Sporadic colorectal cancer (CRC) is characterized by genetic and epigenetic changes such as regional DNA hypermethylation and global DNA hypomethylation. Epidemiological and animal studies suggest that aberrant DNA methylation is associated with low dietary folate intake, which is aggravated by high alcohol intake. The relationship between promoter methylation of genes involved in CRC carcinogenesis and folate and alcohol intake was investigated. Methylation of the APC-1A, p14ARF, p16INK4A, hMLH1, O6-MGMT, and RASSF1A promoters was studied using methylation-specific PCR in 122 sporadic CRCs, derived from patients with folate and alcohol intake at either the lower or the higher quintiles of the distribution. Overall, promoter hypermethylation frequencies observed were: 39% for APC; 33% for p14ARF; 31% for p16INK4A; 29% for hMLH1; 41% for O6-MGMT; and 20% for RASSF1A. For each of the tested genes, the prevalence of promoter hypermethylation was higher in CRCs derived from patients with low folate/high alcohol intake (n = 61) when compared with CRCs from patients with high folate/low alcohol intake (n = 61), but the differences were not statistically significant. The number of CRCs with at least one gene methylated was higher (84%) in the low folate intake/high alcohol intake group when compared with the high folate intake/low alcohol intake group (70%; P = 0.085). Despite the size limitations of this study, these data suggest that folate and alcohol intake may be associated with changes in promoter hypermethylation in CRC.

In addition to hereditary components, known risk factors for CRC3 are related to lifestyle and environment such as smoking and a Western diet (high meat, energy, and alcohol intake and low fruit, vegetable, and fiber intake; Ref. 1). Although the majority of epidemiological studies point to fruit and vegetable intake as protective for CRC (2, 3, 4, 5, 6, 7, 8, 9, 10, 11), the molecular mechanisms for this protection remain to be clarified. It is hypothesized that folate, one of the vitamins mainly found in green leafy vegetables, is in part responsible for the inverse association with CRC risk. The effects of low folate intake are aggravated by high alcohol intake (7), probably because of degradation of folate in the colon by acetaldehyde, the first metabolite of alcohol (12).

Folate, as 5-methyltetrahydrofolate, has a central role in methyl metabolism. It supplies a methyl group to convert homocysteine to methionine, which is then converted to S-adenosylmethionine, the universal methyl donor for methylation of a wide variety of biological substrates such as DNA, RNA, and proteins. Folate deficiency is reported to be associated with the occurrence of point mutations in K-ras in colorectal adenomas (13) and carcinomas (14), single and double strand DNA breaks, chromosome breakage (15, 16), and global DNA hypomethylation (17).

Although the overall level of genomic methylation is actually reduced in certain tumor types, including CRCs (18, 19), hypermethylation at several gene promoters has also been reported. It has been hypothesized that global hypomethylation might induce regional de novo hypermethylation (20). On the other hand, a recent study by Bariol et al.(21) suggests that there is no relationship between global demethylation and regional hypermethylation in CRC.

To investigate whether altered DNA methylation is associated with folate and alcohol intake, we examined promoter methylation of genes that have been reported to be involved and methylated in CRC carcinogenesis, i.e., APC-1A, p14ARF, p16INK4A, hMLH1, O6-MGMT, and RASSF1A(22, 23). This was done using an optimized MSP method. This study was performed with 122 CRC archival specimens derived from incident cases who participate in the prospective NLCS (24). Patients with low folate intake in addition to high alcohol intake at baseline (n = 61) were compared with patients with high folate intake in addition to low alcohol intake (n = 61) with respect to the methylation status of a series of gene promoters.

Study Population and Sample Procurement.

The paraffin-embedded CRC samples used in this study were derived from patients who participate in NLCS. This study started in September 1986 and included 58,279 men and 62,573 women (55–69 years at baseline). At baseline, the cohort members completed a mailed, self-administered food frequency questionnaire on dietary habits and other risk factors for cancer. The study design has been described in detail elsewhere (24). Follow-up for incident cancer is established annually by computerized record linkage with all cancer registries in the Netherlands and with PALGA, a nationwide pathology database (25). Since 1989, the coverage of PALGA is 100% (26). Record linkage covering the period from 1989 up to the end of 1993 (7.3 years follow-up, with exclusion of the first 2.3 years of follow-up) resulted in 819 eligible incident, histologically confirmed CRC patients. After approval by the Medical Ethical Committee of the Maastricht University and PALGA, tissue samples were collected from 54 pathology laboratories throughout the Netherlands. Tumor tissue sample collection was started in August 1999 and was completed in December 2001.

