Breast Cancer Risk and Insulin Resistance: Post Genome-Wide Gene–Environment Interaction Study Using a Random Survival Forest

Abstract

Obesity-insulin connections have been considered potential risk factors for postmenopausal breast cancer, and the association between insulin resistance (IR) genotypes and phenotypes can be modified by obesity-lifestyle factors, affecting breast cancer risk. In this study, we explored the role of IR in those pathways at the genome-wide level. We identified IRgenetic factors and selected lifestyles to generate risk profiles for postmenopausal breast cancer. Using large-scale cohort data from postmenopausal women in the Women's Health Initiative Database for Genotypes and Phenotypes Study, our previous genome-wide association gene-behavior interaction study identified 58 loci for associations with IR phenotypes (homeostatic model assessment-IR, hyperglycemia, and hyperinsulinemia). We evaluated those single-nucleotide polymorphisms (SNP) and additional 31 lifestyles in relation to breast cancer risk by conducting a two-stage multimodal random survival forest analysis. We identified the most predictive genetic and lifestyle variables in overall and subgroup analyses [stratified by body mass index (BMI), exercise, and dietary fat intake]. Two SNPs (LINC00460 rs17254590 and MKLN1 rs117911989), exogenous factors related to lifetime cumulative exposure to estrogen, BMI, and dietary alcohol consumption were the most common influential factors across the analyses. Individual SNPs did not have significant associations with breast cancer, but SNPs and lifestyles combined synergistically increased the risk of breast cancer in a gene-behavior, dose-dependent manner. These findings may contribute to more accurate predictions of breast cancer and suggest potential intervention strategies for women with specific genetic and lifestyle factors to reduce their breast cancer risk. Significance: These findings identify insulin resistance SNPs in combination with lifestyle as synergistic factors for breast cancer risk, suggesting lifestyle changes can prevent breast cancer in women who carry the risk genotypes.

Association Between Triglyceride Glucose Index and Non-Small Cell Lung Cancer Risk in Chinese Population

“…Insulin resistance (IR) is a condition with greater prevalence and significance in the context of obesity, Mets, NAFLD, and T2DM. In addition, emerging evidence suggested that insulin resistance was closely related to cancer incidence and mortality, including colorectal cancer ( 20 ), gastric cancer ( 21 ), and breast cancer ( 22 ). However, there was a considerable controversy about the association of IR with lung cancer.…”

Identifying Stage II Colorectal Cancer Recurrence Associated Genes by Microarray Meta-Analysis and Building Predictive Models with Machine Learning Algorithms

“…For instance, Paik et al developed a 21-gene-based random survival forest model to predict progression-free survival of ovarian cancer [ 42 ]. Jung et al identified insulin resistance SNPs in combination with lifestyle factors for breast cancer risk prediction via the random survival forest model [ 43 ]. In the current study, the random survival forest model had larger AUC and Harrell's C-index than the lasso Cox model.…”

Dynein and muskelin control myosin VI delivery towards the neuronal nucleus

“…Given the role of myosin VI in cellular processes such as proliferation, survival, cell adhesion, and migration (Cho and Chen, 2010;Fili et al, 2017;Hari-Gupta et al, 2020 Loikkanen et al, 2009;Vreugde et al, 2006;Zorca et al, 2015), it is noteworthy that muskelin and other CTLH complex-associated proteins that contain the LisH/CTLH motifs were reported to essentially affect the same fundamental processes as discussed for myosin VI (Huffman et al, 2019;Lampert et al, 2018;Liu and Pfirrmann, 2019;Maitland et al, 2019;Qiao et al, 2020). Consistent with these cellular functions both, myosin VI and muskelin are reported to show significant association with breast and prostate cancers (Chen et al, 2019;Dunn et al, 2006;Gueron et al, 2014;Jung et al, 2019;Wang et al, 2015;Yoshida et al, 2004). Intriguingly, an affinity-based approach to screen for binding partners of the Drosophila homolog of myosin VI (Jaguar) identified muskelin and the other six CTLH complex members to associate with myosin VI (Finan et al, 2011).…”