Abstract
OBJECTIVE: To explore the mechanism of action of baicalin (BA) in the treatment of triple-negative breast cancer (TNBC) based on network pharmacology, molecular docking and molecular dynamics simulations and in vitro validation. METHODS: The inhibitory effects of different concentrations of baicalin on the proliferation of MDA-MB-231, 4T1, MCF-7, and MCF-10A cell lines were evaluated by CCK8 assay with clone formation assay. Three compound target prediction platforms, Swiss Target Prediction, SEA and Pharmmapper, were used to predict baicalin-related targets, and mapped with the triple-negative breast cancer-related targets retrieved from GeneCards and OMMI databases to obtain the potential targets of baicalin for the treatment of triple-negative breast cancer; the STRING database and the STRING database and Cytoscape software were used to construct the protein interaction network and screen the core targets; GO and KEGG enrichment analyses were performed on the core targets; the binding of baicalin to the key targets of triple-negative breast cancer was verified by molecular docking and molecular dynamics simulation; and the expression of the relevant proteins was verified. RESULTS: Baicalin showed more obvious antiproliferative effects on triple-negative breast cancer cell lines at certain concentrations, and had less effect on the proliferation of normal breast cells. A total of nine core targets of baicalin in the treatment of triple-negative breast cancer, including AKT1, ESR1, TNF-α, SRC, EGFR, MMP9, JAK2, PPARG, and GSK3B, were identified through the construction of the PPI protein interactions network and the 'Traditional Chinese Medicine-Component-Target-Disease' network, and a total of 252 targets related to the intersected targets were identified in the GO analysis. GO analysis enriched a total of 2,526 Biological process, 105 Cellular component and 250 Molecular function related to the intersecting targets; KEGG analysis enriched a total of 128 signaling pathways related to the intersecting targets; molecular docking results and molecular dynamics studies found that baicalin was able to interact with MMP9, TNF-α, JAK2, PPARG, GSK3B, and other core targets of baicalin for the treatment of triple-negative breast, MMP9, TNF-α, and JAK2 target proteins, and had significant changes in the expression levels of the target proteins. CONCLUSION: Baicalin inhibits the protein expression of MMP9, TNF-α and JAK2 and their related signaling pathways in the treatment of triple-negative breast cancer.