For this pilot study, 61 CRCs of patients with high methyl donor intake [high folate intake (≥215 μg/day) in addition to extremely low alcohol intake (0–4 g/day)] and 61 CRCs of patients with low methyl donor intake [folate intake (<215 μg/day) in addition to high alcohol intake (≥5 g/day)] were selected (for selection criteria and definitions; Table 1). Among the selected patients were 68 males and 54 females (58–76 years of age at time of diagnosis), 30 Dukes A, 39 Dukes B, 31 Dukes C, and 15 Dukes D CRCs. Dukes stage was not reported in 7 cases. The distribution of the location of the tumors was colon (n = 84), rectosigmoid (n = 14), and rectum (n = 24). The 122 patients in this study were representative for the complete group of eligible CRC patients with respect to age, gender, Dukes stage, and tumor location distribution.

After identification by a pathologist (A. P. d. B.), genomic DNA of tumor cells was microdissected and extracted using proteinase K (Qiagen) and the Puregene DNA Isolation Kit (Gentra Systems) and stored at 4°C. Part of this set of samples has previously been characterized for RASSF1A promoter methylation (23).

Food Frequency Questionnaire.

At the start of the NLCS in 1986, all participants completed a food frequency questionnaire on daily food consumption and potential confounders (e.g., smoking, occupation, physical activity, family history of cancer, drug use). The dietary section of the questionnaire, a 150-item semiquantitative food frequency questionnaire, concentrated on habitual consumption of food and beverages during the year preceding the start of the study. The questionnaire was validated against a 9-day dietary record. Daily mean nutrient intakes are calculated using the computerized Dutch food composition table. Folate intake was calculated from newly established liquid chromatography data for foods (27).

Questionnaire data of all cases and the subcohort are key-entered twice and processed in a manner blinded with respect to case/subcohort status to minimize observer bias in coding and interpreting the data. Both procedures make use of a highly standardized protocol. Subjects with incomplete or inconsistent dietary data are excluded from data analysis, according to criteria described in detail elsewhere (28). Patients with low folate intake in addition to high alcohol intake were defined as the low methyl donor group, whereas patients with high folate intake in addition to low alcohol intake were defined as the high methyl donor group (Table 1). Dietary factors adjusted for in data analyses are chosen on the basis of a previous analysis on the association between folate and CRC (2).

Promoter Methylation Analysis.

DNA methylation in the CpG islands of the APC-1A, p14ARF, p16INK4a, hMLH1, O6-MGMT, and RASSF1A gene promoters was determined by chemical modification of genomic DNA with sodium bisulfite and subsequent MSP as described in detail elsewhere (29). In brief, 1 μg of DNA was denatured by NaOH and modified by sodium bisulfite. DNA samples were then purified using Wizard DNA purification resin (Promega), again treated with NaOH, precipitated with ethanol, and resuspended in H2O.

To facilitate MSP analysis on DNA retrieved from formalin-fixed, paraffin-embedded tissue, DNA was first amplified with flanking PCR primers that amplify bisulfite-modified DNA but do not preferentially amplify methylated or unmethylated DNA. The resulting fragment was used as a template for the MSP reaction.

All PCRs were performed with controls for unmethylated alleles (DNA from normal lymphocytes), methylated alleles [normal lymphocyte DNA treated in vitro with SssI methyltransferase (New England Biolabs)], and a control without DNA. Primer sequences and PCR conditions are described in Table 2. Ten μl of each MSP reaction were directly loaded onto nondenaturing 6% polyacrylamide gels, stained with ethidium bromide, and visualized under UV illumination. To study the reproducibility of the nested MSP approach, duplicate CRC specimens were analyzed. Reproducibility was 95%.

Data Analysis.

Frequencies of promoter methylation of specific genes and frequencies and means of other variables in patients with high and low methyl donor intake were computed. Differences in frequencies of variables were tested using the χ2 test, and differences in mean levels of variables were tested using the t test. Logistic regression analyses were performed to test whether there was a difference in methyl donor intake (low versus high) between CRCs with at least one gene methylated versus no genes methylated while adjusting for potential confounders. Because nine categories of methyl-donor intake were distinguished (categories 1 through 9 in Table 1), although only based on extreme levels of intake, logistic regression analyses were also performed including this variable instead of the dichotomous intake variable in the model. A P of 0.05 was considered to be statistically significant. All tests of statistical significance were two-sided. Data analysis was done using SPSS software (version 9.0).

