Comertpay, BetulGov, Esra2025-01-062025-01-0620222578-528110.20517/jtgg.2021.442-s2.0-85137412618https://doi.org/10.20517/jtgg.2021.44https://hdl.handle.net/20.500.14669/2813Aim: Currently, the obesity epidemic is one of the biggest problems for human health. Obesity is impacted on survival in patients with breast cancer. However, key biomarkers of obesity -related breast cancer risk are still not well known. Thus, using machine learning to identify the most appropriate features in obesity -associated breast cancer patients may improve the predictive accuracy and interpretability of regression models. Methods: In the present study, we identified 23 differentially expressed genes (DEGs) from the GSE24185 transcriptome dataset. Seed genes were identified from DEGs, the co -expression network genes and hub genes of the protein -protein interaction network. Pathway enrichment analysis was performed for DEGs. The Ridge penalty regression model was executed by using P -values of enriched pathways and seed gene pathway association score to obtain the most relevant molecular signatures. The model was performed using 10 -fold cross -validation to fit the penalized models. Results: Angiotensin II receptor type 1 (AGTR1), cyclin D1 (CCND1), glutamate ionotropic receptor AMPA type subunit 2 (GRIA2), interleukin-6 cytokine family signal transducer (IL6ST), matrix metallopeptidase 9 (MMP9), and protein kinase CAMP -dependent type II regulatory subunit beta (PRKAR2B) were considered as candidate molecular signatures of obese patients with breast cancer. In addition, RAF -independent MAPK1/3 activation, collagen degradation, bladder cancer, drug metabolism-cytochrome P450, and signaling by Hedgehog pathways in cancer were primarily associated with obesity -associated breast cancer. Conclusion: These genes may be used for risk analysis of the disease progression of obese patients with breast cancer. Corresponding genes and pathways should be validated via experimental studies.eninfo:eu-repo/semantics/openAccessObesitybreast cancermachine learningpenalty regression modelsArticle941Q3846WOS:001205679300005N/A