Abstract
BACKGROUND: Gastric cancer was still one of the commonly diagnosed cancer types and the third-most common cause of cancer-related death in the world. Gentiopicroside, which is extracted from the Gentianella acuta, is commonly used in both traditional treatment and modern clinical care; therefore, its anticancer effects have been attracted more attention. However, the systematic analysis of action mechanism of Gentiopicroside on gastric cancer (GC) has not yet been carried out. AIM: A network pharmacology-based strategy combined with molecular docking studies and in vitro validation was employed to investigate potential targets and molecular mechanism of Gentiopicroside against GC. MATERIALS AND METHODS: Potential targets of Gentiopicroside, as well as related genes of GC, were acquired from public databases. Potential targets, and signaling pathways were determined through bioinformatic analysis, including protein-protein interaction (PPI), the Gene Ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, molecular docking and cell experiments were performed to further verify the above findings. RESULTS: Our findings revealed that the anticancer activity of Gentiopicroside potentially involves 53 putative identified target genes. In addition, GO, KEGG, and network analyses revealed that these targets were associated with cell proliferation, metabolic process, and other physiological processes. Furthermore, we have proved that critical compound affected the expression of CCND1, CCNE1, p-AKT and p-P38 at protein levels. These findings provide an overview of the anticancer action of Gentiopicroside from a network perspective; meanwhile, it might also set an example for future studies of other materials used in traditional Chinese medicine (TCM). CONCLUSION: This study comprehensively illuminated the potential targets and molecular mechanism of Gentiopicroside against GC. It also provided a promising approach to uncover the scientific basis and therapeutic mechanism of TCM treating for disease.