Abstract
Folate-pathway gene polymorphisms have been implicated in several cancers and investigated inconclusively in relation to prostate cancer. We conducted a systematic review, which identified nine case-control studies (eight included, one excluded). We also included data from four genome-wide association studies and from a case-control study nested within the UK population–based Prostate Testing for Cancer and Treatment study. We investigated by meta-analysis the effects of eight polymorphisms: MTHFR C677T (rs1801133; 12 studies; 10,745 cases; 40,158 controls), MTHFR A1298C (rs1801131; 5 studies; 3,176 cases; 4,829 controls), MTR A2756G (rs1805087; 8 studies; 7,810 cases; 37,543 controls), MTRR A66G (rs1801394; 4 studies; 3,032 cases; 4,515 controls), MTHFD1 G1958A (rs2236225; 6 studies; 7,493 cases; 36,941 controls), SLC19A1/RFC1 G80A (rs1051266; 4 studies; 6,222 cases; 35,821 controls), SHMT1 C1420T (rs1979277; 2 studies; 2,689 cases; 4,110 controls), and FOLH1 T1561C (rs202676; 5 studies; 6,314 cases; 35,190 controls). The majority (10 of 13) of eligible studies had 100% Caucasian subjects; only one study had <90% Caucasian subjects. We found weak evidence of dominant effects of two alleles: MTR 2756A>G [random effects pooled odds ratio, 1.06 (1.00-1.12); P = 0.06 (P = 0.59 for heterogeneity across studies)] and SHMT1 1420C>T [random effects pooled odds ratio, 1.11 (1.00-1.22); P = 0.05 (P = 0.38 for heterogeneity across studies)]. We found no effect of MTHFR 677C>T or any of the other alleles in dominant, recessive or additive models, or in comparing a/a versus A/A homozygous. Neither did we find any difference in effects on advanced or localized cancers. Our meta-analysis suggests that known common folate-pathway single nucleotide polymorphisms do not have significant effects on susceptibility to prostate cancer.(Cancer Epidemiol Biomarkers Prev 2009;18(9):2528–39)
- folate-pathway
- one-carbon metabolism
- prostate cancer
- meta-analysis
Introduction
There is a growing body of evidence that MTHFR and other folate-pathway genes are associated with some cancers, most notably colorectal and gastric cancers (1-3). The putative mechanisms behind these associations are manifold because folate and related metabolites, including vitamins B6 and B12, are fundamental to DNA synthesis, repair, and methylation (Fig. 1; ref. 4). Aberrant DNA methylation in particular seems to play a role in prostate cancer (5, 6). The mechanisms are complex and interdependent, and their possible roles in cancer etiology are likely to be complicated by interactions with dietary intakes and by the different stages of tumorogenesis (7).
Simplified overview of folate-mediated one-carbon metabolism, highlighting enzymes with polymorphisms investigated in this study. Ovals, enzymes; oblongs, substrates; CBS, cystathionine beta synthase; DHF, dihydrofalate; FOLH1, folate hydrolase 1; MTHFD1, methylene tetrahydrofolate dehydrogenase; MTHFR, methylene tetrahydrofolate reductase; MTR, methionine synthase; SAH, S-adenosylhomocysteine; SAM, S-adenosylmethionine; MTRR, methionine synthase reductase; SHMT1, serine hydroxymethyltransferase 1; SLC19A1, solute carrier family 19; THF, tetrahydrofolate.
The majority of dietary intake and blood level studies have indicated no association with prostate cancer risk of folate, vitamin B6, methionine, and homocysteine (8-16). A recent study reported a positive association between folic acid supplementation and prostate cancer risk (17). However, the same study suggested an inverse association with baseline plasma folate, which is consistent with two earlier studies that reported inverse associations with folate intake (11) and plasma folate (18). By contrast, there is some evidence that vitamin B12 intake and blood levels are positively associated with risk of prostate cancer (8, 10, 13, 14).
Here, we have done a meta-analysis of associations between polymorphisms in the folate metabolic pathway and prostate cancer using data obtained from the UK population–based Prostate Testing for Cancer and Treatment (ProtecT) study, from studies identified by systematic review of the literature, and from genome-wide association studies. Our aim was to determine whether the folate metabolic pathway has a role in prostate cancer. Food fortification and use of dietary supplements make this a research question imperative to public health.
