviations for gene names.

 

   Discussion
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
References
 
Genetic variations in enzymes involved in one-carbon metabolism are good candidates for studying the impact of both genetic and environmental effects and their interactions on breast cancer risk. There is epidemiological evidence suggesting that risk of breast cancer may be reduced by increasing consumption of folate and related B vitamins. To the best of our knowledge, our study is the first to systematically evaluate genetic variations involved in one-carbon metabolism pathway in relation to breast cancer risk. Considering the high prevalence of these polymorphisms in the general population, results from the study will help us to identify risk factors for disease prevention.

MTHFR is a critical gene in the one-carbon metabolism pathway. Two non-synonymous polymorphisms, 677C>T and 1298A>C, in the coding region were extensively studied. Their associations with breast cancer risk are conflicting (12,24–30). Reports on other polymorphisms of one-carbon-metabolizing genes are relatively sparse, and have generally been negative. For example, in the Shanghai Breast Cancer Study, the risk of breast cancer did not differ statistically with respect to the MTR or MTRR genotypes, or did these genotypes interact with the MTHFR 677C>T genotype (31). Similarly, a null association between the TYMS 5'-UTR tandem repeat polymorphism and breast cancer risk has been reported (32). A report from a case–control study in Germany showed no association between breast cancer risk and the MTR 2756A>G polymorphism (33). Associations of other one-carbon polymorphisms with breast cancer risk have not been reported.

Except for the significant association between MTHFR and breast cancer risk we reported previously (12), polymorphisms in other one-carbon-metabolizing genes did not substantially modify the risk of breast cancer independently in our study population. These results highlight the critical role of MTHFR in this pathway. MTHFR catalyzes an irreversible conversion of 5,10-MTHF (the major form of intracellular folate) to 5-MTHF (the major form of circulating folate), which may be the rate-limiting step in the pathway since 5,10-MTHF is the substrate for three other enzymatic reactions including TYMS (Figure 1). Changes of MTHFR activity may tilt the balance of one-carbon metabolism in favor of DNA synthesis at the expense of methyl supply (i.e. S-adenosylmethionine) for methylation reactions; a suboptimal methyl supply can lead to aberrant DNA methylation, which has been associated with breast cancer etiology (34). Considering the lack of associations between other polymorphisms with breast cancer risk, we speculate that although one-carbon metabolism involves a web of interrelated enzymes, there exists a substantial redundancy with MTHFR acting as the gatekeeper or the rate-limiting enzyme for the whole pathway. Interrelated reactions in a complex pathway like one-carbon metabolism may compensate any deficit of a single enzyme.

We observed a significant interaction between the MTHFR and TYMS. Such interaction is biologically plausible because 5,10-MTHF is the substrate for both enzymes; it can either be irreversibly converted to 5-MTHF by MTHFR or participate in thymidylate synthesis reaction carried out by TYMS. The former reaction directs the folate pool toward remethylation of homocysteine to methionine whereas the TYMS directs the folate into DNA synthesis, which is the sole de novo source of thymidylate for DNA synthesis in the cell (Figure 1). Nevertheless, the pattern of ORs associated with combined genotypes cannot be easily explained. Given that the MTHFR 677T and TYMS 2R are presumably at-risk alleles indicated by the point estimates in Table I, we should expect to observe the highest risk associated with the MTHFR 677TT- and TYMS 2R/2R-combined genotype. Besides a chance finding, the less than expected OR of this combined genotype may reflect the delicate nature of the balance of MTHFR- and TYMS-mediated reactions.

To investigate relative importance and interactions among multiple genes, we employed RTREE, which is a promising tool to explore potential associations without assuming any model of pre-determined interaction when dealing with large numbers of variables in complex diseases. It has the ability to identify subgroups of individuals defined by genetic characteristics that are at high risk, suggesting the presence of gene–gene interactions. The RTREE results confirmed and strengthened evidence on the important role of the MTHFR in this pathway; it appears at the first split of the tree, which means among all polymorphisms examined in this study, MTHFR 677C>T has the best ability to improve the distribution homogeneity. TYMS was chosen by the program as the second split of the MTHFR CC/CT group. This indicates TYMS, secondary to MTHFR, could further improve the homogeneity among this group. These results support our findings obtained through more traditional epidemiological analyses.

The strong point of the RTREE program lies in its model-free characteristic and its hierarchical structure provides indications of the relative biological importance of each gene examined in the pathway. In molecular epidemiology studies, traditional methods have limited ability to handle multiple polymorphisms simultaneously. While examining the effect of a single polymorphisms, we may also be interested in finding out the relative importance of this polymorphism in the context of the whole pathway. RTREE has the capacity to provide the structural information and allows further investigation by traditional methods. Further more, the structural information may be helpful in interpreting results from a complex pathway in which many factors are involved. Although we focused our investigation on the genetic data in this study, RTREE can also be applied to non-genetic risk factors to subsequently explore the relative importance of all the factors under study and potential gene–environmental interactions.

