Integrated network pharmacology and in-silico approaches to decipher the pharmacological mechanism of Selaginella tamariscina in the treatment of non-small cell lung cancer

Abstract

Background and purpose: Non-small cell lung cancer (NSCLC) is a major pathological type of lung cancer and accounts for more than 80% of all cases. In healthcare management, it is challenging to understand the mech anism of NSCLC due to diverse spectra and the limited number of reported data. Selaginella tamariscina is an evergreen perennial plant, hermaphrodite, and used to treat numerous diseases, including NSCLC. In vitro research revealed the therapeutic importance of S. tamariscina in contrast to NSCLC, but the molecular mecha nism is still unclear. In the present study, a network pharmacology technique was employed to uncover the active ingredients, their potential targets, and signaling pathways in S. tamariscina for the treatment of NSCLC. Methods: Putative ingredients of S. tamariscina and significant genes of NSCLC were retrieved from the public database after screening. The overlapped targets among S. tamariscina related compounds and NSCLC were predicted using Venn plot. Following that, a compound-target-disease network was constructed using Cytoscape to decipher the mechanism of S. tamariscina for NSCLC. KEGG pathway and GO enrichment analysis were per formed to investigate the molecular mechanisms and pathways related to S. tamariscina for NSCLC treatments. Lastly, molecular docking and molecular dynamic simulation analysis were performed to validate the interaction that exists between compounds and target proteins. Results: The findings of the current analysis explored the compound–target–pathway network and figured out that Hinokiflavone, Heveaflavone, Neocryptomerin, Isocryptomerin, Apigenin, Sotetsuflavone, and Cryptomerin B decisively contributed to the development of NSCLC by affecting AKT1, EGFR, VEGFA, and GCK3B genes. Later, molecular docking and simulation analysis was conducted to validate the successful activity of the active compounds against potential targets. Lastly, it is concluded that predicted multi-target compounds of S. tamariscina will help in improving the body’s sensitivity to NSCLC by regulating the expression of AKT1, EGFR, VEGFA, and GCK3B, which may act as potential therapeutic targets of NSCLC. Conclusion: Integrated network pharmacology and docking analysis revealed that S. tamariscina exerted a promising preventive effect on NSCLC by acting on diabetes-associated signaling pathways. The current findings propose that AKT1, VEGFA, EGFR, and GSK3B genes are promising and viable therapeutic targets to reduce the incidence of NSCLC, thereby exerting potential therapeutic effects in NSCLC. This approach introduces a groundwork for further research on the protective mechanisms of S. tamariscina for NSCLC and applications of network pharmacology in drug discovery.