To enable MSP analysis on DNA retrieved from formalin-fixed, paraffin-embedded tumor tissue, nested MSP was performed. Nested MSP analysis could easily be performed for APC-1A, O6-MGMT, and RASSF1A, genes for which the designed MSP amplicons are relatively small, i.e., <165 bp (see Table 2). However, using the flanking MSP primers originally designed for p14ARF, p16INK4a, and hMLH1(30), the success rate for amplification decreased with increasing size of the MSP amplicons. Bisulfite-treated DNA could not be amplified for p14ARF (207 bp) in 18% (22 of 121), for p16INK4a (193 bp) in 12% (14 of 120), and for hMLH1 (182 bp) in 16% (20 of 123) of CRC cases. To increase the MSP success rate, MSP primers for these promoter regions were redesigned to obtain shorter amplicons (<155 bp). Using these primer sets, a subgroup of CRC cases (n = 52), which did and which did not amplify using the original MSP primer sets, were amplified using the redesigned MSP primers. The MSP success rate increased to 94% (49 of 52) for p14ARF, 90% (47 of 52) for p16INK4a, and 100% (52 of 52) for hMLH1. The concordance between the original and the redesigned short primer sets was tested and was found to be 94% (31 of 33) for p16INK4a, 94% (30 of 32) for hMLH1, and 93% (25 of 27) for p14ARF. Because of the rate of concordance between the different primer sets, data using both sets of primers were pooled. Genes that showed methylation using only one of both primer sets were considered as methylated.

Overall, promoter hypermethylation frequencies observed were: 39% (47 of 122) for APC-1A; 33% (40 of 120) for p14ARF; 31% (37 of 119) for p16INK4A; 29% (35 of 122) for hMLH1; 41% (50 of 121) for O6-MGMT; and 20% (24 of 122) for RASSF1A (Table 3). Representative examples of the MSP reactions are shown in Fig. 1.

For all genes, the prevalence of promoter hypermethylation was higher in CRCs derived from patients with low methyl donor intake when compared with CRCs from patients with high methyl donor intake, however, none of these individual differences reached statistical significance. The percentage of CRCs with at least one gene methylated versus no gene methylated was higher (84%) in the low methyl donor intake group when compared with the adequate/high methyl donor intake group (70%; P = 0.085; Table 3).

Dukes stage and location of the tumor did not differ between the two groups of patients (Table 3). Age at diagnosis was higher in patients with low methyl donor intake. There were significantly more males in the low methyl donor intake group (67%) when compared with the high methyl donor intake group (44%; P = 0.011; Table 3). Factors previously shown to be associated with folate intake, i.e., energy-, fiber-, vitamin C-, and iron intake (2), were all significantly lower in patients with low methyl donor intake compared with patients with adequate/high methyl donor intake (Table 3).

Table 4 summarizes the Dukes stage, location of the tumor, age at diagnosis and energy-, fiber-, vitamin C-, and iron intake, none of which are associated with promoter methylation. Family history of cancer frequency was higher (25%) in patients who had none of the six studied genes methylated, compared with patients with at least one gene methylated (13%), but the difference did not reach statistical significance (Table 4). Table 4 also shows that gender is significantly associated with promoter methylation (P = 0.046). Sixty-one percent of patients were male in the group with at least one gene methylated versus 39% in the group with no genes methylated (Table 4). Caution is warranted in interpretation because these results are based on a selection of extreme intakes.

The OR for promoter methylation of at least one gene methylated was 2.13 (95% CI, 0.89–5.11) for patients with low methyl donor intake versus high methyl donor intake. Adjustment for age, sex, energy-, fiber-, vitamin C-, and iron intake and family history of cancer revealed an OR of 1.82 (95% CI, 0.49–6.78). When the methyl donor intake variable with nine categories was modeled in a similar way, a positive association with promoter methylation of at least one gene was still observed, although not statistically significant (unadjusted OR, 1.14, P = 0.124; adjusted OR, 1.12, P = 0.420).

A better understanding of the causality of CRC can be established by combining epidemiology and research on molecular mechanisms. Here, this approach was used to study whether dietary intake of folate and alcohol is associated with hypermethylation of tumor suppressor- and DNA repair genes in CRC specimens derived from the NLCS. Nested MSP analysis was optimized, and as a result, almost all CRC specimens, irrespective of the quality of the DNA, could be analyzed for hypermethylation in the promoter regions of APC-1A, p14ARF, p16INK4A, hMLH1, O6-MGMT, and RASSF1A. This optimized nested MSP approach enables high throughput promoter methylation analysis in archival, formalin-fixed and paraffin-embedded tissues for molecular epidemiology studies.

For p14ARF, p16INK4A, and O6-MGMT, the overall prevalence of promoter methylation, i.e., 33, 31, and 41%, respectively, are in the range of the prevalence reported for CRC (22, 31, 32). For RASSF1A, the prevalence of promoter methylation was similar to the frequency reported on a larger series of CRCs from the same cohort (23). For APC-1A and hMLH1, the overall prevalence is higher than reported thus far (30, 33). This difference might be because the CRC material used in this study is obtained from patients from a different geographic area (the Netherlands) compared with the patient material used in the other studies (United States).

