identification of breast cancer biomarkers by constructing protein-protein interaction and mirnas-mrnas networks

Background: in cancer-related diseases, early detection and control of disease progression are very important for successful treatment. breast cancer is a significant problem due to its high mortality rate in the female population worldwide. by the early diagnosis of breast cancer, the 5-year survival rate reaches 93 to 98%. in this study, to identify breast cancer biomarkers, we construct new protein-protein interaction (ppi) and mirnas-mrnas networks by analyzing upregulated and downregulated genes in breast cancer patients.method: in this in silico study, two gene expression profile datasets, with the accession numbers gse42568 and gse154255, were downloaded from the geo database. geo2r was used to obtain differentially expressed mrna (dems) and mirnas (demis) based on |logfc|>2 and adjusted p-value <0.05. gene ontology and kegg pathway enrichment analysis were performed by enrichr. string v9. 1 and cytohubba plugin in cytoscape (v3.9.1) were used to investigate ppi network construction and identification of hub genes. finally, key micrornas (mirnas) were predicted.results: after protein-protein interaction analysis, a total of 10 upregulated dems (dlgap5, ccnb1, ttk, nusap1, rrm2, bub1b, cdk1, cenpf, top2a, and aspm) and 10 downregulated dems (pparg, lipe, cd36, fabp4, scd, lpl, dgat2, pnpla2, acsl1, and lep) were screened as hub genes. based on mirnas-mrnas networks, 4 key mirnas including hsa-mir-182-5p, hsa-mir-96-5p, hsa-mir-335-3p, and hsa-mir-32-5p play a critical role in network regulation.conclusion: our study presents ppi and mirnas-mrnas networks for identifying molecular biomarkers in breast cancer. the introduced biomarkers open a new approach to diagnostic and therapeutic indicators for clinical applications.