Gene-Metabolite Interaction in the One Carbon Metabolism Pathway: Predictors of Colorectal Cancer in Multi-Ethnic Families
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Population and Setting
2.2. Demographic Data
2.3. Genotyping and Matabolites Data
2.4. Data Analysis
3. Results
3.1. Characteristics of Study Participants
3.2. Most Influential Factors—The Ensemble Method
3.3. Predictive Modeling for Healthy Eating—Generalized Regression Analysis
4. Discussion
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Magalhães, B.; Peleteiro, B.; Lunet, N. Dietary patterns and colorectal cancer: Systematic review and meta-analysis. Eur. J. Cancer. Prev. 2012, 21, 15–23. [Google Scholar] [CrossRef] [PubMed]
- Fan, Y.; Jin, X.; Man, C.; Gao, Z.; Wang, X. Meta-analysis of the association between the inflammatory potential of diet and colorectal cancer risk. Oncotarget 2017, 8, 59592–59600. [Google Scholar] [CrossRef] [PubMed]
- Tárraga López, P.J.; Albero, J.S.; Rodríguez-Montes, J.A. Primary and secondary prevention of colorectal cancer. Clin. Med. Insights Gastroenterol. 2014, 7, 33–46. [Google Scholar] [CrossRef] [PubMed]
- Cavicchia, P.P.; Steck, S.E.; Hurley, T.G.; Hussey, J.R.; Ma, Y.; Ockene, I.S.; Hebert, J.R. A new dietary inflammatory index predicts interval changes in serum high-sensitivity C-reactive protein. J. Nutr. 2009, 139, 2365–2372. [Google Scholar] [CrossRef] [PubMed]
- Shivappa, N.; Steck, S.E.; Hurley, T.G.; Hussey, J.R.; Hebert, J.R. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr. 2014, 17, 1689–1696. [Google Scholar] [CrossRef] [PubMed]
- Johnson, C.M.; Wei, C.; Ensor, J.E.; Smolenski, D.J.; Amos, C.I.; Levin, B.; Berry, D.A. Meta-analyses of colorectal cancer risk factors. Cancer Causes Control. 2013, 24, 1207–1222. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Holden, D.J.; Harris, R.; Porterfield, D.S.; Jones, D.E.; Morgan, L.C.; Reuland, D.; Gilehrist, M.; Viswanathan, M.; Lohr, K.N.; Lynda-McDonald, B. Enhancing the Use of Quality of Colorectal Cancer Screening. Evidence Report-Technology Assessment No. 190; Agency for Health Care Research and Quality, Publication No. 10-E002; Agency for Healthcare Research and Quality: Rockville, MD, USA, 2010. [Google Scholar]
- Visser, A.; Vrieling, A.; Murugesu, L.; Hoogerbrugge, N.; Kampman, E.; Hoedjes, M. Determinants of adherence to recommendations for cancer prevention among Lynch Syndrome mutation carriers: A qualitative exploration. PLoS ONE 2017, 12, e0178205. [Google Scholar] [CrossRef] [PubMed]
- Campbell, P.T.; Curtin, K.; Ulrich, C.M.; Samowitz, W.S.; Bigler, J.; Velicer, C.M.; Caan, B.; Potter, J.D.; Slattery, M.L. Mismatch repair polymorphisms and risk of colon cancer, tumour microsatellite instability and interactions with lifestyle factors. Gut 2009, 58, 661–667. [Google Scholar] [CrossRef] [PubMed]
- Peyrin-Biroulet, L.; Rodriguez-Guéant, R.M.; Chamaillard, M.; Desreumaux, P.; Xia, B.; Bronowicki, J.P.; Bigard, M.A.; Guéant, J.L. Vascular and cellular stress in inflammatory bowel disease: Revisiting the role of homocysteine. Am. J. Gastroenterol. 2007, 102, 1108–1115. [Google Scholar] [CrossRef] [PubMed]
- Lazzerini, P.E.; Capecchi, P.L.; Selvi, E.; Lorenzini, S.; Bisogno, S.; Galeazzi, M.; Laghi Pasini, F. Hyperhomocysteinemia, inflammation and autoimmunity. Autoimmun. Rev. 2007, 6, 503–509. [Google Scholar] [CrossRef] [PubMed]