Materials and Methods
Candidate Genes
Candidate genes were identified by reviewing published literature and public access metabolic pathway and gene-disease association databases. We reviewed published studies that had investigated associations of folate and one-carbon metabolic pathway genetic variants with disease and with blood levels of folate and related metabolites (homocysteine, methionine, B6, and B12).
Systematic Review and Extraction of Published Data
Eligible studies were identified by searching the Medline and Embase databases using text search terms for the folate-pathway genes: “MTHFR,” “MTR/MS,” “MTRR/MSR,” “MTHFD1,” “SLC19A1”/“RFC,” “SHMT1,” “FOLH1,” “CBS,” “FUT2,” and “TCN2”; or their full names, e,g., “methylenetetrahydrofolate reductase”; or single nucleotide polymorphism (SNP) identifiers, e.g., “rs1801133,” “rs1801131,” “rs1805087,” “rs1801394,” “rs2236225,” “rs1051266,” “rs1979277,” “rs202676,” “rs492602,” and “rs1801198”; each in conjunction with the MeSH term “prostatic neoplasm” and text terms “prostate cancer” and “prostatic carcinoma.” No language or publication date restrictions were imposed. HuGENet was also searched for genetic associations with prostatic diseases. All databases were last searched on 19/12/2008. References of retrieved articles were screened. Nonfamilial case-control studies were eligible in which genotype frequencies had been determined by molecular methods among cancer cases and prostate cancer–free controls. Where available, we also extracted genotype frequencies by cancer stage and grade, i.e., advanced/high-risk (stage III/IV or Gleason of ≥7/8) or localized/low-risk cases (stage I/II and Gleason of <7/8). Studies in which controls had Benign Prostatic Hyperplasia (BPH) or which included female controls were accepted. Data were extracted independently by two investigators.
Data from Genome-Wide Association Studies
Eligible genome-wide association studies were identified by searching the Medline and Embase databases using text search terms for “gene/genetic/genome-wide association” in conjunction with the MeSH term “prostatic neoplasm” and text terms “prostate cancer” and “prostatic carcinoma.” Genotype count data were obtained via public access data portals or by submitting requests for data via the principal investigators.
Data from the ProtecT Study
Study Population
The aim of the ProtecT study is to evaluate, in a randomized controlled trial, treatments for localized prostate cancer. Recruitment to the study occurred between 2001 and 2008. Men ages 50 to 69 y in general practices located around nine UK cities were invited to attend a nurse-led prostate check clinic and, if they consented, to have a Prostate-Specifc Antigen (PSA) test. Participants with a single raised PSA test result between 3.0 and 19.9 ng/mL were invited to attend the center's urology department for digital rectal examination, repeat PSA test, and 10-core transrectal ultrasound-guided biopsy. Men with clinically localized disease were eligible to participate in the treatment trial. Men with a PSA level of ≥20 ng/mL were referred as a matter of urgency to a urologist, and were eligible to participate in the treatment trial only if localized cancer was confirmed. A diagnosis of localized prostate cancer was defined as a positive biopsy, clinical stage T1-T2, NX, M0; advanced prostate cancer was defined as positive biopsy, clinical stage T3-T4 or N1 or M1. All men provided written informed consent. Trent Multicentre Research Ethics Committee approved the study.
DNA Extraction and Genotyping
In an allied (nested case-control) study, blood samples from 1,600 men with and 1,855 men without prostate cancer who had consented to DNA extraction and analysis were genotyped for 72 candidate gene polymorphisms, including 10 folate pathway genes. DNA was extracted by Tepnel Life Sciences PLC and genotyping was done by KBiosciences Ltd. using their own competitive allele-specific PCR (KASPar) and Taqman. Samples that produced seven or more genotype failures were considered to have poor DNA quality (2.5% of samples) and were discarded. Genotyping was carried out in duplicate in approximately 1 in 10 of the samples; of these repeats, 98.1% were in exact agreement.
