Identification of adipose-proximal biomarkers in breast cancer using weighted gene co-expression network analysis.

Breast cancer is a widely studied cancer that involves multiple complex molecular mechanisms in its development and progression. To gain a deeper understanding of the molecular mechanisms of breast cancer and to search for potential therapeutic targets and prognostic markers, we performed an in-depth analysis of breast cancer gene expression data using weighted co-expression network analysis. First, we downloaded breast cancer-related gene expression data from public databases and performed weighted co-expression network analysis. Through the analysis, we identified the purple modules that are closely related to breast cancer and screened out 224 genes for further functional enrichment analysis. To construct the protein interactions network, we selected 90 of these genes for analysis after screening. The GO enrichment analysis mainly focused on the response of extracellular matrix organization to hormones, negative regulation of angiogenesis, positive regulation of cell proliferation, positive regulation of epithelial-to-mesenchymal transition, transforming growth factor β-receptor signaling pathway, localization of proteins to membranes, response to cortisol, positive regulation of protein kinase B signaling, and other biological processes. KEGG pathway enrichment analysis mainly includes PI3K-Akt signaling pathway, TGF-β signaling pathway, cell cycle, proteoglycan in cancer, MAPK signaling pathway, and many other cancer disease pathways. Finally, we screened the key genes in the protein interactions network using Cytoscape's MCODE plug-in and identified nine key markers, namely THBS2, ACTA2, TIMP1, VCAN, TGFB2, FN1, BGN, CCN2, and TAGLN. These genes may play an important role in the pathogenesis of breast cancer providing new ideas for breast cancer treatment and prognosis.