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Single Nucleotide Polymorphisms of One-Carbon Metabolism and Cancers of the Esophagus, Stomach, and Liver in a Chinese Population

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      Abstract

      One-carbon metabolism (folate metabolism) is considered important in carcinogenesis because of its involvement in DNA synthesis and biological methylation reactions. We investigated the associations of single nucleotide polymorphisms (SNPs) in folate metabolic pathway and the risk of three GI cancers in a population-based case-control study in Taixing City, China, with 218 esophageal cancer cases, 206 stomach cancer cases, 204 liver cancer cases, and 415 healthy population controls. Study participants were interviewed with a standardized questionnaire, and blood samples were collected after the interviews. We genotyped SNPs of the MTHFR, MTR, MTRR, DNMT1, and ALDH2 genes, using PCR-RFLP, SNPlex, or TaqMan assays. To account for multiple comparisons and reduce the chances of false reports, we employed semi-Bayes (SB) shrinkage analysis. After shrinkage and adjusting for potential confounding factors, we found positive associations between MTHFR rs1801133 and stomach cancer (any T versus C/C, SB odds-ratio [SBOR]: 1.79, 95% posterior limits: 1.18, 2.71) and liver cancer (SBOR: 1.51, 95% posterior limits: 0.98, 2.32). There was an inverse association between DNMT1 rs2228612 and esophageal cancer (any G versus A/A, SBOR: 0.60, 95% posterior limits: 0.39, 0.94). In addition, we detected potential heterogeneity across alcohol drinking status for ORs relating MTRR rs1801394 to esophageal (posterior homogeneity P = 0.005) and stomach cancer (posterior homogeneity P = 0.004), and ORs relating MTR rs1805087 to liver cancer (posterior homogeneity P = 0.021). Among non-alcohol drinkers, the variant allele (allele G) of these two SNPs was inversely associated with the risk of these cancers; while a positive association was observed among ever-alcohol drinkers. Our results suggest that genetic polymorphisms related to one-carbon metabolism may be associated with cancers of the esophagus, stomach, and liver. Heterogeneity across alcohol consumption status of the associations between MTR/MTRR polymorphisms and these cancers indicates potential interactions between alcohol drinking and one-carbon metabolic pathway.

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      Molecular mechanisms of alcohol-mediated carcinogenesis.

      Approximately 3.6% of cancers worldwide derive from chronic alcohol drinking, including those of the upper aerodigestive tract, the liver, the colorectum and the breast. Although the mechanisms for alcohol-associated carcinogenesis are not completely understood, most recent research has focused on acetaldehyde, the first and most toxic ethanol metabolite, as a cancer-causing agent. Ethanol may also stimulate carcinogenesis by inhibiting DNA methylation and by interacting with retinoid metabolism. Alcohol-related carcinogenesis may interact with other factors such as smoking, diet and comorbidities, and depends on genetic susceptibility.
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        • Record: found
        • Abstract: found
        • Article: not found

        Quantifying biases in causal models: classical confounding vs collider-stratification bias.

        It has long been known that stratifying on variables affected by the study exposure can create selection bias. More recently it has been shown that stratifying on a variable that precedes exposure and disease can induce confounding, even if there is no confounding in the unstratified (crude) estimate. This paper examines the relative magnitudes of these biases under some simple causal models in which the stratification variable is graphically depicted as a collider (a variable directly affected by two or more other variables in the graph). The results suggest that bias from stratifying on variables affected by exposure and disease may often be comparable in size with bias from classical confounding (bias from failing to stratify on a common cause of exposure and disease), whereas other biases from collider stratification may tend to be much smaller.
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          • Record: found
          • Abstract: found
          • Article: not found

          Identifiability and exchangeability for direct and indirect effects.

          We consider the problem of separating the direct effects of an exposure from effects relayed through an intermediate variable (indirect effects). We show that adjustment for the intermediate variable, which is the most common method of estimating direct effects, can be biased. We also show that even in a randomized crossover trial of exposure, direct and indirect effects cannot be separated without special assumptions; in other words, direct and indirect effects are not separately identifiable when only exposure is randomized. If the exposure and intermediate never interact to cause disease and if intermediate effects can be controlled, that is, blocked by a suitable intervention, then a trial randomizing both exposure and the intervention can separate direct from indirect effects. Nonetheless, the estimation must be carried out using the G-computation algorithm. Conventional adjustment methods remain biased. When exposure and the intermediate interact to cause disease, direct and indirect effects will not be separable even in a trial in which both the exposure and the intervention blocking intermediate effects are randomly assigned. Nonetheless, in such a trial, one can still estimate the fraction of exposure-induced disease that could be prevented by control of the intermediate. Even in the absence of an intervention blocking the intermediate effect, the fraction of exposure-induced disease that could be prevented by control of the intermediate can be estimated with the G-computation algorithm if data are obtained on additional confounding variables.
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            Author and article information

            Affiliations
            [1 ]Department of Epidemiology, University of California Los Angeles Fielding School of Public Health, Los Angeles, CA, United States of America
            [2 ]Department of Social and Preventive Medicine, State University of New York at Buffalo, Buffalo, NY, United States of America
            [3 ]Department of Epidemiology, Fujian Medical University, Fuzhou, Fujian, China
            [4 ]Department of Epidemiology, Fudan University School of Public Health, Shanghai, China
            [5 ]Center for Human Nutrition, University of California Los Angeles David Geffen School of Medicine, Los Angeles, CA, United States of America
            [6 ]Department of Epidemiology, Peking University School of Public Health, Beijing, China
            [7 ]Department of Statistics, University of California Los Angeles College of Letters and Science, Los Angeles, CA, United States of America
            University of Arizona, United States of America
            Author notes

            Competing Interests: The authors have declared that no competing interests exist.

            Conceived and designed the experiments: SCC LM LC SZY QYL ZFZ. Performed the experiments: SCC BYG LM NCYY AB. Analyzed the data: SCC PYC SG ZFZ. Contributed reagents/materials/analysis tools: DH. Wrote the paper: SCC. Interpreted the results: SCC PYC LL SG ZFZ. Participated in manuscript writing process: BB LM SZY QYL LL SG ZFZ.

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, USA )
            1932-6203
            2014
            22 October 2014
            : 9
            : 10
            25337902
            4206280
            PONE-D-14-05560
            10.1371/journal.pone.0109235
            (Editor)

            This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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            Pages: 16
            Funding
            This work is supported in part by the International Union Against Cancer (UICC) Technology Transfer fellowship (ICRETT) awarded to Dr. Li-Na Mu and by the Foundation for the Author of National Excellent Doctoral Dissertation of P.R. China, No. 200157, awarded to Dr. Lin Cai. The study was also partially supported by the NIH National Institute of Environmental Health Sciences, National Cancer Institute, Department of Health and Human Services, grants ES06718, ES 011667, CA09142, as well as the Alper Research Program of Environmental Genomics of the UCLA Jonsson Comprehensive Cancer Center and the UCLA Clinical Nutrition Research Unit. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
            Categories
            Research Article
            Biology and Life Sciences
            Genetics
            Medicine and Health Sciences
            Epidemiology
            Gastroenterology and Hepatology
            Oncology

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