Machine Learning-Driven Identification of Blood-Based Biomarkers and Therapeutic Agents for Personalized Ischemic...

Ischemic stroke (IS) is the most common subtype of stroke. However, reliable blood biomarkers for early diagnosis remain unavailable. This study developed a predictive model based on peripheral blood (PB) biomarkers. PB samples from two independent cohorts including IS patients and healthy controls (CTR) were analyzed by RNA sequencing (RNA-seq). 69 mRNAs were consistently and significantly dysregulated in IS patients. Functional enrichment analysis revealed that the IS phenotype was negatively associated with NK cell-mediated cytotoxicity and single-sample gene set enrichment analysis (ssGSEA) revealed a significant reduction in Cd56^bright NK cells, Cd56^dim NK cells, and NKT cells in IS patients. A four-gene diagnostic model-BCL2A1, FAM200B, IGJ, and TXN-was identified and exhibited high diagnostic accuracy across derivation, validation, and external cohorts (AUCs: 0.94, 0.91, and 0.96, respectively). Additionally, potential small molecule compounds were screened using Enrichr database, among which cytochalasin D may represent a novel candidate drug for IS treatment.