Statistical Methods
We tested whether genotype frequencies of controls were in Hardy-Weinberg Equilibrium (HWE) using the Pearson χ2 test. We calculated unadjusted odds ratios of prostate cancer risk from genotype counts using all cases versus controls, and odds ratios of advanced and localized prostate cancer defined by cancer stage and/or grade: advanced (stage III/IV or Gleason of ≥7/8); localized (stage I/II and Gleason of <7/8). We estimated effects in four models: dominant [(a/a | a/A) versus A/A], recessive [a/a versus (a/A | A/A)], a/a homozygous versus A/A homozygous, and additive [in which genotype was coded A/A = 0 (reference), a/A = 1, a/a = 2]. Meta-analysis was by fixed (Mantel-Haenszel) and random effects models, the latter using the method of DerSimonian and Laird (19). Heterogeneity was quantified by the χ2-based Q statistic and the I2 measure of percentage between-studies variation attributable to heterogeneity. Small-study bias was assessed by inspection of funnel plots and tested by the Egger meta-regression method (20). Differences in effects on advanced and localized cancer subgroups were assessed using the χ2-based Q statistic (test for heterogeneity). All statistical analyses were done using Stata Release 10 (StataCorp. 2007. Stata Statistical Software: Release 10).
Results
Eligible Studies
Our systematic review identified nine published studies (Table 1) of which eight were eligible (21-28) and one family study was excluded (29). Unpublished data were obtained from four genome-wide association studies (30-33) and from the ProtecT study (Table 2). The majority (10 of 13) of eligible studies had 100% Caucasian subjects; of the remainder, only one study had <90% Caucasian subjects (23). Six studies recruited prostate cancer cases from hospitals (21, 23, 25-27, 33), five from prospective longitudinal cohorts or cancer registries (22, 24, 28, 30, 32), and the CGEMS (31) and ProtecT studies by population-based PSA testing. Two of the hospital case-control studies recruited men with BPH to the control group (23, 27). ProtecT and four other studies (24, 25, 28, 31) purposively matched controls on age and at least one other characteristic. The mean age of cases and controls was reported to be different in one study (23). Two studies included women among their control groups (21, 30), which affects only the power of the study if genotype frequencies are not associated with sex (verified by the Icelandic study group). The Icelandic study reported an inflation factor (1.12) due to relatedness among cases and controls (30). ProtecT controls that had also been used in the UK Genetic Prostate Cancer Study (33) were removed from the ProtecT control group when both studies were included in a meta-analysis.
Studies identified by systematic review of the literature
Sources of unpublished genotype frequency data
Genotype Frequencies
Genotype counts and frequencies for all cases and controls and for advanced and localized cases are presented in Supplementary Tables S1 and S2, respectively. There was strong evidence (P < 0.001) of a departure from HWE among the control group in the Icelandic study for rs2236225 and rs202676, and marginal evidence (0.03 < P < 0.08) among the control groups in three studies for rs1801133 (23, 24, 26), in one study for rs1805087 (28), in the ProtecT study for rs1979277, and in the Framingham Heart Study SNP Health Association Resource study for rs202676.
Meta-analysis
MTHFR C677T (rs1801133)
Twelve eligible studies included data from 10,745 cases and 40,158 controls. The T allele was not associated with risk of prostate cancer in dominant or recessive modeling, in comparing T/T versus C/C genotypes, or in an additive (per allele) model (Table 3; Fig. 2). There was substantial between-study heterogeneity in the recessive and T/T versus C/C models. In sensitivity analyses, exclusion of the three studies (Marchal, Reljic, deCODE) that showed weak evidence of departure from HWE among the control group (respectively, P = 0.03, P = 0.05, and P = 0.07) yielded pooled (random effects) odds ratios [95% confidence interval (95% CI)] for the dominant, recessive, T/T versus C/C, and additive models of the following: 1.05 (0.99-1.11), P = 0.14 (P = 0.53 for heterogeneity across studies); 1.11 (0.93-1.34), P = 0.26 (P = 0.01 for heterogeneity across studies); 1.12 (0.95-1.33), P = 0.18 (P = 0.05 for heterogeneity across studies); and 1.05 (1.00-1.11), P = 0.05 (P = 0.34 for heterogeneity across studies), respectively. For the recessive and T/T versus C/C models, the pooled fixed-effects odds ratios (95% CI) with these exclusions were, respectively: 1.13 (1.02-1.24), P = 0.02; and 1.13 (1.02-1.26), P = 0.02.