Although not significant, our results indicate that methyl donor deficiency is associated with overall methylation of multiple genes, an effect which is stronger than the effect on specific promoter regions itself. The association, although not statistically significant, between the nine different categories of methyl donor intake and promoter methylation of at least one gene supports this. However, these results have to be interpreted with caution because only extreme categories were used, and intermediate categories of methyl donor intake in this pilot study were omitted. In addition, stratification of patients was done primarily on folate intake and secondarily on alcohol intake. Therefore, the conclusions of this study are based primarily on folate intake. Other dietary factors of importance for methyl donor intake (e.g., vitamins B6 and B12) were not accounted for in this selection of patients.

Increasing the power of the study, including CRCs from patients with the complete spectrum of folate and alcohol intake and analysis of the effect of intake of other methyl donors such as vitamins B6 and B12, will reveal whether the observed effects of methyl donor deficiency is stable. In addition, it is necessary to study whether there is effect modification by alcohol (as suggested with overall CRC in the First National Health and Nutrition Examination Survey study; Ref. 7) in the complete group of CRCs derived from the NLCS. Another drawback of this study is that only case-case and not case-cohort analyses were performed. Knowledge on global hypomethylation status, which is hypothesized to occur previously to regional hypermethylation, would also be interesting to analyze with respect to promoter hypermethylation. In addition, it is possible that the observed effect of folate deficiency on promoter methylation will be stronger after stratification for functionally important SNPs in genes involved in folate metabolism. Methylenetetrahydrafolate reductase, methionine synthase, and methionine synthase reductase are interesting enzymes involved in the generation of S-adenosylmethionine, the primary methyl donor for DNA methylation reactions. All three enzymes have been reported to have common SNPs, which are associated with enhanced thermolability and decreased enzyme activity. The effect of the SNPs on colorectal and other cancer risks also seems to be dependent on methionine, folate, vitamins B6 and B12, and alcohol intake (34, 35, 36, 37, 38, 39, 40).

In conclusion, despite its limited size, this study suggests that methyl donor intake is associated with an increased frequency of promoter hypermethylation of genes involved in CRC carcinogenesis.

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.

1

Supported by the Dutch Cancer Society and the René Vogels Foundation.

3

The abbreviations used are: CRC, colorectal cancer; APC, adenomatous polyposis coli; hMLH1, human mut-L homologue; O6-MGMT, O6-methylguanine DNA methyl transferase; NLCS, Netherlands Cohort Study on Diet and Cancer; MSP, methylation-specific PCR; OR, odds ratio; CI, confidence interval; SNP, single nucleotide polymorphism; PALGA, Pathologisch Anatomisch Landelijk Geautomatiseerd Archief.

Fig. 1.

Representative examples of APC-1A-, O6-MGMT-, and hMLH1-nested MSP reactions of six primary CRCs and controls. The presence of a visible PCR product in those lanes marked U indicates the presence of unmethylated alleles; the presence of product in those lanes marked M indicates the presence of methylated alleles. All CRCs include amplification with the U primer set, probably a result of the presence of normal, contaminating tissue. Normal lymphocytes (NL) and in vitro methylated DNA (IVD) were used as negative and positive controls for APC-1A, O6-MGMT, and hMLH1 promoter methylation, respectively. The H2O control was included in the flanking PCR and subsequently in the MSP reaction.

Fig. 1.

Representative examples of APC-1A-, O6-MGMT-, and hMLH1-nested MSP reactions of six primary CRCs and controls. The presence of a visible PCR product in those lanes marked U indicates the presence of unmethylated alleles; the presence of product in those lanes marked M indicates the presence of methylated alleles. All CRCs include amplification with the U primer set, probably a result of the presence of normal, contaminating tissue. Normal lymphocytes (NL) and in vitro methylated DNA (IVD) were used as negative and positive controls for APC-1A, O6-MGMT, and hMLH1 promoter methylation, respectively. The H2O control was included in the flanking PCR and subsequently in the MSP reaction.

