Abstract
Ischemic stroke (IS) is a leading cause of mortality and long-term disability, underpinned by complex molecular mechanisms, such as oxidative stress, neuroinflammation, and apoptosis. The flavonoid Engeletin exhibits promising neuroprotective properties, but its mechanism of action remains largely unknown. In this study, we employed a systems biology approach, combined with artificial intelligence (AI), to uncover the multitarget mechanisms of Engeletin in IS. Potential targets were predicted using SwissTargetPrediction and PharmMapper and were found to intersect with IS-related genes from multiple disease databases. Functional enrichment analyses (GO/KEGG) revealed significant involvement in three classical neuroprotective pathways: PI3K-Akt-mTOR/Caspase/BCL2 (anti-apoptotic), TLR4/NF-κB (anti-inflammatory), and NRF2/KEAP1/HO-1 (antioxidant). Notably, we integrated six machine learning models (RF, SVM, GLM, KNN) to identify robust IS-specific biomarkers from the GSE22255 transcriptomic database. We used CIBERSORTx to characterize immune cell infiltration patterns in IS, revealing elevated populations of CD8(+) T cells, M0 macrophages, and other PBMC-derived immune cells, suggesting the presence of an immunologically dynamic microenvironment. Molecular docking predicted favorable binding affinities of Engeletin to core targets (e.g., EGFR, IGF1R, KEAP1, JAK2). Finally, in vitro experiments using a Na(2)S(2)O(4)-induced PC12 cell model confirmed Engeletin's efficacy in reducing oxidative stress, modulating calcium overload, and regulating apoptosis- and inflammation-related genes. Overall, our study establishes a comprehensive pharmacological mechanistic framework for Engeletin in combating IS and reveals the multitarget and multi-pathway neuroprotective mechanisms, thus providing preliminary support for using Engeletin in combating ischemic stroke.