Abstract
For the personalization of polygenic/omics-based health care, the purpose of this study was to examine the gene–environment interactions and predictors of colorectal cancer (CRC) by including five key genes in the one-carbon metabolism pathways. In this proof-of-concept study, we included a total of 54 families and 108 participants, 54 CRC cases and 54 matched family friends representing four major racial ethnic groups in southern California (White, Asian, Hispanics, and Black). We used three phases of data analytics, including exploratory, family-based analyses adjusting for the dependence within the family for sharing genetic heritage, the ensemble method, and generalized regression models for predictive modeling with a machine learning validation procedure to validate the results for enhanced prediction and reproducibility. The results revealed that despite the family members sharing genetic heritage, the CRC group had greater combined gene polymorphism rates than the family controls (p < 0.05), on MTHFR C677T, MTR A2756G, MTRR A66G, and DHFR 19 bp except MTHFR A1298C. Four racial groups presented different polymorphism rates for four genes (all p < 0.05) except MTHFR A1298C. Following the ensemble method, the most influential factors were identified, and the best predictive models were generated by using the generalized regression models, with Akaike’s information criterion and leave-one-out cross validation methods. Body mass index (BMI) and gender were consistent predictors of CRC for both models when individual genes versus total polymorphism counts were used, and alcohol use was interactive with BMI status. Body mass index status was also interactive with both gender and MTHFR C677T gene polymorphism, and the exposure to environmental pollutants was an additional predictor. These results point to the important roles of environmental and modifiable factors in relation to gene–environment interactions in the prevention of CRC.
Highlights
Colorectal cancer (CRC) is a cancer that is preventable by modifying environmental and lifestyle interventions for human ecological development [1,2,3,4,5,6]
We examined two loci of methylenetetrahydrofolate reductase (MTHFR) gene polymorphisms, C677T and A1298C, both are associated with MTHFR enzymatic deficiency resulting in increased homocysteine concentrations [18,19]
We used generalized regression models for predictive modeling with machine learning validation procedures [47], including significant variables and variables with significant interactions identified through the data visualization of bi-variate interaction profilers, to validate the results for enhanced prediction and reproducibility
Summary
Colorectal cancer (CRC) is a cancer that is preventable by modifying environmental and lifestyle interventions for human ecological development [1,2,3,4,5,6]. Well-defined environmental interventions may improve cancer treatment effects, prevent cancer progression and increase survival through epigenetic mechanisms with gene environment interactions [1,4,5]. 70% of CRC is related to environmental and lifestyle factors, while about 30% of CRC risk is inheritable with 5% being highly aggressive in cancer progression for metastatic penetrance [7,8,9].
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