Identification of key regulators associated with breast cancer prognosis

Introduction: Breast cancer is a heterogeneous disease that is the leading cause of cancer-related death in women globally. Although many efforts have been made to determine the biomarkers and the exact mechanism of this cancer, it is still not completely clear. This study aims to examine the prognostic-associated genes of breast cancer based on bioinformatic analysis of PPI, enrichment analysis, immune interaction, and ceNetwork. Method: Initially, gepia۲ was used to select the prognostic genes based on survival analysis with logFC and HR. Then, the PPI network for up/down prognostic genes was drawn separately with the string in Cytoscape software and the hub genes were selected based on their degree and interaction; enrichment analysis of up/ down prognostic genes was evaluated separately with Enrichr. Also, for hub down-regulated genes, the network (genes, their TFs, miRNA, and their circRNA) and immune interaction (TIMER database) were analyzed, and the Human Protein Atlas (HPA) was used for evaluating and confirming the expression of selected genes in breast tissue. Result: Out of ۳۵۵۶ genes acquired from TCGA-RNAseq for AML, ۳۳۴ genes are considered significant, of which ۱۲۷ genes are prognostic. The result of the PPI network showed that the down-prognostic genes didn't have any interactions, and ۴ up-prognostic genes (PAICS, HN۱, UBE۲C, AAGAB) have degrees of more than ۱۰ and were selected as hub genes for further analysis. Besides, we conducted enrichment analysis for up and down prognostic genes separately. The result showed that Mitotic Cell Cycle Phase Transition (biological process), External Side Of Apical Plasma Membrane (cellular component), Neutral L-amino Acid Transmembrane Transporter Activity (molecular function) and Cell cycle (kegg pathway) were associated significantly with up-regulated genes and Aspartate Metabolic Process (biological process), Lytic Vacuole (cellular component), Carboxylic Acid Binding (molecular function) and Glycosaminoglycan degradation (kegg pathway) were associated with down-prognostic genes. the result of TIMER analysis showed that the hub genes were almost associated with the immune systems. In the end, the ceRNA network was drawn for hub genes based data conducted from mirDB for miRNA associated with genes, which identified ۲۲ miRNA for UBE۲C, ۷۶ for AAGAB, ۷۵ for HN۱ and ۱۰۳ for PAICS; besides, we identified nine circRNA associated with HN۱, seven circRNA associated with PAICS, five circRNA associated with UBE۲C and ten circRNA associated with AAGAB; also, ۳۶ TF associated with breast were identified for AAGAB, ۳۱ for HN۱, ۳۷ for PAICS and ۲۷ for UBE۲C. finally, the expression level of these genes was checked in breast tissues obtained from HPA. Conclusion: These results indicated that PAICS, HN۱, UBE۲C, and AAGAB can be a prognostic indicator for BRCA. overexpression of these genes may serve as an indicator for poor breast cancer outcomes. Further clinical and para-clinical investigation in needed for confirmation and also, these genes may be a choice for future targeted therapy studies.

کلیدواژه ها:

Breast cancer ، BRCA ، prognostic ، ceRNA network ، bioinformatics