- Kennedy, D.A.; Stern, S.J.; Matok, I.; Moretti, M.E.; Sarkar, M.; Adams-Webber, T.; Koren, G. Folate intake, MTHFR polymorphisms, and the risk of colorectal cancer: A systematic review and meta-analysis. J. Cancer Epidemiol. 2012, 2012, 952508. [Google Scholar] [CrossRef] [PubMed]
- Shiao, S.P.K.; Yu, C.H. Meta-prediction of MTHFR gene polymorphism mutations and associated risks for colorectal cancer. Biol. Res. Nur. 2016, 18, 357–369. [Google Scholar] [CrossRef] [PubMed]
- Taioli, E.; Garza, M.A.; Ahn, Y.O.; Bishop, D.T.; Bost, J.; Budai, B.; Chen, K.; Gemignani, F.; Keku, T.; Lima, C.C.; et al. Meta- and pooled analyses of the methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism and colorectal cancer: A HuGE-GSEC review. Am. J. Epidemiol. 2009, 170, 1207–1221. [Google Scholar] [CrossRef] [PubMed]
- Zacho, J.; Yazdanyar, S.; Bojesen, S.E.; Tybjærg-Hansen, A.; Nordestgaard, B.G. Hyperhomocysteinemia, methylenetetrahydrofolate reductase c.677C>T polymorphism and risk of cancer: Cross-sectional and prospective studies and meta-analyses of 75,000 cases and 93,000 controls. Int. J. Cancer 2012, 128, 644–652. [Google Scholar] [CrossRef] [PubMed]
- Zhang, D.; Wen, X.; Wu, W.; Guo, Y.; Cui, W. Elevated homocysteine level and folate deficiency associated with increased overall risk of carcinogenesis: Meta-analysis of 83 case-control studies involving 35,758 individuals. PLoS ONE 2015, 10, e0123423. [Google Scholar] [CrossRef] [PubMed]
- Anderson, O.S.; Sant, K.E.; Dolinoy, D.C. Nutrition and epigenetics: An interplay of dietary methyl donors, one-carbon metabolism and DNA methylation. J. Nutr. Biochem. 2012, 23, 853–859. [Google Scholar] [CrossRef] [PubMed]
- Peng, H.Y.; Man, C.F.; Xu, J.; Fan, Y. Elevated homocysteine levels and risk of cardiovascular and all-cause mortality: A meta-analysis of prospective studies. J. Zhejiang Univ. Sci. B 2015, 16, 78–86. [Google Scholar] [CrossRef] [PubMed]
- Kim, Y.; Je, Y. Dietary fiber intake and total mortality: A meta-analysis of prospective cohort studies. Am. J. Epidemiol. 2014, 180, 565–573. [Google Scholar] [CrossRef] [PubMed]
- Xiao, Y.; Zhang, Y.; Lv, X.; Su, D.; Li, D.; Xia, M.; Qiu, J.; Ling, W.; Ma, J. Relationship between lipid profiles and plasma total homocysteine, cysteine and the risk of coronary artery disease in coronary angiographic subjects. Lipids Health Dis. 2011, 10, 137. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kim, M.; Long, T.I.; Arakawa, K.; Wang, R.; Yu, M.C.; Laird, P.W. DNA methylation as a biomarker for cardiovascular disease risk. PLoS ONE 2010, 5, e9692. [Google Scholar] [CrossRef] [PubMed]
- Maron, B.A.; Loscalzo, J. The treatment of hyperhomocysteinemia. Annu. Rev. Med. 2009, 60, 39–54. [Google Scholar] [CrossRef] [PubMed]
- Refsum, H.; Nurk, E.; Smith, A.D.; Ueland, P.M.; Gjesdal, C.G.; Bjelland, I.; Tverdal, A.; Tell, G.S.; Nygård, O.; Vollset, S.E. The Hordaland Homocysteine Study: A community-based study of homocysteine, its determinants, and associations with disease. J. Nutr. 2006, 136 (Suppl. 6), 1731S–1740S. [Google Scholar] [CrossRef] [PubMed]
- Sibani, S.; Leclerc, D.; Weisber, I.S.; O’Ferrall, E.; Watkins, D.; Artigas, C.; Rosenblatt, D.S.; Rozen, R. Characterization of mutations in severe methylenetetrahydrofolate reductase deficiency reveals an FAD-responsive mutation. Hum. Mutat. 2003, 21, 509–520. [Google Scholar] [CrossRef] [PubMed]
- McBride, C. Applications of Genomics to Improve Public Health (Lecture 12). National Human Genome Research Institute’s Current Topics in Genome Analysis 2012. Available online: http://www.genome.gov/Course2012/ (accessed on 27 February 2017).