Meta-analysis results (all cases vs controls)
Association of MTHFR C677T with risk of prostate cancer: additive model comparing T/T, C/T versus C/C genotypes [ES, effect size (per allele odds ratio); I-V overall, inverse-variance fixed effects estimate; D+L overall, DerSimonian and Laird random effects estimate].
In a meta-analysis based on the seven studies that provided genotype count data for advanced (n = 2,959) and localized (n = 3,763) prostate cancer cases (seven studies; 6,002 controls), we found no differences in effects of the T allele on these two subgroups (Table 4). However, in comparing T/T versus C/C genotypes of MTHFR, the pooled (random effects) odds ratio for advanced cancer was 1.14 (0.98-1.33), P = 0.09 (P = 0.70 for heterogeneity across studies). Excluding the Marchal study, for which there was some evidence of Hardy Weinberg disequilibrium (P = 0.03), changed this odds ratio to 1.16 (95% CI, 0.99-1.35), P = 0.07 (P = 0.83 for heterogeneity across studies).
Meta-analysis results (advanced or localized cases vs controls)
MTR A2756G (rs1805087)
Eight eligible studies included data from 7,810 cases and 37,543 controls. We found weak evidence that the G allele was associated with increased risk of prostate cancer in dominant and additive models (Table 3; Fig. 3), but not in a recessive model or in comparing G/G versus A/A genotypes. There was no heterogeneity in any of the models. Exclusion of the Stevens study (HWE P = 0.03) yielded pooled odds ratios for the dominant and additive models of 1.06 (0.99-1.13), P = 0.10, and 1.05 (0.99-1.11), P = 0.09, respectively. We found no differences in effects of the G allele on advanced (n = 1,780) and localized (n = 2,187) cancers (5 studies; 4,414 controls), although dominant (Fig. 4) and additive effects were evident for the localized, but not for the advanced, subgroup: dominant effect pooled odds ratio, 1.16 (1.04-1.29); P = 0.008 (P = 0.79 for heterogeneity across studies); additive effect pooled odds ratio, 1.10 (1.01-1.21); P = 0.03 (P = 0.77 for heterogeneity across studies; Table 4). These effects were slightly weakened if the Stevens study was excluded [dominant effect pooled odds ratio, 1.14 (1.01-1.30); P = 0.04; additive effect pooled odds ratio, 1.10 (0.99-1.23); P = 0.07].
Association of MTR A2756G with risk of prostate cancer: additive model comparing G/G, A/G versus A/A genotypes [ES, effect size (per allele odds ratio); I-V overall, inverse-variance fixed effects estimate; D+L overall, DerSimonian and Laird random effects estimate].
Association of MTR A2756G with risk of localized and advanced prostate cancer: dominant model comparing (G/G and A/G) versus A/A genotypes (I-V, inverse-variance fixed effects estimate; D+L, DerSimonian and Laird random effects estimate].
SHMT1 C1420T (rs1979277)
Two eligible studies included data from 2,689 cases and 4,110 controls. We found weak evidence that the T allele was associated with increased risk of prostate cancer in dominant and additive models (Table 3), but not in a recessive model or in comparing T/T versus C/C genotypes. There was no heterogeneity in any of the models. We found no differences in effects of the T allele on advanced (n = 960) and localized (n = 1,540) cancers (2 studies; 4,110 controls), although effects were evident for the localized, but not for the advanced, subgroup in a recessive model [pooled odds ratio, 1.20 (95% CI, 1.00-1.45); P = 0.05 (P = 0.57 for heterogeneity across studies)], an additive model [pooled odds ratio, 1.10 (95% CI 1.01-1.20); P = 0.04 (P = 0.96 for heterogeneity across studies)], and in comparing T/T versus C/C genotypes [pooled odds ratio, 1.24 (95% CI, 1.02-1.51); P = 0.03 (P = 0.43 for heterogeneity across studies; Table 4)].
