Mechanistic prediction and validation of Brevilin A Therapeutic effects in Lung Cancer

Abstract

Abstract Background Traditional Chinese medicine (TCM) has been found widespread application in neoplasm treatment, yielding promising therapeutic candidates. Previous studies have revealed the anti-cancer properties of Brevilin A, a naturally occurring sesquiterpene lactone derived from Centipeda minima (L.) A.Br. (C. minima), a TCM herb, specifically against lung cancer. However, the underlying mechanisms of its effects remain elusive. This study employs network pharmacology and experimental analyses to unravel the molecular mechanisms of Brevilin A in lung cancer. Methods The Batman-TCM, Swiss Target Prediction, Pharmmapper, SuperPred, and BindingDB databases were screened to identify Brevilin A targets. Lung cancer-related targets were sourced from GEO, Genecards, OMIM, TTD, and Drugbank databases. Utilizing Cytoscape software, a protein-protein interaction (PPI) network was established. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene set enrichment analysis (GSEA), and gene-pathway correlation analysis were conducted using R software. To validate network pharmacology results, molecular docking, molecular dynamics simulations, and in vitro experiments were performed. Results We identified 599 Brevilin A-associated targets and 3864 lung cancer-related targets, with 155 overlapping genes considered as candidate targets for Brevilin A against lung cancer. The PPI network highlighted STAT3, TNF, HIF1A, PTEN, ESR1, and MTOR as potential therapeutic targets. GO and KEGG analyses revealed 2893 enriched GO terms and 157 enriched KEGG pathways, including the PI3K-Akt signaling pathway, FoxO signaling pathway, and HIF-1 signaling pathway. GSEA demonstrated a close association between hub genes and lung cancer. Gene-pathway correlation analysis indicated significant associations between hub genes and the cellular response to hypoxia pathway. Molecular docking and dynamics simulations confirmed Brevilin A’s interaction with PTEN and HIF1A, respectively. In vitro experiments demonstrated Brevilin A-induced dose- and time-dependent cell death in A549 cells. Notably, Brevilin A treatment significantly reduced HIF-1α mRNA expression while increasing PTEN mRNA levels. Conclusions This study demonstrates that Brevilin A exerts anti-cancer effects in treating lung cancer through a multi-target and multi-pathway manner, with the HIF pathway potentially being involved. These results lay a theoretical foundation for the prospective clinical application of Brevilin A.

Keywords

• Brevilin A
• Lung cancer
• Molecular mechanism
• Network pharmacology
• Molecular docking
• Experimental validation