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Table 1

Selection of CRC patients based on extreme intakes of folate and alcohol intake (quintile distribution)

Methyl donor group (dichotome)Quintile combinationaMethyl donor categoryFolate intake (μg/day)Alcohol intake (g/day)n
Low folate/high alcohol (n = 61) F1/A5 0–157 ≥30 
 F1/A4 0–157 15–29 14 
 F1/A3 0–157 5–14 15 
 F2/A5 158–186 ≥30 16 
 F2/A4 158–186 15–29 
 F3/A5 187–215 ≥30 
High folate/low alcohol group (n = 61) F4/A1 215–255 19 
 F5/A2 ≥255 0.1–4 24 
 F5/A1 ≥255 18 
Methyl donor group (dichotome)Quintile combinationaMethyl donor categoryFolate intake (μg/day)Alcohol intake (g/day)n
Low folate/high alcohol (n = 61) F1/A5 0–157 ≥30 
 F1/A4 0–157 15–29 14 
 F1/A3 0–157 5–14 15 
 F2/A5 158–186 ≥30 16 
 F2/A4 158–186 15–29 
 F3/A5 187–215 ≥30 
High folate/low alcohol group (n = 61) F4/A1 215–255 19 
 F5/A2 ≥255 0.1–4 24 
 F5/A1 ≥255 18 
a

Quintiles of folate (F) and alcohol (A) intake; i.e., F1/A5 is first quintile of folate and fifth quintile of alcohol.

