Exploring the therapeutic potential and in vitro validation of baicalin for the treatment of triple-negative breast cancer

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.