A systematic in silico mining of the mechanistic implications and therapeutic potentials of estrogen receptor (ER)-α in breast cancer.

Abstract

Estrogen receptor (ER)-α has long been a potential target in ER-α-positive breast cancer therapeutics. In this study, we integrated ER-α-related bioinformatic data at different levels to systematically explore the mechanistic and therapeutic implications of ER-α. Firstly, we identified ER-α-interacting proteins and target genes of ER-α-regulating microRNAs (miRNAs), and analyzed their functional gene ontology (GO) annotations of those ER-α-associated proteins. In addition, we predicted ten consensus miRNAs that could target ER-α, and screened candidate traditional Chinese medicine (TCM) compounds that might hit diverse conformations of ER-α ligand binding domain (LBD). These findings may help to uncover the mechanistic implications of ER-α in breast cancer at a systematic level, and provide clues of miRNAs- and small molecule modulators- based strategies for future ER-α-positive breast cancer therapeutics.