Dataset Information

Alcohol, one-carbon nutrient intake, and risk of colorectal cancer according to tumor methylation level of IGF2 differentially methylated region.

ABSTRACT: Although a higher consumption of alcohol, which is a methyl-group antagonist, was previously associated with colorectal cancer risk, mechanisms remain poorly understood.We hypothesized that excess alcohol consumption might increase risk of colorectal carcinoma with hypomethylation of insulin-like growth factor 2 (IGF2) differentially methylated region-0 (DMR0), which was previously associated with a worse prognosis.With the use of a molecular pathologic epidemiology database in 2 prospective cohort studies, the Nurses' Health Study and Health Professionals Follow-up Study, we examined the association between alcohol intake and incident colorectal cancer according to the tumor methylation level of IGF2 DMR0. Duplication-method Cox proportional cause-specific hazards regression for competing risk data were used to compute HRs and 95% CIs. In addition, we investigated intakes of vitamin B-6, vitamin B-12, methionine, and folate as exposures.During 3,206,985 person-years of follow-up, we identified 993 rectal and colon cancer cases with an available tumor DNA methylation status. Compared with no alcohol consumption, the consumption of ?15 g alcohol/d was associated with elevated risk of colorectal cancer with lower levels of IGF2 DMR0 methylation [within the first and second quartiles: HRs of 1.55 (95% CI: 1.08, 2.24) and 2.11 (95% CI: 1.44, 3.07), respectively]. By contrast, alcohol consumption was not associated with cancer with higher levels of IGF2 DMR0 methylation. The association between alcohol and cancer risk differed significantly by IGF2 DMR0 methylation level (P-heterogeneity = 0.006). The association of vitamin B-6, vitamin B-12, and folate intakes with cancer risk did not significantly differ according to IGF2 DMR0 methylation level (P-heterogeneity > 0.2).Higher alcohol consumption was associated with risk of colorectal cancer with IGF2 DMR0 hypomethylation but not risk of cancer with high-level IGF2 DMR0 methylation. The association between alcohol intake and colorectal cancer risk may differ by tumor epigenetic features.

SUBMITTER: Nishihara R

PROVIDER: S-EPMC4232016 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Alcohol, one-carbon nutrient intake, and risk of colorectal cancer according to tumor methylation level of IGF2 differentially methylated region.

Nishihara Reiko R Wang Molin M Qian Zhi Rong ZR Baba Yoshifumi Y Yamauchi Mai M Mima Kosuke K Sukawa Yasutaka Y Kim Sun A SA Inamura Kentaro K Zhang Xuehong X Wu Kana K Giovannucci Edward L EL Chan Andrew T AT Fuchs Charles S CS Ogino Shuji S Schernhammer Eva S ES

The American journal of clinical nutrition 20141008 6

<h4>Background</h4>Although a higher consumption of alcohol, which is a methyl-group antagonist, was previously associated with colorectal cancer risk, mechanisms remain poorly understood.<h4>Objective</h4>We hypothesized that excess alcohol consumption might increase risk of colorectal carcinoma with hypomethylation of insulin-like growth factor 2 (IGF2) differentially methylated region-0 (DMR0), which was previously associated with a worse prognosis.<h4>Design</h4>With the use of a molecular p ...[more]

PMID: 25411283

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Project description:Deoxyribonucleic acid (DNA) methylation is an epigenetic alteration crucial for regulating stress responses. Identifying large-scale DNA methylation at single nucleotide resolution is made possible by whole genome bisulfite sequencing. An essential task following the generation of bisulfite sequencing data is to detect differentially methylated cytosines (DMCs) among treatments. Most statistical methods for DMC detection do not consider the dependency of methylation patterns across the genome, thus possibly inflating type I error. Furthermore, small sample sizes and weak methylation effects among different phenotype categories make it difficult for these statistical methods to accurately detect DMCs. To address these issues, the wavelet-based functional mixed model (WFMM) was introduced to detect DMCs. To further examine the performance of WFMM in detecting weak differential methylation events, we used both simulated and empirical data and compare WFMM performance to a popular DMC detection tool methylKit. Analyses of simulated data that replicated the effects of the herbicide glyphosate on DNA methylation in Arabidopsis thaliana show that WFMM results in higher sensitivity and specificity in detecting DMCs compared to methylKit, especially when the methylation differences among phenotype groups are small. Moreover, the performance of WFMM is robust with respect to small sample sizes, making it particularly attractive considering the current high costs of bisulfite sequencing. Analysis of empirical Arabidopsis thaliana data under varying glyphosate dosages, and the analysis of monozygotic (MZ) twins who have different pain sensitivities-both datasets have weak methylation effects of <1%-show that WFMM can identify more relevant DMCs related to the phenotype of interest than methylKit. Differentially methylated regions (DMRs) are genomic regions with different DNA methylation status across biological samples. DMRs and DMCs are essentially the same concepts, with the only difference being how methylation information across the genome is summarized. If methylation levels are determined by grouping neighboring cytosine sites, then they are DMRs; if methylation levels are calculated based on single cytosines, they are DMCs.

Project description:Hypermethylation of promoter CpG islands is strongly correlated to transcriptional gene silencing and epigenetic maintenance of the silenced state. As well as its role in tumor development, CpG island methylation contributes to the acquisition of resistance to chemotherapy. Differential Methylation Hybridisation (DMH) is one technique used for genome-wide DNA methylation analysis. The study of such microarray data sets should ideally account for the specific biological features of DNA methylation and the non-symmetrical distribution of the ratios of unmethylated and methylated sequences hybridised on the array. We have therefore developed a novel algorithm tailored to this type of data, Methylation Linear Discriminant Analysis (MLDA).MLDA was programmed in R (version 2.7.0) and the package is available at CRAN 1. This approach utilizes linear regression models of non-normalised hybridisation data to define methylation status. Log-transformed signal intensities of unmethylated controls on the microarray are used as a reference. The signal intensities of DNA samples digested with methylation sensitive restriction enzymes and mock digested are then transformed to the likelihood of a locus being methylated using this reference. We tested the ability of MLDA to identify loci differentially methylated as analysed by DMH between cisplatin sensitive and resistant ovarian cancer cell lines. MLDA identified 115 differentially methylated loci and 23 out of 26 of these loci have been independently validated by Methylation Specific PCR and/or bisulphite pyrosequencing.MLDA has advantages for analyzing methylation data from CpG island microarrays, since there is a clear rational for the definition of methylation status, it uses DMH data without between-group normalisation and is less influenced by cross-hybridisation of loci. The MLDA algorithm successfully identified differentially methylated loci between two classes of samples analysed by DMH using CpG island microarrays.

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Dataset Information

Alcohol, one-carbon nutrient intake, and risk of colorectal cancer according to tumor methylation level of IGF2 differentially methylated region.

Publications

Alcohol, one-carbon nutrient intake, and risk of colorectal cancer according to tumor methylation level of IGF2 differentially methylated region.

Similar Datasets

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