Integrative bioinformatics and machine learning to explore the mechanism of Scutellaria baicalensis Georgi in TGF-β1-induced ASMCs proliferation during asthma airway remodeling.

Bronchial asthma is a complex disease characterized by chronic inflammation and airway hyperresponsiveness. As a traditional Chinese herb with anti-inflammatory and anti-allergic properties, Scutellaria baicalensis Georgi (SBG) has demonstrated significant potential in asthma treatment. The present study aimed to identify the role of airway smooth muscle cells (ASMCs) in asthma and elucidate the mechanisms of SBG against asthma. First, differentially expressed genes and key modules associated with asthma were identified from the GSE41665 using limma and weighted gene co-expression network analysis. Functional enrichment analysis revealed that genes were primarily enriched related to inflammation, immune, cell migration, cell cycle, and Toll-like receptor/NF-κB signaling pathway. Second, the ASMC proliferation model was induced by TGF-β1, and 47 candidate genes were identified. The temporal expression patterns further revealed a dynamic regulatory network. Machine learning algorithms-LASSO, SVM-RFE, and Random Forest-were applied to identify potential biomarkers: Lcn2, Il18, Fcgr2b, and Cd14. Simultaneously, molecular docking and molecular dynamics simulation were employed to demonstrate that the SBG components form stable complexes with Lcn2, TLR4, and Rela. Finally, in vitro and in vivo experiments validated that baicalein and scutellarin significantly inhibited TGF-β1-induced ASMC proliferation. The underlying mechanism was associated with the G1 phase arrest reverse, cell migration inhibition, and ACTA2 and CCND1 expression suppression. In conclusion, this study systematically elucidated the core molecular network of asthma ASMCs and the mechanisms of SBG, providing potential drug targets for asthma treatment.