Table 2

MSP primer sequences and PCR conditions

Primer setSense primerAntisense primerAmp Size (bp)Annealing temperature (°C)No. of PCR cycles
APC-1A flank 5′-TGGGYGGGGTTTTGTGTTTTATT-3′ 5′-TACRCCCACACCCAACCAATC-3′ 136 56 35 
APC-1A Ua 5′-GTGTTTTATTGTGGAGTGTGGGTT-3′ 5′-CCAATCAACAAACTCCCAACAA-3′ 108 60 25 
APC-1A M 5′-TATTGCGGAGTGCGGGTC-3′ 5′-TCGACGAACTCCCGACGA-3′ 98 60 25 
p14ARF flank 5′-TTTAGTTTGTAGTTAAGGGGGTAGGAG-3′ 5′-CRCTACCCACTCCCCCRTAAACC-3′ 207 56 35 
p14ARF flank short 5′-GYGTTGTTTATTTTTGGTGTTAAAGG-3′ 5′-AAATATAAACCACRAAAACCCTCACT-3′ 152 56 35 
p14ARF5′-TTTTTGGTGTTAAAGGGTGGTGTAGT-3′ 5′-CACAAAAACCCTCACTCACAACAA-3′ 132 60 25 
p14ARF5′-GTGTTAAAGGGCGGCGTAGC-3′ 5′-AAAACCCTCACTCGCGACGA-3′ 122 60 25 
p16INK4a flank 5′-AGAAAGAGGAGGGGTTGGTTGG-3′ 5′-ACRCCCRCACCTCCTCTACC-3′ 193 56 35 
p16INK4a flank short 5′-GGGTTGGTTGGTTATTAGAGGGT-3′ 5′-RACCRTAACCAACCAATCAACC-3′ 148 56 35 
p16INK4a5′-TTATTAGAGGGTGGGGTGGATTGT-3′ 5′-CAACCCCAAACCACAACCATAA-3′ 151 60 30 
p16INK4a U short 5′-GTTGGTTATTAGAGGGTGGGGTGGATTGT-3′ 5′-AACCAAAAACTCCATACTACTCCCCACCA-3′ 124 62 25 
p16INK4a5′-TTATTAGAGGGTGGGGCGGATCGC-3′ 5′-GACCCCGAACCGCGACCGTAA-3′ 150 60 30 
p16INK4a M short 5′-TTATTAGAGGGTGGGGCGGATCGC-3′ 5′-GAAAACTCCATACTACTCCCCGCCG-3′ 115 62 25 
O6-MGMT flank 5′-GYGTTTYGGATATGTTGGGATAGTT-3′ 5′-AAACTCCRCACTCTTCCRAAAAC-3′ 135 56 35 
O6-MGMT5′-TTTGTGTTTTGATGTTTGTAGGTTTTTGT-3′ 5′-AACTCCACACTCTTCCAAAAACAAAACA-3′ 93 60 30 
O6-MGMT5′-TTTCGACGTTCGTAGGTTTTCGC-3′ 5′-GCACTCTTCCGAAAACGAAACG-3′ 81 60 30 
hMLH1 flank 5′-GGAGTGAAGGAGGTTAYGGGTAAGT-3′ 5′-AAAAACRATAAAACCCTATACCTAATCTATC-3′ 182 56 35 
hMLH1 flank short 5′-TTTTGAYGTAGAYGTTTTATTAGGGT-3′ 5′-AAAACRATAAAACCCTATACCTAATCTATC-3′ 154 56 35 
hMLH15′-TTTTGATGTAGATGTTTTATTAGGGTTGT-3′ 5′-ACCACCTCATCATAACTACCCACA-3′ 124 60 35 
hMLH1 U short 5′-TGTGTGTTTGTTGTTTGTTATATATTGTTT-3′ 5′-ACCACCTCATCATAACTACCCACA-3′ 98 60 35 
hMLH15′-ACGTAGACGTTTTATTAGGGTCGC-3′ 5′-CCTCATCGTAACTACCCGCG-3′ 115 60 35 
hMLH1 M short 5′-GTTCGTCGTTCGTTATATATCGTTC-3′ 5′-CCTCATCGTAACTACCCGCG-3′ 89 60 35 
RASSF1A flank 5′-GTTTAGTTTGGATTTTGGGGGAG-3′ 5′-CCCRCAACTCAATAAACTCAAACTC-3′ 144 56 35 
RASSF1A5′-GGGGTTTGTTTTGTGGTTTTGTTT-3′ 5′-AACATAACCCAATTAAACCCATACTTCA-3′ 81 60 30 
RASSF1A5′-GGGTTCGTTTTGTGGTTTCGTTC-3′ 5′-TAACCCGATTAAACCCGTACTTCG-3′ 76 60 30 
Primer setSense primerAntisense primerAmp Size (bp)Annealing temperature (°C)No. of PCR cycles
APC-1A flank 5′-TGGGYGGGGTTTTGTGTTTTATT-3′ 5′-TACRCCCACACCCAACCAATC-3′ 136 56 35 
APC-1A Ua 5′-GTGTTTTATTGTGGAGTGTGGGTT-3′ 5′-CCAATCAACAAACTCCCAACAA-3′ 108 60 25 
APC-1A M 5′-TATTGCGGAGTGCGGGTC-3′ 5′-TCGACGAACTCCCGACGA-3′ 98 60 25 
p14ARF flank 5′-TTTAGTTTGTAGTTAAGGGGGTAGGAG-3′ 5′-CRCTACCCACTCCCCCRTAAACC-3′ 207 56 35 
p14ARF flank short 5′-GYGTTGTTTATTTTTGGTGTTAAAGG-3′ 5′-AAATATAAACCACRAAAACCCTCACT-3′ 152 56 35 
p14ARF5′-TTTTTGGTGTTAAAGGGTGGTGTAGT-3′ 5′-CACAAAAACCCTCACTCACAACAA-3′ 132 60 25 
p14ARF5′-GTGTTAAAGGGCGGCGTAGC-3′ 5′-AAAACCCTCACTCGCGACGA-3′ 122 60 25 
p16INK4a flank 5′-AGAAAGAGGAGGGGTTGGTTGG-3′ 5′-ACRCCCRCACCTCCTCTACC-3′ 193 56 35 
p16INK4a flank short 5′-GGGTTGGTTGGTTATTAGAGGGT-3′ 5′-RACCRTAACCAACCAATCAACC-3′ 148 56 35 
p16INK4a5′-TTATTAGAGGGTGGGGTGGATTGT-3′ 5′-CAACCCCAAACCACAACCATAA-3′ 151 60 30 
p16INK4a U short 5′-GTTGGTTATTAGAGGGTGGGGTGGATTGT-3′ 5′-AACCAAAAACTCCATACTACTCCCCACCA-3′ 124 62 25 
p16INK4a5′-TTATTAGAGGGTGGGGCGGATCGC-3′ 5′-GACCCCGAACCGCGACCGTAA-3′ 150 60 30 
p16INK4a M short 5′-TTATTAGAGGGTGGGGCGGATCGC-3′ 5′-GAAAACTCCATACTACTCCCCGCCG-3′ 115 62 25 
O6-MGMT flank 5′-GYGTTTYGGATATGTTGGGATAGTT-3′ 5′-AAACTCCRCACTCTTCCRAAAAC-3′ 135 56 35 
O6-MGMT5′-TTTGTGTTTTGATGTTTGTAGGTTTTTGT-3′ 5′-AACTCCACACTCTTCCAAAAACAAAACA-3′ 93 60 30 
O6-MGMT5′-TTTCGACGTTCGTAGGTTTTCGC-3′ 5′-GCACTCTTCCGAAAACGAAACG-3′ 81 60 30 
hMLH1 flank 5′-GGAGTGAAGGAGGTTAYGGGTAAGT-3′ 5′-AAAAACRATAAAACCCTATACCTAATCTATC-3′ 182 56 35 
hMLH1 flank short 5′-TTTTGAYGTAGAYGTTTTATTAGGGT-3′ 5′-AAAACRATAAAACCCTATACCTAATCTATC-3′ 154 56 35 
hMLH15′-TTTTGATGTAGATGTTTTATTAGGGTTGT-3′ 5′-ACCACCTCATCATAACTACCCACA-3′ 124 60 35 
hMLH1 U short 5′-TGTGTGTTTGTTGTTTGTTATATATTGTTT-3′ 5′-ACCACCTCATCATAACTACCCACA-3′ 98 60 35 
hMLH15′-ACGTAGACGTTTTATTAGGGTCGC-3′ 5′-CCTCATCGTAACTACCCGCG-3′ 115 60 35 
hMLH1 M short 5′-GTTCGTCGTTCGTTATATATCGTTC-3′ 5′-CCTCATCGTAACTACCCGCG-3′ 89 60 35 
RASSF1A flank 5′-GTTTAGTTTGGATTTTGGGGGAG-3′ 5′-CCCRCAACTCAATAAACTCAAACTC-3′ 144 56 35 
RASSF1A5′-GGGGTTTGTTTTGTGGTTTTGTTT-3′ 5′-AACATAACCCAATTAAACCCATACTTCA-3′ 81 60 30 
RASSF1A5′-GGGTTCGTTTTGTGGTTTCGTTC-3′ 5′-TAACCCGATTAAACCCGTACTTCG-3′ 76 60 30 
a

