Gene-environment interactions and predictors of breast cancer in family-based multi-ethnic groups.

Breast cancer (BC) is the most common cancer in women worldwide and second leading cause of cancer-related death. Understanding gene-environment interactions could play a critical role for next stage of BC prevention efforts. Hence, the purpose of this study was to examine the key gene-environmental factors affecting the risks of BC in a diverse sample.

Personalized Nutrition-Genes, Diet, and Related Interactive Parameters as Predictors of Cancer in Multiethnic Colorectal Cancer Families.

To personalize nutrition, the purpose of this study was to examine five key genes in the folate metabolism pathway, and dietary parameters and related interactive parameters as predictors of colorectal cancer (CRC) by measuring the healthy eating index (HEI) in multiethnic families. The five genes included () 677 and 1298, () 2756, ( 66), and () , and they were used to compute a total gene mutation score. We included 53 families, 53 CRC patients and 53 paired family friend members of diverse population groups in Southern California.

Meta-analysis of homocysteine-related factors on the risk of colorectal cancer.

The major objective of this meta-analysis was to examine the association between homocysteine and related measurements with the risk of colorectal cancer (CRC) and adenomatous polyps (AP). Many studies presented an association between gene polymorphisms and risk of CRC. Yet, there have been variances on what homocysteine-related and dietary factors play on the risk of CRC or AP, in association with folate-related one carbon metabolism pathways.

Predictors of the Healthy Eating Index and Glycemic Index in Multi-Ethnic Colorectal Cancer Families.

For personalized nutrition in preparation for precision healthcare, we examined the predictors of healthy eating, using the healthy eating index (HEI) and glycemic index (GI), in family-based multi-ethnic colorectal cancer (CRC) families. A total of 106 participants, 53 CRC cases and 53 family members from multi-ethnic families participated in the study. Machine learning validation procedures, including the ensemble method and generalized regression prediction, Elastic Net with Akaike's Information Criterion with correction and Leave-One-Out cross validation methods, were applied to validate the results for enhanced prediction and reproducibility.