In our study, we applied a ‘candidate gene’ approach to investigate genetic susceptibility of breast cancer. We restricted our investigation in common polymorphisms (minor allele frequency ≥10%) that were non-synonymous or resided in the promoter region of the gene. In addition, there had to be at least one peer-reviewed publication to demonstrate the functionality or to support the positive genotype–phenotype association of these polymorphisms. An alternative approach would be to use haplotype-tagging single nucleotide polymorphisms (35), where the most common haplotype patterns characterizing each block can be utilized to improve the efficiency of gene–disease association studies. Since informative haplotype-tagging single nucleotide polymorphisms in one-carbon-metabolizing genes in the public database were not well established at the time we performed our genotyping, we focused our study on putatively functional polymorphisms in this pathway. In the future, the haplotype-tagging single nucleotide polymorphism method could be implemented to examine genetic variability in the one-carbon metabolism pathway relation to breast cancer risk.

The major strength of this study lies in its population-based study design in which cases encompassed a broad age range and were drawn from a population-based sample. Thus, results of this study may be more generalizable than a series of cases from a narrow age range or from a single institution. In addition, the relatively large sample size and collection of comprehensive exposure data allow multiple risk factors to be taken into consideration in studying associations with the ability to conduct stratified analyses and adjustment in multivariate models.

One potential limitation of the study is lack of biological folate measurements (e.g. folate in plasma or red blood cells) of the study participants. Biological folate levels were not measured from participants of the LIBCSP because of the case–control study design; biological samples collected after disease diagnosis may have been influenced by the onset, development or even treatment of the disease. It is important to point out that folate measurement at a single time point may not reflect the long-term folate status; integrating dietary intake and inherited genetic variability in folate metabolism may offer better systematic and long-term assessment of biological folate status.

In summary, we systematically evaluated genetic variations involved in one-carbon metabolism in relation to breast cancer risk by applying both the more traditional epidemiological method and the RTREE program. Our findings underscore the importance of one-carbon metabolism in breast cancer etiology. Furthermore, although the pathway is a network of interrelated enzymes, redundancy exists; evaluating the rate-limiting enzyme and its interaction with environment and other genes within the same pathway is critical in assessing breast cancer risk.


   Acknowledgments
 
This work was supported by the Department of Defense (BC031746) and the National Cancer Institute (R01CA109753) and in part by grants from National Cancer Institute and the National Institutes of Environmental Health and Sciences (UO1CA/ES66572, P30ES10126, P30ES09089, K02DA017713 and R01DA016750). X.X. is a recipient of the Predoctoral Traineeship Award (W81XWH-06-1-0298) of Department of Defense Breast Cancer Research Program.

Conflict of Interest Statement: None declared.


   References
Top
 Abstract
 Introduction
 Materials and methods
 Results
 Discussion
 References
 

