Transcriptomics-based screening of molecular signatures associated with patients overall survival and their key regulators in subtypes of breast cancer.

Molecular subtypes of breast cancer are associated with differences in prognosis and strategies of molecular targeted therapies. Gene regulatory mechanisms as one of the reasons might modulate these differences. In the present study, we proposed a comprehensive data analysis and systems biology approach to explore molecular signatures which reduce the chance of patients overall survival and the possible mechanisms of their regulation by transcription factors (TFs) and microRNAs (miRNAs) in the main subtypes of breast tumor consist of Basal like, Her2 enriched, Luminal A and Luminal B breast cancer. In this regards, we used available microarray datasets to assess common differentially expressed genes (DEGs) in breast cancer subtypes. Using Kaplan-Meier curve analysis we identified common DEGs which are associated with decreasing in the overall survival of breast cancer patients. Furthermore, gene regulatory networks (GRNs) were depicted based on TFs and miRNAs with interest target genes. Then GRNs were analyzed and using five algorithms (Control centrality, Betweenness, Degree, Classification, and MCDS) the key regulators were identified for each subtype. In this study, we highlighted mechanisms underlying the regulation of breast cancer molecular signatures by TFs and miRNAs which their alteration reduce the chance of survival rate in each subtype of breast cancer. Our current study in a holistic insight revealed the importance of some genes and their regulators as potential prognostic markers and/or therapeutic targets in breast cancer patients.