Null Findings
We found no associations with prostate cancer in dominant, recessive, or additive modeling, or in comparing homozygous genotypes for MTHFR A1298G (rs1801131), MTRR A66G (rs1801394), MTHFD1 G1958A (rs2236225), SLC19A1 G80A (rs1051266), and FOLH1 T1561C (rs202676; Table 3). Neither did we find any differential association with advanced versus localized prostate cancer (Table 4) for these alleles. For MTHFD1 G1958A and FOLH1 T1561C, exclusion of the studies that had evidence of departure from HWE made no difference to these null findings.
Bias Diagnostics
We found no evidence of small-study bias for measures of effect (dominant, recessive, a/a versus A/A) derived from genotype count data (all cases versus controls) for any of the SNPs. Neither did we detect any small-study bias for the two SNPs (rs1801133, rs1801131) for which more than three studies had been published. For rs1805087, there was weak evidence of a trend with study size toward a protective effect of the 2756G allele in recessive modeling (Egger's test, P = 0.07) and in comparing a/a versus A/A genotypes (Egger's test, P = 0.05). However, these apparent trends were based on only three studies [Kimura et al. (21), Marchal et al. (27), and Stevens et al. (28)]. The Kimura and Stevens studies (21, 28) reported null findings for this SNP, and the Marchal study (27) reported only that the 2756G allele was more common in advanced cases (χ2 test, P = 0.02). We found no differences in pooled measures of effect from published versus unpublished (genome-wide and ProtecT) studies.
Other Folate-Pathway Genes
Two of the studies identified by systematic review had investigated cystathionine β synthase (CBS) polymorphisms in relation to prostate cancer risk: Kimura et al. (21), the 844ins68 polymorphism; and Stevens et al. (28), the C699T (rs234706) and C1261T (rs1801181) polymorphisms. Neither study found any association with risk of prostate cancer. The ProtecT study genotyped polymorphisms rs492602 in the FUT2 gene and rs1801198 in the TCN2 gene, the homozygous G/G variants of which are associated with altered vitamin B12 metabolism (34, 35). In the ProtecT data, there were no associations between these alleles (rs492602 HWE in controls P = 0.7; rs1801198 HWE in controls P = 0.4) and prostate cancer in dominant modeling [rs492602 odds ratio, 1.06 (95% CI, 0.92-1.22); rs1801198 odds ratio, 1.04 (95% CI, 0.91-1.19)] or in comparing G/G versus A/A genotypes [rs492602 odds ratio, 1.04 (95% CI, 0.87-1.23); rs1801198 odds ratio, 1.15 (95% CI, 0.97-1.37)]. In recessive modeling, TCN2 rs1801198 showed a positive association [odds ratio, 1.15 (95% CI, 0.99-1.34); P = 0.06], and FUT2 rs492602 showed no association [odds ratio, 0.99 (95% CI, 0.86-1.14)]. There were no differences in effects of these alleles on advanced and localized cancer subgroups. Stevens et al. (28) reported no associations for a further 31 SNPs in 9 one-carbon metabolic pathway genes.
Gene-Gene and Gene-Nutrient Interactions
Four of the included studies assessed haplotype effects on prostate cancer: one study found no pairwise interactions between MTHFR C677T, MTR A2756G, and CBS 844ins68 (21); two studies found no interaction between MTHFR C677T and MTHFR A1298C (23, 24); and one study found that the MTR 2756 A/A genotype strengthened slightly the protective effect of the MTHFR 677 T/T genotype, but this was reported without a statistical test of interaction (27). Only one of the included studies assessed modification of SNP genotype effects on prostate cancer by nutrients, reporting an increased risk of prostate cancer associated with the highest versus lowest plasma folate levels in men with the MTHFR 677 C/T or T/T genotypes but not in men with the C/C genotype; no interactions with plasma levels of homocysteine or B12 were found (24).
