A Systematic In Silico Mining of the Mechanistic Implications and Therapeutic Potentials of Estrogen Receptor (ER)-α in Breast Cancer

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