- Wade, D.H.; McBride, C.M.; Kardia, S.L.R.; Brody, L.C. Considerations for designing a prototype genetic test for use in translational research. Public Health Genom. 2010, 13, 155–165. [Google Scholar] [CrossRef] [PubMed]
- Lissowska, J.; Gaudet, M.M.; Brinton, L.A.; Chanock, S.J.; Peplonska, B.; Welch, R.; Zatonski, W.; Szeszenia-Dabrowska, N.; Park, S.; Sherman, M.; Garcia-Closas, M. Genetic polymorphisms in the one-carbon metabolism pathway and breast cancer risk: A population-based case-control study and meta-analyses. Int. J. Cancer 2007, 120, 696–703. [Google Scholar] [CrossRef] [PubMed]
- Song, M.; Garrett, W.S.; Chan, A.T. Nutrients, foods, and colorectal cancer prevention. Gastroenterology 2015, 148, 1244–1260. [Google Scholar] [CrossRef] [PubMed]
- Solomon, L.R. Functional vitamin B12 deficiency in advanced malignancy: Implications for the management of neuropathy and neuropathic pain. Support Care Cancer 2016, 24, 3489–3494. [Google Scholar] [CrossRef] [PubMed]
- Vashi, P.; Edwin, P.; Popiel, B.; Lammersfeld, C.; Gupta, D. Methylmalonic Acid and Homocysteine as Indicators of Vitamin B-12 Deficiency in Cancer. PLoS ONE 2016, 11, e0147843. [Google Scholar] [CrossRef] [PubMed]
- Vistad, I.; Kristensen, G.B.; Fosså, S.D.; Dahl, A.A.; Mørkrid, L. Intestinal malabsorption in long-term survivors of cervical cancer treated with radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 2009, 73, 1141–1147. [Google Scholar] [CrossRef] [PubMed]
- Wolters, M.; Ströhle, A.; Hahn, A. Age-associated changes in the metabolism of vitamin B(12) and folic acid: Prevalence, aetiopathogenesis and pathophysiological consequences. Z. Gerontol. Geriatr. 2004, 37, 109–135. [Google Scholar] [CrossRef] [PubMed]
- Klai, S.; Fekih-Mrissa, N.; El Housaini, S.; Kaabechi, N.; Nsiri, B.; Rachdi, R.; Gritli, N. Association of MTHFR A1298C polymorphism (but not of MTHFR C677T) with elevated homocysteine levels and placental vasculopathies. Blood Coagul. Fibrinolysis 2011, 22, 374–378. [Google Scholar] [CrossRef] [PubMed]
- Yang, B.; Liu, Y.; Li, Y.; Fan, S.; Zhi, X.; Lu, X.; Wang, D.; Zheng, Q.; Wang, Y.; Wang, Y. Geographical distribution of MTHFR C677T, A1298C and MTRR A66G gene polymorphisms in China: Findings from 15357 adults of Han nationality. PLoS ONE 2013, 8, e57917. [Google Scholar] [CrossRef] [PubMed]
- Frosst, P.; Blom, H.J.; Milos, R.; Goyette, P.; Sheppard, C.A.; Matthews, R.G.; Boers, G.J.; den Heijer, M.; Kluijtmans, L.A.; van den Heuvel, L.P.; et al. A candidate genetic risk factor for vascular disease: A common mutation in methylenetetrahydrofolate reductase. Nat. Genet. 1995, 10, 111–113. [Google Scholar] [CrossRef] [PubMed]
- Yaliwal, L.V.; Desai, R.M. Methylenetetrahydrofolate reductase mutations, a genetic cause for familial recurrent neural tube defects. Indian J. Hum. Genet. 2012, 18, 122–124. [Google Scholar] [CrossRef] [PubMed]
- Ravegnini, G.; Zolezzi Moraga, J.M.; Maffei, F.; Musti, M.; Zenesini, C.; Simeon, V.; Sammarini, G.; Festi, D.; Hrelia, P.; Angelini, S. Simultaneous analysis of SEPT9 promoter methylation status, micronuclei frequency, and folate-related gene polymorphisms: The potential for a novel blood-based colorectal cancer biomarker. Int. J. Mol. Sci. 2015, 16, 28486–28497. [Google Scholar] [CrossRef] [PubMed]
- Selhub, J.; Rosenberg, I.H. Excessive folic acid intake and relation to adverse health outcome. Biochimie 2016, 126, 71–78. [Google Scholar] [CrossRef] [PubMed]
- Cheng, T.Y.; Makar, K.W.; Neuhouser, M.L.; Miller, J.W.; Song, X.; Brown, E.C.; Beresford, S.A.; Zheng, Y.; Poole, E.M.; Galbraith, R.L.; et al. Folate-mediated one-carbon metabolism genes and interactions with nutritional factors on colorectal cancer risk: Women’s Health Initiative Observational Study. Cancer 2015, 121, 3684–3691. [Google Scholar] [CrossRef] [PubMed]