Discussion
Our meta-analysis suggests that the methionine synthase (MTR) A2756G and serine hydroxymethlytransferase-1 (SHMT1) C1420T single-nucleotide polymorphisms are associated with increased risk of prostate cancer. The MTR 2756G allele was associated with 6% higher odds of prostate cancer [eight studies, pooled odds ratio, 1.06 (1.00-1.12); P = 0.06] and 16% higher odds of localized prostate cancer [five studies; pooled odds ratio, 1.16 (1.04-1.29); P = 0.008] in a dominant model (comparing G/A and G/G genotypes with the A/A genotype). The SHMT1 1420T allele was associated with 11% higher odds of prostate cancer [two studies; pooled odds ratio, 1.11 (1.00-1.22); P = 0.05], also in a dominant model. This allele was also associated with 20% higher odds of localized prostate cancer in a recessive model [two studies; pooled odds ratio, 1.20 (1.00-1.45); P = 0.05) and with 24% higher odds of localized prostate cancer in a model comparing T/T versus C/C genotypes [two studies; pooled odds ratio, 1.24 (1.02-1.51); P = 0.03]. We found no convincing evidence that other known folate-pathway polymorphisms, including MTHFR C677T, were associated with prostate cancer risk.
MTR A2756G (rs1805087)
Methionine synthase (MTR) catalyzes the demethylation of 5-methyl-tetrahydrofolate to tetrahydrofolate and the remethylation (using the methyl group donated by 5-methyl-tetrahydrofolate) of homocysteine to methionine (Fig. 1). MTR is therefore a key enzyme linking the folate and methionine cycles within the one-carbon metabolic pathway (36). Vitamin B12 (cobalamin) is an essential cofactor of MTR, acting in its methyl-cobalamin(III) form as an intermediate carrier of the donated methyl group. After transferring this methyl group to homocysteine, the resultant cobalamin(I) form is either remethylated by MTR or oxidized to an inactive cobalamin (II) form (37). Cobalamin (II) is remethylated to the active methyl-cobalamin(III) form by the enzyme methionine synthase reductase (MTRR) using a methyl group donated by S-adenosylmethionine (AdoMet). The A2756G polymorphism occurs in the activation domain of MTR (containing the binding site for AdoMet) in close proximity to the adjacent cobalamin-binding domain (38).
Evidence from dietary and blood-level studies suggests that B12 is positively associated with risk of prostate cancer (8, 10, 13, 14). Our results would be consistent with this evidence if the MTR A2756G polymorphism were to encode an enzyme whose activity was equivalent to elevated intake or plasma levels of B12. Given that the polymorphism occurs in the activation domain of MTR, and has been associated with depleted levels of homocysteine (39-43), this “activating polymorphism” hypothesis is not implausible (39). In a rat model, deficient B12 intake was associated with low MTR activity, and these factors together were associated with DNA hypomethylation (44). It is conceivable that high B12 or hyperactive MTR could be associated with DNA hypermethylation, hence increased prostate cancer risk through methylation of tumor suppressor genes (6).
Evidence of associations of MTR A2756G with other cancers is inconsistent. Reduced risk of colorectal (45-47) and breast (48) cancer, and reduced (49, 50) or increased (51-53) risk of hematopoietic malignancies have been reported, but the majority of studies have reported no associations.
Some of the studies that investigated associations between MTR A2756G and cancer or adenoma risk reported interactions with intakes of folate, methionine and B vitamins (including B12; refs. 46, 48, 54, 55), and alcohol (45, 46, 55), and with other folate-pathway gene polymorphisms (46, 49, 55-58). With regard to interactions with B12 status, one study reported increased risk of colorectal cancer associated with the MTR A2756G polymorphism in men with the lowest versus highest B12 intakes (54), another a protective effect of the MTR A2756G polymorphism against breast cancer only in women with the highest B12 intakes (48).
Our finding (from a meta-analysis of data from 7,810 cases and 37,543 controls in eight studies) of an increased risk of prostate cancer associated with the MTR A2756G polymorphism warrants further research, to include investigations of phenotypic effects (particularly on plasma homocysteine and B12), haplotypic effects, and interactions with dietary intakes of folate, B vitamins, and alcohol.
