Objective To screen key genes of ischemic stroke (IS) and analyze their mechanisms by integrating multi-omics and machine learning methods, and predict their potential targets for traditional Chinese medicine prevention and treatment. Methods IS transcriptome data from GEO database was integrated, and candidate genes were screened through differential expression, weighted gene co-expression network analysis (WGCNA) and protein interaction network analysis. A variety of machine learning algorithms including logistic least absolute shrinkage and selection operator (Lasso), random forest, etc. were used to construct a diagnostic model, and the optimal gene set is determined through cross-verification. An middle cerebral artery occlusion (MCAO) model was constructed and further verified by Bederson scoring, HE staining and real-time quantitative polymerase chain reaction (RT-qPCR). Immune infiltration was analyzed using CIBERSORTx and potential traditional Chinese medicines were reverse-matched based on Coremine Medical database. Results Eight core genes (ARG1, CLEC4E, CLEC5A, FCAR, FCGR1A, IRAK3, MCEMP1, TLR5) were identified, and their diagnostic models performed well in both the training and verification cohorts [area under curve (AUC) > 0.7]. Animal experiments had confirmed that the expression of these genes was significantly up-regulated in the cortical tissue of IS model rats, and was closely related to the infiltration level of immune cells such as M0-type macrophages and neutrophils. Based on the above targets, 59 potential traditional Chinese medicines were predicted. Most of the four qi were cold, warm and calm, and most of the five are bitter and sweet. The meridian tropism was mainly concentrated in the liver, kidney and spleen meridians. The medicinal properties and meridian tropism were consistent with the pathogenesis of IS “liver and kidney yin deficiency, and stasis and heat accumulation”. Conclusion By integrating bioinformatics, machine learning and experimental validation, this study systematically identified and validated eight key genes involved in the remodelling of the post-stroke immune microenvironment, which may serve as potential diagnostic biomarkers for IS. Predictive analysis of traditional Chinese medicine suggests that the pharmacological properties—including taste, nature and meridian tropism—of drugs targeting these genes are consistent with the pathogenesis of IS, thereby providing a theoretical basis for the prevention and treatment of IS using traditional Chinese medicine from the perspective of immune regulation.

ischemic stroke  /  machine learning  /  molecular mechanisms  /  strategies for prevention and treatment with traditional Chinese medicine  /  medicinal properties