- Li, W.X.; Dai, S.X.; Zheng, J.J.; Liu, J.Q.; Huang, J.F. Homocysteine metabolism gene polymorphisms (MTHFR C677T, MTHFR A1298C, MTR A2756G and MTRR A66G) jointly elevate the risk of folate deficiency. Nutrients 2015, 7, 6670–6687. [Google Scholar] [CrossRef] [PubMed]
- Lucock, M.; Yates, Z.; Martin, C.; Choi, J.H.; Beckett, E.; Boyd, L.; LeGras, K.; Ng, X.; Skinner, V.; Wai, R.; et al. Methylation diet and methyl group genetics in risk for adenomatous polyp occurrence. BBA Clin. 2015, 3, 107–112. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhou, D.; Mei, Q.; Luo, H.; Tang, B.; Yu, P. The polymorphisms in methylenetetrahydrofolate reductase, methionine synthase, methionine synthase reductase, and the risk of colorectal cancer. Int. J. Biol. Sci. 2012, 8, 819–830. [Google Scholar] [CrossRef] [PubMed]
- Shiao, S.P.K.; Grayson, J.; Yu, C.; Wasek-Patterson, B.; Bottiglieri, T. Gene environment interactions and predictors of colorectal cancer in family-based multi-ethnic groups. J. Personal. Med. 2018, 8, 10. [Google Scholar] [CrossRef] [PubMed]
- Simidjievski, N.; Todorovski, L.; Džeroski, S. Modeling dynamic systems with efficient ensembles of process-based models. PLoS ONE 2016, 11, e0153507. [Google Scholar] [CrossRef] [PubMed]
- Khalilia, M.; Chakraborty, S.; Popescu, M. Predicting disease risks from highly imbalanced data using random forest. BMC Med. Inform. Decis. Mak. 2011, 11, 51. [Google Scholar] [CrossRef] [PubMed]
- Islam, M.M.; Yao, X.; Shahriar Nirjon, S.M.; Islam, M.A.; Murase, K. Bagging and boosting negatively correlated neural networks. IEEE Trans. Syst. Man Cybern. B Cybern. 2008, 38, 771–784. [Google Scholar] [CrossRef] [PubMed]
- Wang, C.W. New ensemble machine learning method for classification and prediction on gene expression data. In Proceedings of the 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, New York, NY, USA, 30 August–3 September 2006; Volume 1, pp. 3478–3481. [Google Scholar]
- Friedman, J.; Hastie, T.; Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 2010, 33, 1–22. [Google Scholar] [CrossRef] [PubMed]
- Song, L; Langfelder, P.; Horvath, S. Random generalized linear model: A highly accurate and interpretable ensemble predictor. BMC Bioinform. 2013, 14, 5. [Google Scholar] [CrossRef] [PubMed]
- Witten, D.M.; Tibshirani, R. Covariance-regularized regression and classification for high-dimensional problems. J. R. Stat. Soc. Ser. B Stat. Methodol. 2009, 71, 615–636. [Google Scholar] [CrossRef] [PubMed]
- Wu, Y. Elastic Net for Cox’s proportional hazards model with a solution path algorithm. Stat. Sin. 2012, 22, 27–294. [Google Scholar] [CrossRef] [PubMed]
- Shiao, S.P.K.; Grayson, J.; Lie, A.; Yu, C.H. Predictors of healthy eating index and glycemic index in multiethnic colorectal cancer families. Nutrients 2018, 10, 674. [Google Scholar] [CrossRef] [PubMed]
- Krist, A.H.; Glenn, B.A.; Glasgow, R.E.; Balasubramanian, B.A.; Chambers, D.A.; Fernandez, M.E.; Heurtin-Roberts, S.; Kessler, R.; Ory, M.G.; Phillips, S.M.; et al. Designing a valid randomized pragmatic primary care implementation trial: The my own health report (MOHR) project. Implement. Sci. 2013, 8, 73. [Google Scholar] [CrossRef] [PubMed]
- CDC. National Health and Nutrition Examination Survey. In Center for Disease Control and Prevention.; 2012. Available online: http://www.cdc.gov/nchs/nhanes/nhanes_questionnaires.htm (accessed on 15 December 2012).
- National Coalition for Health Professional Education in Genetics. Family History Educational Aids. NCHPEG. Available online: http://www.nchpeg.org/index.php?option=com_content&view=article&id=145&Itemid=64 (accessed on 10 October 2016).
- Ducros, V.; Belva-Besnet, H.; Casetta, B.; Favier, A. A robust liquid chromatography tandem mass spectrometry method for total plasma homocysteine determination in clinical practice. Clin. Chem. Lab. Med. 2006, 44, 987–990. [Google Scholar] [CrossRef] [PubMed]
