Investigation on the mechanism of Ranunculus ternatus Thunb. against esophageal squamous cell carcinoma based on network pharmacology and experimental verification

Abstract Purpose This study was conducted to assess the pharmacological mechanisms of ethyl acetate extract of Ranunculus ternatus Thunb. (RTE) in combating esophageal squamous cell carcinoma (ESCC) through the integration of network pharmacology analysis and experimental validation. Methods Utilizing network pharmacology methodologies, potential targets of RTE and targets associated with ESCC were identified from public databases. Subsequently, protein-protein interaction (PPI) network analysis, Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted to ascertain the key targets and pathways through which RTE may exert its effects against ESCC. Finally, the putative mechanisms of action of RTE on ESCC, as predicted by network pharmacology analysis, were validated through in vitro experiments. Results A total of 274 potential targets were retrieved by searching the intersection of RTE and ESCC targets. 14 key genes of RTE acting on ESCC were obtained combined Component-Target-Disease Pathway and PPI network analysis, including AKT1, HSP90AA1, EGFR, MAPK1, and TNF.GO biological process analysis mainly involved in regulation of inflammatory response, response to lipopolysaccharide, and regulation of apoptotic signaling pathway, etc. KEGG signaling pathway analysis mainly related to MAPK signaling pathway, Relaxin signaling pathway, and PI3K/Akt pathway, etc. Then, the results of in vitro experiment indicated that RTE could inhibit proliferation of EC-109 and TE-13 cells. The in vitro experiments validated that RTE exhibited its therapeutic effects on ESCC mainly though the regulation of cell proliferation via MAPK/ERK and PI3K/Akt signaling pathways. Conclusion This study demonstrated that it may offer a useful tool to clear the molecular mechanism of RTE on ESCC by combination of network pharmacology prediction with experimental validation.