Identification of molecular signatures and pathways of obese breast cancer gene expression data by a machine learning algorithm

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dc.contributor.authorComertpay, Betul
dc.contributor.authorGov, Esra
dc.date.accessioned2025-01-06T17:43:48Z
dc.date.available2025-01-06T17:43:48Z
dc.date.issued2022
dc.description.abstractAim: 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.
dc.identifier.doi10.20517/jtgg.2021.44
dc.identifier.endpage94
dc.identifier.issn2578-5281
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85137412618
dc.identifier.scopusqualityQ3
dc.identifier.startpage84
dc.identifier.urihttps://doi.org/10.20517/jtgg.2021.44
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2813
dc.identifier.volume6
dc.identifier.wosWOS:001205679300005
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherOae Publishing Inc
dc.relation.ispartofJournal of Translational Genetics and Genomics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectObesity
dc.subjectbreast cancer
dc.subjectmachine learning
dc.subjectpenalty regression models
dc.titleIdentification of molecular signatures and pathways of obese breast cancer gene expression data by a machine learning algorithm
dc.typeArticle

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