- Arning, E.; Bottiglieri, T. Quantitation of S-Adenosylmethionine and S-Adenosylhomocysteine in Plasma Using Liquid Chromatography-Electrospray Tandem Mass Spectrometry. Methods Mol. Biol. 2016, 1378, 255–262. [Google Scholar] [CrossRef] [PubMed]
- Inoue-Choi, M.; Nelson, H.H.; Robien, K.; Arning, E.; Bottiglieri, T.; Koh, W.P.; Yuan, J.M. One-carbon metabolism nutrient status and plasma S-adenosylmethionine concentrations in middle-aged and older Chinese in Singapore. Int. J. Mol. Epidemiol. Genet. 2012, 3, 160–173. [Google Scholar] [PubMed]
- Butler, L.M.; Arning, E.; Wang, R.; Bottiglieri, T.; Govindarajan, S.; Gao, Y.T.; Yuan, J.M. Prediagnostic levels of serum one-carbon metabolites and risk of hepatocellular carcinoma. Cancer Epidemiol. Biomark. Prev. 2013, 22, 1884–1893. [Google Scholar] [CrossRef] [PubMed]
- Fasching, C.; Singh, J. Quantitation of Methylmalonic Acid in Plasma Using Liquid Chromatography—Tandem Mass Spectrometry. Clin. Appl. Mass Spectrom. Methods Mol. Biol. 2010, 603. [Google Scholar] [CrossRef]
- Wren, M.E.; Shirtcliff, E.A.; Drury, S.S. Not all biofluids are created equal: Chewing over salivary diagnostics and the epigenome. Clin. Ther. 2015, 37, 529–539. [Google Scholar] [CrossRef] [PubMed]
- Torres-Sánchez, L.; Chen, J.; Díaz-Sánchez, Y.; Palomeque, C.; Bottiglieri, T.; López-Cervantes, M.; López-Carrillo, L. Dietary and genetic determinants of homocysteine levels among Mexican women of reproductive age. Eur. J. Clin. Nutr. 2006, 60, 691–697. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lievers, K.J.; Boers, G.H.; Verhoef, P.; Heijer, M.; Kluijtmans, L.A.; Put, N.M.; Trijbels, F.J.; Blom, H.J. A second common variant in the methylenetetrahydrofolate reductase (MTHFR) gene and its relationship to MTHFR enzyme activity, homocysteine, and cardiovascular disease risk. J. Mol. Med. 2001, 79, 522–528. [Google Scholar] [CrossRef] [PubMed]
- Klimberg, R.; McCullough, B.D. Fundamentals of Predictive Analytics with JMP, 2nd ed.; SAS Press: Cary, NC, USA, 2016. [Google Scholar]
- Yu, C.H. Resampling: A Conceptual and Procedural Introduction. In Best Practices in Quantitative Methods; Osborne, J., Ed.; Sage Publications: Thousand Oaks, CA, USA, 2007; pp. 283–298. [Google Scholar]
- Murphy, K.P. Machine Learning: A Probabilistic Perspective; MIT Press: Boston, MA, USA, 2012. [Google Scholar]
- Diaconis, P.; Efron, B. Computer-intensive methods in statistics. Sci. Am. 1983, 248, 116–130. [Google Scholar] [CrossRef]
- Meir, R.; Rätsch, G. An introduction to boosting and leveraging. In Advanced Lectures on Machine Learning; Lecture Notes in Computer Science; Mendelson, S., Smola, A.J., Eds.; Springer: Berlin, Germany, 2003; Volume 2600, pp. 118–183. [Google Scholar]
- Zaman, M.F.; Hirose, H. Classification performance of bagging and boosting type ensemble methods with small training sets. New Gener. Comput. 2011, 29, 277–292. [Google Scholar] [CrossRef]
- Wujek, B. Machine Learning; SAS Press: Cary, NC, USA, 2016. [Google Scholar]
- Burnham, K.P.; Anderson, D.R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, 2nd ed.; Springer: New York, NY, USA, 2002. [Google Scholar]
- Burnham, K.P.; Anderson, D.R. Multimodel inference: Understanding AIC and BIC in model selection. Sociol. Meth. Res. 2004, 33, 261–304. [Google Scholar] [CrossRef]
- Yang, Y. Can the strengths of AIC and BIC be shared? Biometrika 2005, 92, 937–950. [Google Scholar] [CrossRef]
- Akaike, H. A new look at the statistical model identification. IEEE Trans. Autom. Control 1974, 19, 716–723. [Google Scholar] [CrossRef]
- Akaike, H. A Bayesian analysis of the minimum AIC procedure. Ann. Inst. Stat. Math. 1978, 30, 9–14. [Google Scholar] [CrossRef]
- Faraway, J.J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Texts in Statistical Science); Chapman & Hall/CRC: Boca Raton, FL, USA, 2005. [Google Scholar]