U, unmethylated DNA specific primers; M, methylated DNA specific primers.

Table 3

Prevalence of promoter methylation, clinicopathological parameters and other patient characteristics for the total number of CRCs and for the high and low methyl donor intake groups

Total (n = 122)Methyl donor intakeP
High (n = 61)Low (n = 61)
Methylation     
 Promoter methylation     
  APC 47/122 (39%) 20/61 (33%) 27/61 (44%) 0.193 
  p14ARF 40/120 (33%) 17/60 (28%) 23/60 (38%) 0.245 
  p16INK4 37/119 (31%) 18/59 (31%) 19/60 (32%) 0.891 
  hMLH1 35/122 (29%) 17/61 (28%) 18/61 (30%) 0.841 
  O6-MGMT 50/121 (41%) 22/61 (36%) 28/60 (47%) 0.236 
  RASSF1A 24/122 (20%) 9/61 (15%) 15/61 (25%) 0.172 
 Number of genes methylated     
  0 28/122 (23%) 18/61 (30%) 10/61 (16%)  
  1 25/122 (20%) 12/61 (20%) 13/61 (21%)  
  2 29/122 (24%) 13/61 (21%) 16/61 (26%)  
  3 18/122 (15%) 8/61 (13%) 10/61 (16%)  
  4 16/122 (13%) 9/61 (15%) 7/61 (12%)  
  5 4/122 (3%) 1/61 (2%) 3/61 (5%)  
  6 2/122 (2%) 0/61 (0%) 2/61 (3%) 0.411 
 At least 1 gene methylated 94/122 (77%) 43/61 (70%) 51/61 (84%) 0.085 
Clinicopathological parameters     
 Dukes stagea     
  A 30 (26%) 17 (29%) 13 (23%)  
  B 39 (34%) 18 (30%) 21 (37%)  
  C 31 (27%) 13 (22%) 18 (32%)  
  D 15 (13%) 11 (19%) 4 (7%) 0.19 
 Location of tumor     
  Colon 84 (69%) 45 (74%) 39 (64%)  
  Rectosigmoid 14 (11%) 4 (7%) 10 (16%)  
  Rectum 24 (20%) 12 (20%) 12 (20%) 0.223 
 Proximal location (yes)b 75 (62%) 36 (60%) 39 (64%) 0.498 
Other patient characteristics     
 Age at diagnosis (yr) 67.4 66.7 68.1 0.086 
 Gender (male) 68 (56%) 27 (44%) 41 (67%) 0.011 
 Family history of CRC (yes) 19 (16%) 9 (15%) 10 (16%) 0.803 
 Dietary factors     
  Energy intake (kcal) 1951 2105 1797 0.002 
  Fiber intake (g) 27 32.9 21.2 0.019 
  Vitamin C intake (mg) 107 135.1 79 0.001 
  Iron intake (mg) 12.7 14.6 10.8 0.022 
Total (n = 122)Methyl donor intakeP
High (n = 61)Low (n = 61)
Methylation     
 Promoter methylation     
  APC 47/122 (39%) 20/61 (33%) 27/61 (44%) 0.193 
  p14ARF 40/120 (33%) 17/60 (28%) 23/60 (38%) 0.245 
  p16INK4 37/119 (31%) 18/59 (31%) 19/60 (32%) 0.891 
  hMLH1 35/122 (29%) 17/61 (28%) 18/61 (30%) 0.841 
  O6-MGMT 50/121 (41%) 22/61 (36%) 28/60 (47%) 0.236 
  RASSF1A 24/122 (20%) 9/61 (15%) 15/61 (25%) 0.172 
 Number of genes methylated     
  0 28/122 (23%) 18/61 (30%) 10/61 (16%)  
  1 25/122 (20%) 12/61 (20%) 13/61 (21%)  
  2 29/122 (24%) 13/61 (21%) 16/61 (26%)  
  3 18/122 (15%) 8/61 (13%) 10/61 (16%)  
  4 16/122 (13%) 9/61 (15%) 7/61 (12%)  
  5 4/122 (3%) 1/61 (2%) 3/61 (5%)  
  6 2/122 (2%) 0/61 (0%) 2/61 (3%) 0.411 
 At least 1 gene methylated 94/122 (77%) 43/61 (70%) 51/61 (84%) 0.085 
Clinicopathological parameters     
 Dukes stagea     
  A 30 (26%) 17 (29%) 13 (23%)  
  B 39 (34%) 18 (30%) 21 (37%)  
  C 31 (27%) 13 (22%) 18 (32%)  
  D 15 (13%) 11 (19%) 4 (7%) 0.19 
 Location of tumor     
  Colon 84 (69%) 45 (74%) 39 (64%)  
  Rectosigmoid 14 (11%) 4 (7%) 10 (16%)  
  Rectum 24 (20%) 12 (20%) 12 (20%) 0.223 
 Proximal location (yes)b 75 (62%) 36 (60%) 39 (64%) 0.498 
Other patient characteristics     
 Age at diagnosis (yr) 67.4 66.7 68.1 0.086 
 Gender (male) 68 (56%) 27 (44%) 41 (67%) 0.011 
 Family history of CRC (yes) 19 (16%) 9 (15%) 10 (16%) 0.803 
 Dietary factors     
  Energy intake (kcal) 1951 2105 1797 0.002 
  Fiber intake (g) 27 32.9 21.2 0.019 
  Vitamin C intake (mg) 107 135.1 79 0.001 
  Iron intake (mg) 12.7 14.6 10.8 0.022 
a

