Integrated Network Pharmacology and Machine Learning to Reveal the Mechanisms of Schisandrin A Against Triple-Negative Breast Cancer.

This study aimed to elucidate the mechanism of action of Schisandrin A in the intervention of triple-negative breast cancer (TNBC). Through the application of network pharmacology and the integration of targets from multiple databases, the research identified 128 overlapping targets between Schisandrin A and TNBC. From these, 36 core targets were determined via topological analysis. Utilizing the random forest survival algorithm, four prognosis-related core targets (GSK3B, IDO1, KDR, PKM) were identified from the METABRIC database, and the model's predictive performance was validated using the GSE58812 dataset. Pan-cancer expression analysis confirmed the aberrant expression of these four targets across various tumours. Immune infiltration analysis suggested that GSK3B and IDO1 may influence the tumour immune microenvironment. Molecular docking studies demonstrated a high binding affinity of Schisandrin A with IDO1 and PKM. In vitro cell experiments indicated that Schisandrin A inhibited the proliferation of MDA-MB-231 cells in a concentration-dependent manner, induced apoptosis and caused cell cycle arrest at the S phase. Transcriptome sequencing further revealed the transcriptional changes induced by Schisandrin A, elucidating that the cell cycle and DNA replication are the primary regulatory pathways affected. In conclusion, Schisandrin A exhibits potential anti-triple-negative breast cancer (TNBC) effects by modulating and regulating diverse pathways, including GSK3B and IDO1, alongside its impact on the cell cycle and immune microenvironment. This study presents novel candidate drugs and therapeutic targets for the precise treatment of TNBC.