  1. Machlin LJ. Handbook of Vitamins (1991) 2nd edn. New York: M. Dekker. [revised and expanded edition].
  2. Kim YI. Methylenetetrahydrofolate reductase polymorphisms, folate, and cancer risk: a paradigm of gene-nutrient interactions in carcinogenesis. Nutr. Rev. (2000) 58:205–209.[Web of Science][Medline]
  3. Choi SW, et al. Folate and carcinogenesis: an integrated scheme. J. Nutr. (2000) 130:129–132.[Abstract/Free Full Text]
  4. Miller JW, et al. Folate-deficiency-induced homocysteinaemia in rats: disruption of S-adenosylmethionine's co-ordinate regulation of homocysteine metabolism. Biochem. J. (1994) 298((Pt 2)):415–419.[Web of Science][Medline]
  5. Zhang S, et al. A prospective study of folate intake and the risk of breast cancer. JAMA (1999) 281:1632–1637.[Abstract/Free Full Text]
  6. Seyoum E, et al. Properties of food folates determined by stability and susceptibility to intestinal pteroylpolyglutamate hydrolase action. J. Nutr. (1998) 128:1956–1960.[Abstract/Free Full Text]
  7. Sellers TA, et al. Dietary folate intake, alcohol, and risk of breast cancer in a prospective study of postmenopausal women. Epidemiology (2001) 12:420–428.[CrossRef][Web of Science][Medline]
  8. Feigelson HS, et al. Alcohol, folate, methionine, and risk of incident breast cancer in the American Cancer Society Cancer Prevention Study II Nutrition Cohort. Cancer Epidemiol. Biomarkers Prev. (2003) 12:161–164.[Abstract/Free Full Text]
  9. Baglietto L, et al. Does dietary folate intake modify effect of alcohol consumption on breast cancer risk? Prospective cohort study. BMJ (2005) 331:807.[Abstract/Free Full Text]
  10. Lewis SJ, et al. Meta-analyses of observational and genetic association studies of folate intakes or levels and breast cancer risk. J. Natl Cancer Inst. (2006) 98:1607–1622.[Abstract/Free Full Text]
  11. Stolzenberg-Solomon RZ, et al. Folate intake, alcohol use, and postmenopausal breast cancer risk in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Am. J. Clin. Nutr. (2006) 83:895–904.[Abstract/Free Full Text]
  12. Chen J, et al. One-carbon metabolism, MTHFR polymorphisms, and risk of breast cancer. Cancer Res. (2005) 65:1606–1614.[Abstract/Free Full Text]
  13. Gammon MD, et al. The Long Island Breast Cancer Study Project: description of a multi-institutional collaboration to identify environmental risk factors for breast cancer. Breast Cancer Res. Treat. (2002) 74:235–254.[CrossRef][Web of Science][Medline]
  14. Gammon MD, et al. Environmental toxins and breast cancer on Long Island. I. Polycyclic aromatic hydrocarbon DNA adducts. Cancer Epidemiol. Biomarkers Prev. (2002) 11:677–685.[Abstract/Free Full Text]
  15. Gaudet MM, et al. Fruits, vegetables, and micronutrients in relation to breast cancer modified by menopause and hormone receptor status. Cancer Epidemiol. Biomarkers Prev. (2004) 13:1485–1494.[Abstract/Free Full Text]
  16. De Marco P, et al. Polymorphisms in genes involved in folate metabolism as risk factors for NTDs. Eur. J. Pediatr. Surg. (2001) 11((suppl. 1)):S14–S17.[CrossRef][Web of Science][Medline]
  17. Heil SG, et al. Is mutated serine hydroxymethyltransferase (SHMT) involved in the etiology of neural tube defects? Mol. Genet. Metab. (2001) 73:164–172.[CrossRef][Web of Science][Medline]
  18. Chen J, et al. Polymorphism in the thymidylate synthase promoter enhancer region and risk of colorectal adenomas. Cancer Epidemiol. Biomarkers Prev. (2004) 13:2247–2250.[Abstract/Free Full Text]
  19. Johnson WG, et al. New 19 bp deletion polymorphism in intron-1 of dihydrofolate reductase (DHFR): a risk factor for spina bifida acting in mothers during pregnancy? Am. J. Med. Genet. A (2004) 124:339–345.[Medline]
  20. Cox DG, et al. Genotype transposer: automated genotype manipulation for linkage disequilibrium analysis. Bioinformatics (2001) 17:738–739.[Abstract/Free Full Text]
  21. Rothman KJ. Modern Epidemiology (1998) 2nd edn. Philadelphia, PA: Lippincott-Raven Publishers.
  22. Hosmer DW. Applied Logistic Regression (1989) New York, NY: Wiley.
  23. Zhang H, et al. Recursive Partitioning in the Health Sciences (1999) 1st edn. New York: Springer.
  24. Shrubsole MJ, et al. MTHFR polymorphisms, dietary folate intake, and breast cancer risk: results from the Shanghai Breast Cancer Study. Cancer Epidemiol. Biomarkers Prev. (2004) 13:190–196.[Abstract/Free Full Text]
  25. Lee SA, et al. Methylenetetrahydrofolate reductase polymorphism, diet, and breast cancer in Korean women. Exp. Mol. Med. (2004) 36:116–121.[Web of Science][Medline]
  26. Le Marchand L, et al. MTHFR polymorphisms, diet, HRT, and breast cancer risk: the multiethnic cohort study. Cancer Epidemiol. Biomarkers Prev. (2004) 13:2071–2077.[Abstract/Free Full Text]
  27. Sharp L, et al. Folate and breast cancer: the role of polymorphisms in methylenetetrahydrofolate reductase (MTHFR). Cancer Lett. (2002) 181:65–71.[CrossRef][Web of Science][Medline]
  28. Forsti A, et al. Single nucleotide polymorphisms in breast cancer. Oncol. Rep. (2004) 11:917–922.[Web of Science][Medline]
  29. Semenza JC, et al. Breast cancer risk and methylenetetrahydrofolate reductase polymorphism. Breast Cancer Res. Treat. (2003) 77:217–223.[CrossRef][Web of Science][Medline]
  30. Kalemi TG, et al. The association of p53 mutations and p53 codon 72, Her 2 codon 655 and MTHFR C677T polymorphisms with breast cancer in Northern Greece. Cancer Lett. (2005) 222:57–65.[CrossRef][Web of Science][Medline]
  31. Shrubsole MJ, et al. MTR and MTRR polymorphisms, dietary intake, and breast cancer risk. Cancer Epidemiol. Biomarkers Prev. (2006) 15:586–588.[Free Full Text]
  32. Zhai X, et al. Polymorphisms in thymidylate synthase gene and susceptibility to breast cancer in a Chinese population: a case-control analysis. BMC Cancer (2006) 6:138.[CrossRef][Medline]
  33. Justenhoven C, et al. One-carbon metabolism and breast cancer risk: no association of MTHFR, MTR, and TYMS polymorphisms in the GENICA study from Germany. Cancer Epidemiol. Biomarkers Prev. (2005) 14:3015–3018.[Free Full Text]
  34. Narayan A, et al. Hypomethylation of pericentromeric DNA in breast adenocarcinomas. Int. J. Cancer (1998) 77:833–838.[CrossRef][Web of Science][Medline]
  35. Johnson GC, et al. Haplotype tagging for the identification of common disease genes. Nat. Genet. (2001) 29:233–237.[CrossRef][Web of Science][Medline]
Received January 25, 2007; revised March 9, 2007; accepted March 10, 2007.


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