Network Pharmacology-Based Approach to Investigate the Molecular Targets of Sinomenine for Treating Breast Cancer

Purpose: Sinomenine has been known to inhibit the proliferation of breast cancer cells. However, its targets have not been found yet. This study aimed to search for molecular targets of sinomenine for treating breast cancer via network pharmacology. Methods: Potential targets of sinomenine or breast cancer were separately screened from indicated databases. The common targets of both sinomenine and breast cancer were considered as the targets of sinomenine for treating breast cancer. A sinomenine-targetpathway network was constructed based on the obtained results from Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The putative targets of sinomenine were further determined by using protein-protein interaction (PPI) analysis and molecular docking. Finally, the putative targets were verified in vitro and in vivo. Results: Twenty predicted targets were identified through network pharmacological analysis. Gene Ontology (GO) and KEGG pathway enrichment indicated that these predicted targets enriched in the process of MAP kinase activity, VEGF signaling pathway, Relaxin signaling pathway, Growth hormone synthesis, secretion and action. MAPK1, NOS3, NR3C1, NOS1 and NOS2 were further identified as the putative targets by using PPI and molecular docking analysis. Expression of MAPK1, NR3C1, NOS1, NOS2 and NOS3 genes were significantly regulated by sinomenine in both MCF-7 cells and MDA-MB-231 cells. Furthermore, the expression of NR3C1 in human breast cancer specimens was lower than that in para-tumor normal tissues. Meanwhile, the expression of NR3C1 in xenograft tumors was up-regulated after sinomenine treatment. Conclusion: MAPK1, NR3C1, NOS1, NOS2 and NOS3 were identified as the putative targets of sinomenine for treating breast cancer. NR3C1 was preliminarily confirmed as a target of sinomenine in two breast cancer cell lines, xenograft tumor models and human breast cancer specimens. These data indicated that the network pharmacology-based prediction of sinomenine targets for treating breast cancer could be reliable.