- SAS Institute Inc. JMP 13 Fitting Linear Models, 2nd ed.; SAS Institute Inc.: Cary, NC, USA, 2016. [Google Scholar]
- Cheng, H.; Garrick, D.J.; Fernando, R.L. Efficient strategies for leave-one-out cross validation for genomic best linear unbiased prediction. J. Anim. Sci. Biotechnol. 2017, 8, 38. [Google Scholar] [CrossRef] [PubMed]
- Shmueli, G. To Explain or to Predict? Stat. Sci. 2010, 25, 289–310. [Google Scholar] [CrossRef]
- AS Institute. Overview of the Generalized Regression Personality. 2017. Available online: https://www.jmp.com/support/help/14/overview-of-the-generalized-regression-personali.shtml (accessed on 1 April 2018).
- Sha, Q.; Zhang, S. A test of Hardy-Weinberg equilibrium in structured populations. Genet. Epidemiol. 2011, 35, 671–678. [Google Scholar] [CrossRef] [PubMed]
- Behrens, M.; Lange, R. A highly reproducible and economically competitive SNP analysis of several well characterized human mutations. Clin. Lab. 2004, 50, 305–316. [Google Scholar] [PubMed]
- Cenit, M.C.; Olivares, M.; Codoñer-Franch, P.; Sanz, Y. Intestinal microbiota and celiac disease: Cause, consequence or co-evolution? Nutrients 2015, 7, 6900–6923. [Google Scholar] [CrossRef] [PubMed]
- Cadet, J.; Douki, T.; Ravanat, J.L. Oxidatively generated base damage to cellular DNA. Free Radic. Biol. Med. 2010, 49, 9–21. [Google Scholar] [CrossRef] [PubMed]
- Lochhead, P.; Nishihara, R.; Qian, Z.R.; Mima, K.; Cao, Y.; Sukawa, Y.; Kim, S.A.; Inamura, K.; Zhang, X.; Wu, K.; et al. Postdiagnostic intake of one-carbon nutrients and alcohol in relation to colorectal cancer survival. Am. J. Clin. Nutr. 2015, 102, 1134–1141. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Jensen, L.H.; Lindebjerg, J.; Crüger, D.G.; Brandslund, I.; Jakobsen, A.; Kolvraa, S.; Nielsen, J.N. Microsatellite instability and the association with plasma homocysteine and thymidylate synthase in colorectal cancer. Cancer Investig. 2008, 26, 583–589. [Google Scholar] [CrossRef] [PubMed]
- Shannon, B.; Gnanasampanthan, S.; Beilby, J.; Iacopetta, B. A polymorphism in the methylenetetrahydrofolate reductase gene predisposes to colorectal cancers with microsatellite instability. Gut 2002, 50, 520–524. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Keku, T.; Millikan, R.; Worley, K.; Winkel, S.; Eaton, A.; Biscocho, L.; Martin, C.; Sandler, R. 5,10-methylenetetrahydrofolate reductase codon 677 and 1298 polymorphisms and colon cancer in African Americans and Whites. Cancer Epidemiol. Biomark. Prev. 2002, 11, 1611–1621. [Google Scholar]
- Shiao, S.P.K.; Lie, A.; Yu, C.H. Meta-analysis of homocysteine-related factors on the risk of colorectal cancer. Oncotarget 2018, 9, 25681–25697. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hair, J.M.; Terzoudi, G.I.; Hatzi, V.I.; Lehockey, K.A.; Srivastava, D.; Wang, W.; Pantelias, G.E.; Georgakilas, A.G. BRCA1 role in the mitigation of radiotoxicity and chromosomal instability through repair of clustered DNA lesions. Chem. Biol. Interact. 2010, 188, 350–358. [Google Scholar] [CrossRef] [PubMed]
- Baccarelli Ai Cassano, P.A.; Litonjua, A.; Park, S.K.; Suh, H.; Sparrow, D.; Vokonas, P.; Schwartz, J. Cardiac autonomic dysfunction: Effects from particulate air pollution and protection by dietary methyl nutrients and metabolic polymorphisms. Circulation 2008, 117, 1802–1809. [Google Scholar] [CrossRef] [PubMed]
- Wu, S.M.; Chen, Z.F.; Young, L.; Shiao, S.P.K. Meta-prediction of the effects of methylenetetrahydrofolate reductase gene polymorphisms and air pollution on risk of Alzheimer’s disease. Int. J. Environ. Res. Public Health 2017, 14, 63. [Google Scholar] [CrossRef] [PubMed]
- Lien, S.Y.A.; Young, L.; Gau, B.S.; Shiao, S.P.K. Meta-prediction of MTHFR gene polymorphism-mutations, air pollution, and risks of leukemia among world populations. Oncotarget 2017, 8, 4387–4398. [Google Scholar] [CrossRef] [PubMed]
