Abstract
BACKGROUND: Traditional Chinese medicines (TCMs) offer a comprehensive approach to managing malignant tumors. OBJECTIVES: The present study aims to predict the high-frequency herbs, core components, and core targets for liver cancer treatment through data mining and network pharmacology. METHODS: The "traditional Chinese Medicine - active components - target - disease" network was established using Cytoscape to identify core components. A protein-protein interaction (PPI) network was constructed using the STRING database, and Cytoscape was used for network topological analysis to identify the core targets. Subsequently, molecular docking was performed using AutoDock Vina and PyMOL to calculate binding energies of core components with core targets. Furthermore, in vitro experiments explored quercetin's impact on liver cancer cell migration, apoptosis, and protein expression. RESULTS: A total of 50 high-frequency drugs were selected. Among these, Atractylodes macrocephala koidz, Astragalus membranaceus, Scutellaria barbata, and Cremastra appendiculata were high-frequency drugs for invigorating qi and heat-clearing in liver cancer treatment. There were 226 common targets of herbal medicine for liver cancer treatment. Based on the degree value, beta-sitosterol, kaempferol, stigmasterol, and luteolin potentially represented core components. Eight key targets, including JUN, MAPK1, RELA, TNF, ESR1, IL-6, TP53, and FOS, were screened out, which were involved in 417 entries and 159 pathways. Molecular docking verified a strong binding affinity of the key compounds to the core targets. In vitro experiments showed that quercetin induced apoptosis and inhibited migration activity of HepG2 cells in a dose-dependent manner by affecting the expression levels of p-c-Jun/c-Jun and c-Fos proteins. CONCLUSIONS: This study provides a foundational basis for future clinical application of TCMs in liver cancer treatment.