This study investigated the molecular mechanisms underlying the anticancer effects of Hedyotis Diffusae Herba (HDH) against esophageal squamous cell carcinoma (ESCC) through network pharmacology and molecular docking approaches. We identified active compounds in HDH using the traditional Chinese medicine systems pharmacology database and analysis platform, while ESCC-related targets were retrieved from the GeneCards database. Drug–disease target interactions were analyzed and visualized through Venn diagrams and network topology analysis. We employed Kyoto encyclopedia of genes and genomes pathway enrichment analysis to elucidate potential therapeutic mechanisms and constructed comprehensive “drug-active ingredient-key target-signaling pathway-disease” networks using Cytoscape software. Key active ingredients and targets were prioritized based on network topological parameters, and their binding interactions were validated through molecular docking using AutoDock software. Our analysis identified 15 bioactive compounds in HDH, including quercetin, beta-sitosterol, and stigmasterol, as primary contributors to its anti-ESCC activity, corresponding to 91 potential therapeutic targets. From the GeneCards database, we identified 6262 ESCC-associated genes, including key oncogenes such as TP53, MET, and EGFR. Notably, 43 genes overlapped between HDH targets and ESCC-related genes, including critical cancer drivers such as EGFR, ERBB2, and MYC. Kyoto encyclopedia of genes and genomes pathway enrichment analysis revealed that HDH’s anti-ESCC targets are predominantly involved in the MAPK signaling pathway, p53 signaling pathway, and chemical carcinogenesis–receptor interaction pathways. Molecular docking simulations indicated strong binding affinity and conformational stability between key active compounds (quercetin and beta-sitosterol) and critical target proteins (BCL2 and PRKCA). This study infers the molecular mechanisms through which HDH components may regulate ESCC via an integrated multi-omics approach; however, its computational nature primarily necessitates experimental validation.
Tian et al. (Fri,) studied this question.