SHMT1 C1420T (rs1979277)
Our finding that the serine hydroxymethyltransferase-1 (SHMT1) C1420T polymorphism is associated with increased risk of prostate cancer should be interpreted with some caution because our meta-analysis included only two studies. SHMT1 catalyzes the conversion of tetrahydrofolate to 5,10-methylenetetrahydrofolate and serine to glycine, a reversible reaction that requires vitamin B6 as a cofactor (Fig. 1). SHMT1 exists in the cytoplasm; an isoenzyme SHMT2 catalyzes the same reactions in the mitochondrion. The SHMT1 1420T allele was associated with elevated plasma and RBC levels of folate in one (59) but not another study (60). In studies of associations with cancer risk, the SHMT1 1420T allele was reported to be associated with reduced risk of acute lymphocytic leukemia (58) and malignant lymphoma (61), but null findings have been reported for colorectal (60), bladder (62), breast (48), lung (63), and ovarian cancers (64). In the absence of a phenotypic effect of SHMT1 C1420T, and given the generally null findings of dietary and blood level studies with regard to vitamin B6 (8, 9, 11, 13), our result could be attributable to chance. However, haplotypic approaches to analysis of SHMT gene-cancer associations have proven more fruitful (63, 64), and could be justified on the basis of our finding.
MTHFR C677T and other Folate-Pathway Gene Polymorphisms
That we found no associations of other folate-pathway gene polymorphisms with prostate cancer is consistent with the predominantly null findings from dietary and blood-level studies. Our null result for MTHFR C677T (based on a meta-analysis of 10,745 cases and 40,158 controls from 12 studies) is an important finding, given the considerable research interest in this important functional polymorphism and its associations with colorectal and gastric cancer (2). Our findings suggest that the aberrant DNA methylation implicated by epigenetic studies of prostate cancer cells is not caused primarily by genetically determined enzymatic defects in the folate metabolic pathway, and may differ from the methylation processes postulated for colorectal and gastric carcinogenesis. That we found no differential effects of folate-pathway gene polymorphisms on localized versus advanced prostate cancer suggests that the role, if any, of the folate metabolic pathway in prostatic carcinogenesis does not change as the cancer progresses. The localized versus advanced subgroup results for MTR A2756G and SHMT1 C1420T should be interpreted with some caution, given that the large deCODE study did not contribute data for MTR A2756G and the result for SHMT1 C1420T was based on only two studies. In neither case was there statistical evidence of heterogeneity between the subgroups.
Limitations
The included study groups were almost entirely Caucasian with little or no population variation in minor allele frequency; hence, subgroup meta-analyses by race were not possible. The only study that had a significant proportion (33%) of Black men found no difference in the effects of MTHFR C677T or A1298C by race, but the study was small (n = 123; ref. 23). We made no correction for the known relatedness of individuals in the deCODE study population, but a sensitivity analysis in the additive effects model showed that this correction would make no difference to our pooled measures of effect (due to the small inflation factor, large study size, and null findings from the deCODE study). The large size of the control group in the deCODE study would also have contributed to the departure from HWE for two SNPs in this study. These deviations would have had little or no effect on the null findings from this study for these two SNPs. The test for heterogeneity between subgroups may be invalid when there is substantial heterogeneity within subgroups, as observed within advanced and localized subgroups for some of the SNPs (Table 4). Gene effects may have been underestimated in studies that used patients with BPH as controls, but these control groups were used only by two of the smaller studies (23, 27). Only one of the included studies investigated interactions with dietary factors (24), a mechanism of effect modification that has been shown to be of importance in studies of other cancers. It is also possible that unknown folate-pathway polymorphisms could affect prostate cancer risk, and a tag SNP approach may yet yield evidence for the involvement of folate-pathway genetic variation in susceptibility to prostate cancer.