- Gonzales, M.C.; Yu, P.J.; Shiao, S.P.K. Meta-prediction of MTHFR gene polymorphism-mutations and air pollution as risk factors for breast cancer. Nurs. Res. 2017, 66, 152–163. [Google Scholar] [CrossRef] [PubMed]
- Yang, Y.L.; Yang, H.L.; Shiao, S.P.K. Meta-prediction of MTHFR gene polymorphisms and air pollution on the risk of hypertensive disorders in pregnancy worldwide. Int. J. Environ. Res. Public Health 2018, 15, 326. [Google Scholar] [CrossRef] [PubMed]
Factors | Control (Groups 1, 2) | Cancer (Groups 3, 4) | |||
---|---|---|---|---|---|
n (%) or M ± SD (Ranges) | 1-Healthy | 2-Chronic Diseases | 3-Cancer | 4-Advanced | p |
(n = 4) | (n = 11) | (n = 5) | (n = 10) | ||
Gender | |||||
Male | 0 (0%) | 4 (36.4%) | 5 (100%) | 2 (20%) | 0.008 |
Female | 4 (100%) | 11 (63.6%) | 0 (0%) | 8 (80%) | |
Age (Years) | 34 ± 14 | 43 ± 12 | 50 ± 11 | 60 ± 9 | 0.006 |
(19–51) | (21–58) | (38–62) | (44–72) | ||
Posthoc | <4 (p = 0.013) | <4 (p = 0.048) | |||
BMI | 24 ± 3.2 | 28 ± 8.5 | 24 ± 2.2 | 31 + 8.6 | 0.24 |
(17–28) | (21–49) | (19–29) | (19–51) | ||
Weight (Kg) | 63 ± 6.8 | 77 ± 26 | 72 ± 11 | 79 + 26 | 0.59 |
(57–71) | (52–141) | (59–88) | (45–138) | ||
Vegetable intake | 2.3 ± 0.0 | 2 ± 0.8 | 2.6 ± 0.6 | 1.6 ± 0.7 | 0.087 |
Cup Servings | (2–3) | (1–3) | (2–3) | (1–3) | |
Posthoc | <3 (p = 0.027) | ||||
Fruit | 1.3 ± 1.0 | 1.5 ± 0.7 | 1.8 ± 0.5 | 0.9 ± 0.7 | 0.073 |
Cup Servings | (0–2) | (0–2) | (1–2) | (0–2) | |
Posthoc | <3 (p = 0.015) | ||||
Whole grain cups | 1.5 ± 0.6 | 1.7 ± 0.7 | 1.8 ± 0.8 | 1.8 ± 0.8 | 0.92 |
(1–2) | (1–3) | (1–2) | (0–2) | ||
Liquid cups | 5.8 ± 1.5 | 5.5 ± 1.6 | 6.2 ± 1.6 | 5.3 ± 1.5 | 0.56 |
(5–8) | (4–8) | (5–8) | (4–8) | ||
Race | |||||
White (10) | 1 (25%) | 3 (27.3%) | 2 (20%) | 4 (40%) | 0.68 |
Asian (9) | 2 (50%) | 3 (27.3%) | 3 (30%) | 1 (10%) | |
Hispanic (9) | 1 (25%) | 4 (36.4%) | 0 (0%) | 4 (40%) | |
African (2) | 0 (0%) | 1 (9.1%) | 0 (0%) | 1 (10%) |
Genotype | Control (Groups 1, 2) | Cancer (Groups 3, 4) | p | ||
---|---|---|---|---|---|
Enzyme Deficiency | 1-Healthy | 2-Chronic Disease | 3-Cancer | 4-Advanced | |
(n = 4) | (n = 11) | (n = 5) | (n = 10) | ||
MTHFR 677 | |||||
0 (CC) | 2 (50%) | 5 (45.4%) | 2 (40%) | 2 (20%) | 0.70 |
1 (CT) | 1 (25%) | 5 (45.4%) | 2 (40%) | 7 (70%) | |
2 (TT) | 1 (25%) | 1 (9.1%) | 1 (20%) | 1 (10%) | |
MTHFR 1298 | |||||
0 (AA) | 2 (50%) | 7 (63.6%) | 4 (80%) | 7 (70%) | 0.82 |
1 (AC) | 2 (50%) | 4 (36.4%) | 1 (20%) | 2 (20%) | |
2 (CC) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (10%) | |
MTR 2756 | |||||
0 (AA) | 2 (50%) | 7 (63.6%) | 4 (80%) | 3 (30%) | 0.40 |
1 (AG) | 2 (50%) | 2 (18.2%) | 1 (20%) | 6 (60%) | |
2 (GG) | 0 (0%) | 2 (18.2%) | 0 (0%) | 1 (10%) | |
MTRR 66 | |||||
0 (AA) | 2 (66.7%) | 6 (54.5%) | 4 (40%) | 0.93 | |
1 (AG) | 0 (0%) | 3 (27.3%) | 1 (20%) | 4 (40%) | |
2 (GG) | 1 (33.3%) | 2 (18.2%) | 1 (20%) | 2 (20%) | |
DHFR 19 | |||||
00 (++) | 1 (25%) | 5 (45.4%) | 0 (0%) | 3 (30%) | 0.69 |
01 (+−) | 2 (50%) | 4 (36.4%) | 2 (40%) | 4 (40%) | |
11 (−−) | 1 (25%) | 2 (18.2%) | 3 (60%) | 3 (30%) | |
Total Mutation | |||||
≥4 | 1 (25%) | 4 (36.4%) | 1 (20%) | 8 (80%) | 0.077 |
3.25 ± 0.50 | 3.36 ± 1.57 | 2.20 ± 1.30 | 3.90 ± 1.45 | 0.16 | |
(3–4) | (1–6) | (1–4) | (1–6) | ||
Posthoc | <4 (p = 0.049) |
Metabolites | Control (Groups 1, 2) | Cancer (Groups 3, 4) | p | ||
---|---|---|---|---|---|
M + SD (ranges) | 1-Healthy n = 4 | 2-Chronic Disease n = 11 | 3-Cancer n = 5 | 4-Advanced n = 10 | |
Homocysteine (µmol/L) | 4.5 ± 1.8 (3.1–7) | 5.1 ± 1.0 (4.2–7.2) | 8.6 ± 3.8 (5.8–14) | 9.1 ± 4.2 (4–17) | 0.014 |
Posthoc | <4 (p = 0.023) | <3 (p = 0.028)<4 (p = 0.019) | |||
SAM (nmol/L) | 85 ± 24 (70–122) | 89 ± 17 (63–120) | 129 ± 61 (77–233) | 102± 21 (67–134) | 0.12 |
SAH (nmol/L) | 25 ± 14 (11–43) | 23 ± 7.2 (12–38) | 52 ± 51 (23–142) | 29 + 13 (16–56) | 0.25 |
