Integrating multi-omics, network pharmacology, and experimental validation to unveil the molecular mechanisms of Fructus Xanthii in treating asthma

Introduction This study employs an integrated approach combining multi-omics analysis, network pharmacology, machine learning, and experimental validation to elucidate the molecular mechanisms of Fructus Xanthii (Chinese name: Cang-Er-Zi) in treating asthma. Methods Asthma-related differentially expressed genes (DEGs) were identified from GEO datasets (GSE63142 and GSE14787). Weighted gene co-expression network analysis (WGCNA) was performed on GSE14787. Active ingredient targets of Fructus Xanthii were predicted using TCMSP and SwissTargetPrediction. Integration included machine learning (RF, SVM, XGB), PPI network analysis, GO/KEGG enrichment, immune infiltration (CIBERSORT), molecular docking, molecular dynamics simulations, and in vivo validation in an ovalbumin-induced murine asthma model. Results Yielded 3,755 DEGs and the MEblack module correlated with asthma. Identified 1,317 potential targets, with 100 intersecting DEGs. Hub targets included HSP90AB1, CCNB1, CASP9, CDK6, NR3C1, ERBB2, and CCK. Strong binding affinities (e.g., carboxyatractyloside with HSP90AB1 at −10.09 kcal/mol) and stable complexes were confirmed. Immune profiling showed altered cell populations. In vivo , Fructus Xanthii extract reduced inflammation, cytokines (TNF-α, IL-6, IL-1β, IL-5), and hub gene expression. Discussion Fructus Xanthii exerts anti-asthmatic effects by modulating HSP90AB1/IL6/TNF and PI3K-AKT pathways, regulating inflammation, cell cycle, apoptosis, and immune homeostasis, providing empirical support for multi-target traditional Chinese medicine strategies in asthma management.