Identification of molecular signatures and pathways of obese breast cancer gene expression data by a machine learning algorithm
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Tarih
2022
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Oae Publishing Inc
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Aim: 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.
Açıklama
Anahtar Kelimeler
Obesity, breast cancer, machine learning, penalty regression models
Kaynak
Journal of Translational Genetics and Genomics
WoS Q Değeri
N/A
Scopus Q Değeri
Q3
Cilt
6
Sayı
1