Posthoc | <3 (p = 0.041) | ||||
SAM/SAH Ratio | 4.1 ± 1.9 (1.7–6.3) | 4.2 ± 1.1 (2.8–6.3) | 3.2 ± 1.1 (1.6–4.6) | 3.9 ± 1.1 (0–5.2) | 0.56 |
ADMA (nmol/L) | 573 ± 198 (393–849) | 519 ± 110 (278–720) | 666 ± 223 (472–917) | 557 ± 110 (406–754) | 0.77 |
SDMA (nmol/L) | 488 ± 130 (324–642) | 466 ± 78 (340–589) | 885 ± 671 (401–2050) | 516 ± 109 (425–778) | 0.44 |
Methionine (nmol/L) | 37 ± 10 (27–51) | 30 ± 7.3 (20–46) | 32 ± 4.8 (26–39) | 26 ± 6.2( 18–38) | 0.14 |
Posthoc | <3 (p = 0.041) | ||||
MMA (nmol/L) | 249 ± 48 (185–301) | 285 ± 229 (178–972) | 359 ± 72 (304–480) | 274 ± 97 (186–521) | 0.025 |
Posthoc | <3 (p = 0.02) | <3 (p = 0.013) | |||
Cystathionine (nmol/L) | 423 ± 267 (227–796) | 243 ± 147 (107–600) | 470 ± 221 (193–692) | 244 ± 102 (149–502) | 0.043 |
Posthoc | <3 (p = 0.041) | <3 (p = 0.043) | |||
Betaine (nmol/L) | 71 ± 18 (48–89) | 63 ± 20 (38–111) | 61 ± 24 (37–96) | 53 ± 11 (36–67) | 0.45 |
Vitamin B-6 (nmol/L) | 50 ± 16 (29–67) | 60 ± 42 (14–155) | 64 ± 52 (5.3–128) | 46 ± 24 (20–88) | 0.95 |
5-MTHF (nmol/L) | 30 ± 10 (18–43) | 48 ± 19 (30–97) | 36 ± 5.3 (32–45) | 36 ± 16 (18–78) | 0.063 |
Posthoc | <2 (p = 0.045) | ||||
Choline (nmol/L) | 12 ± 5.7 (7.9–21) | 9.7 ± 2.8 (5.7–16) | 14 ± 7.5 (8–27) | 10 ± 3.1 (6.9–18) | 0.50 |
Logistic Regression Original Model | Generalized Regression Elastic Net Model | |||||
---|---|---|---|---|---|---|
AICc Validation | Leave-One-Out Validation | |||||
Parameters | Estimate | p (X2) | Estimate | p (X2) | Estimate | p (X2) |
(Intercept) | −5.6 | 0.93 | 0.4 | 0.78 | 1.1 | 0.45 |
MMA * Gene mutations | −42 | 0.68 | −30 | <0.0001 | −11 | <0.0001 |
Homocysteine | −15 | 0.77 | −12 | <0.0001 | −5.7 | <0.0001 |
Methyl-folate | 14 | 0.69 | 9.1 | <0.0001 | 3.4 | 0.0019 |
Gene mutations | 14 | 0.86 | 11 | <0.0001 | 4.0 | 0.0188 |
Vegetable intake | 28 | 0.62 | 17 | <0.0001 | 5.6 | 0.0005 |
Age | −14 | 0.63 | −8.7 | <0.0001 | −2.9 | 0.0024 |
MMA | −0.4 | 0.996 | −1.7 | 0.28 | 0 | 1.0 |
Misclassification Rate | 0.2 | – | 0.03 | – | 0.04 | – |
AICc | 27 | – | 26 | – | – | – |
Area under the curve | 1.0 | – | 0.998 | – | 0.997 | – |
Logistic Regression Original Model | Generalized Regression Elastic Net Model | |||||
---|---|---|---|---|---|---|
AICc Validation | Leave-One-Out Validation | |||||
Parameters | Estimate | p (X2) | Estimate | p (X2) | Estimate | p (X2) |
(Intercept) | −0.4 | 0.997 | −0.36 | 0.79 | 1.2 | 0.38 |
MMA * Gene mutations | −35 | 0.77 | −29 | <0.0001 | −9.2 | <0.0001 |
Homocysteine | −13 | 0.63 | −12 | <0.0001 | −4.9 | <0.0001 |
Methyl-folate (MTHF) | 10 | 0.48 | 8.7 | <0.0001 | 2.8 | 0.0093 |
Gene mutations ≥4 | 17 | 0.92 | 12 | 0.0007 | 3.2 | 0.0496 |
Vegetable intake | 20 | 0.35 | 16 | <0.0001 | 4.4 | 0.0033 |
Age | −10 | 0.45 | −8.1 | <0.0001 | −2.5 | 0.0096 |
MMA | −1.9 | 0.99 | −0.7 | 0.64 | 0 | 1.0 |
MTHF * Gene mutations | −4.0 | 0.98 | −0.2 | 0.92 | 0 | 1.0 |
Misclassification Rate | 0.03 | – | 0.03 | – | 0.04 | – |
AICc | 30 | – | 30 | – | – | – |
Area under the curve | 0.998 | – | 0.998 | – | 0.997 | – |
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Shiao, S.P.K.; Grayson, J.; Yu, C.H. Gene-Metabolite Interaction in the One Carbon Metabolism Pathway: Predictors of Colorectal Cancer in Multi-Ethnic Families. J. Pers. Med. 2018, 8, 26. https://doi.org/10.3390/jpm8030026
Shiao SPK, Grayson J, Yu CH. Gene-Metabolite Interaction in the One Carbon Metabolism Pathway: Predictors of Colorectal Cancer in Multi-Ethnic Families. Journal of Personalized Medicine. 2018; 8(3):26. https://doi.org/10.3390/jpm8030026
Chicago/Turabian StyleShiao, S. Pamela K., James Grayson, and Chong Ho Yu. 2018. "Gene-Metabolite Interaction in the One Carbon Metabolism Pathway: Predictors of Colorectal Cancer in Multi-Ethnic Families" Journal of Personalized Medicine 8, no. 3: 26. https://doi.org/10.3390/jpm8030026