Oncotarget

Research Papers:

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

Mildred C. Gonzales, James Grayson, Amanda Lie, Chong Ho Yu and Shyang-Yun Pamela K. Shiao _

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Oncotarget. 2018; 9:29019-29035. https://doi.org/10.18632/oncotarget.25520

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Abstract

Mildred C. Gonzales1, James Grayson2, Amanda Lie3, Chong Ho Yu4 and Shyang-Yun Pamela K. Shiao5

1Los Angeles County College of Nursing and Allied Health, Los Angeles, CA, USA

2Hull College of Business, Augusta University, Augusta, GA, USA

3Citrus Valley Health Partners, Foothill Presbyterian Hospital, Glendora, CA, USA

4University of Phoenix, Pasadena, CA, USA

5College of Nursing and Medical College of Georgia, Augusta University, Augusta, GA, USA

Correspondence to:

Shyang-Yun Pamela K. Shiao, email: pshiao@msn.com; pshiao@augusta.edu

Keywords: gene-environment interaction; breast cancer; predictors

Received: April 11, 2018    Accepted: May 08, 2018    Published: June 26, 2018

ABSTRACT

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. Five genes in one-carbon metabolism pathway including MTHFR 677, MTHFR 1298, MTR 2756, MTRR 66, and DHFR 19bp together with demographics, lifestyle, and dietary intake factors were examined in association with BC risks. A total of 80 participants (40 BC cases and 40 family/friend controls) in southern California were interviewed and provided salivary samples for genotyping. We presented the first study utilizing both conventional and new analytics including ensemble method and predictive modeling based on smallest errors to predict BC risks. Predictive modeling of Generalized Regression Elastic Net Leave-One-Out demonstrated alcohol use (p = 0.0126) and age (p < 0.0001) as significant predictors; and significant interactions were noted between body mass index (BMI) and alcohol use (p = 0.0027), and between BMI and MTR 2756 polymorphisms (p = 0.0090). Our findings identified the modifiable lifestyle factors in gene-environment interactions that are valuable for BC prevention.


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