Our meta-analyses were based on very large numbers of prostate cancer cases and controls, and the inclusion of unpublished data from genome-wide association studies protected against publication bias. The observed or hypothetical phenotypic effects of folate-pathway single-nucleotide gene polymorphisms readily accommodate the two principal mechanisms postulated for a role of one-carbon metabolism in carcinogenesis, namely disturbances to DNA synthesis and methylation. Our study revealed potential effects of methionine synthase and serine hydroxymethyltransferase polymorphisms, the former consistent with observations that vitamin B12 may have a role in prostatic carcinogenesis. However, convincing evidence of associations of folate-pathway gene polymorphisms with prostate cancer has yet to emerge, suggesting either that different approaches, e.g., haplotype analyses for nonfunctional variants, are required, or that genetic variation in the folate metabolic pathway is not a detectable determinant of prostate cancer risk.
Disclosure of Potential Conflicts of Interest
Dr. R. Eeles receives educational support from Illumina Inc. and from Tepnel Life Sciences PLC. The other authors disclosed no potential conflicts of interest.
Acknowledgments
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
ProtecT study: We thank the tremendous contribution of all members of the ProtecT study research group, and especially the following who were involved in this research: Prasad Bollina, Sue Bonnington, Debbie Cooper, Andrew Doble, Alan Doherty, Emma Elliott, David Gillatt, Pippa Herbert, Peter Holding, Joanne Howson, Liz Down, Mandy Jones, Roger Kockelbergh, Howard Kynaston, Teresa Lennon, Norma Lyons, Hilary Moody, Philip Powell, Stephen Prescott, Liz Salter, and Pauline Thompson.
UKGPCS: The UK Genetic Prostate Cancer Study Group includes Lynne O'Brien, Rosemary Wilkinson, Amanda Hall, and Emma Sawyer. Rosalind Eeles is supported by a National Institute for Health Research grant to the Biomedical Research Centre at the Institute of Cancer Research and Royal Marsden NHS Foundation Trust. This work was supported by Cancer Research UK Grant C5047/A3354. Douglas Easton is a Principal Research Fellow of Cancer Research UK. We thank the following for funding support: The Institute of Cancer Research and The Everyman Campaign, The Prostate Cancer Research Foundation, Prostate Research Campaign UK, The National Cancer Research Network UK, The National Cancer Research Institute UK. We thank the collaboration of the UK Genetic Prostate Cancer Study Collaborators and The British Association of Urological Surgeons' Section of Oncology.
Framingham Heart Study: We thank Shih-Jen Hwang [The National Heart, Lung, and Blood Institute (NHLBI) Framingham Heart Study (FHS), Framingham, MA, and NHLBI Bethesda, MD], Joanne M Murabito (NHLBI's FHS, Framingham, MA, and Department of Medicine, Boston University School of Medicine, Boston, MA), and Carol L Rosenberg (Department of Medicine, Boston University School of Medicine, Boston, MA), for data from the FHS (NHLBI contract numbers N01-HC-25195, N02-HL-6-4278), and FHS investigators participating in the SNP Health Association Resource (SHARe)11 project for resource development. We thank the FHS participants for their ongoing participation and dedication to that study.
Others: We thank the CGEMS project for making their results available online to registered users,12 and to Dr. Victoria Stevens (American Cancer Society, Atlanta, GA) for providing additional data.
Footnotes
Grant support: World Cancer Research Fund UK (grant numbers 2004/18 and 2007/07), with additional support from the National Cancer Research Institute (administered by the Medical Research Council), which provided funding through the Prostate Mechanisms of Progression and Treatment collaborative. The Prostate Testing for Cancer and Treatment (ProtecT) study is funded by the UK National Institute for Health Research Health Technology Assessment Programme (projects 96/20/06, 96/20/99). Funding for the ProtecT biorepository in Cambridge is provided by National Institute for Health Research through the Biomedical Research Centre. DNA extraction in ProtecT was supported by U.S. Department of Defense award W81XWH-04-1-0280 and Yorkshire Cancer Research.
Note: Supplementary data for this article are available at Cancer Epidemiology Biomakers and Prevention Online (http://cebp.aacrjournals.org/).
The list of names of the members of the UK Genetic Prostate Cancer Study Group is available upon request.
↵11http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000007.v6.p3 accessed 10th July 2009.
↵12http://cgems.cancer.gov/data/ accessed 10th July 2009.
- Received March 9, 2009.
- Revision received June 9, 2009.
- Accepted July 9, 2009.
References
Volume 18, Issue 9