For 7 cases, Dukes stage is not reported.

b

For 1 case, proximal/distal location was not determined.

Table 4

Clinicopathological parameters and other patient characteristics in CRCs with no genes versus at least one gene methylated

No genes methylated (n = 28)At least one gene methylated (n = 94)P
Clinicopathological parameters    
 Dukes stage    
  A 7/26 (27%) 23/89 (26%)  
  B 10/26 (39%) 29/89 (33%)  
  C 4/26 (15%) 27/89 (30%)  
  D 5/26 (19%) 10/89 (11%) 0.412 
 Location of tumor    
  Colon 19/28 (68%) 65/94 (69%)  
  Rectosigmoid 2/28 (7%) 11/94 (12%)  
  Rectum 7/28 (25%) 18/94 (19%) 0.675 
 Proximal location (yes) 19/28 (68%) 56/93 (60%) 0.465 
Other patient characteristics    
 Age at diagnosis (yr) 67.8 67.3 0.581 
 Gender (male) 11/28 (39%) 57/94 (61%) 0.046 
 Family history of cancer (yes) 7/28 (25%) 12/94 (13%) 0.117 
 Dietary factors    
  Energy intake (kcal) 1937 1955 0.854 
  Fiber intake (g) 28.6 26.6 0.274 
  Vitamin C intake (mg) 115 104 0.339 
  Iron intake (mg) 13.2 12.6 0.368 
No genes methylated (n = 28)At least one gene methylated (n = 94)P
Clinicopathological parameters    
 Dukes stage    
  A 7/26 (27%) 23/89 (26%)  
  B 10/26 (39%) 29/89 (33%)  
  C 4/26 (15%) 27/89 (30%)  
  D 5/26 (19%) 10/89 (11%) 0.412 
 Location of tumor    
  Colon 19/28 (68%) 65/94 (69%)  
  Rectosigmoid 2/28 (7%) 11/94 (12%)  
  Rectum 7/28 (25%) 18/94 (19%) 0.675 
 Proximal location (yes) 19/28 (68%) 56/93 (60%) 0.465 
Other patient characteristics    
 Age at diagnosis (yr) 67.8 67.3 0.581 
 Gender (male) 11/28 (39%) 57/94 (61%) 0.046 
 Family history of cancer (yes) 7/28 (25%) 12/94 (13%) 0.117 
 Dietary factors    
  Energy intake (kcal) 1937 1955 0.854 
  Fiber intake (g) 28.6 26.6 0.274 
  Vitamin C intake (mg) 115 104 0.339 
  Iron intake (mg) 13.2 12.6 0.368 

We thank Marco M. M. Pachen for technical assistance and Jasper E. Manning and Kam-Wing Jair for helpful suggestions. We also thank Sacha H. M. van de Crommert, Willy van Dijk, Marijke I. G. Moll, Jolanda J. H. Nelissen, Conny W. C. de Zwart, Harry P. L. van Montfort, and Ruud J. G. C. Schmeitz for data input and data management.

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