Immune subtypes and diagnostic genes revealed by neutrophil trap-associated transcriptomic signatures in ischemic stroke

Abstract Objective To investigate the involvement of neutrophil extracellular trap (NET)-associated genes in ischemic stroke (IS) and create a diagnostic model for precision stroke treatment. Methods Transcriptomic datasets from GEO were integrated to identify NETs-related gene signatures in ischemic stroke. We conducted differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA), followed by biomarker identification using support vector machine-recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO), and Random Forest (RF) algorithms. A multigene diagnostic model was developed and validated. Immune subtypes were defined via consensus clustering based on hub genes. Immune infiltration, functional enrichment, drug-gene interaction analysis, and molecular docking were performed to identify therapeutic targets specific to subtypes. Results Six hub genes (KCNJ15, ARG1, CLEC4E, ABCA1, ANXA1, and MMP9) were recognized as promising biomarkers for diagnosis with excellent performance (AUC = 0.990). Two immune subtypes of IS were revealed, characterized by distinct metabolic activity, immune cell infiltration, and proinflammatory signaling. Functional analysis confirmed significant immunometabolic divergence between the subtypes. Rosuvastatin was identified as a potential therapeutic agent targeting ABCA1, MMP9, and KCNJ15, suggesting subtype-specific therapeutic effects. Conclusion Our study provides a novel framework for immune subtyping and targeted therapy in IS, demonstrating the diagnostic and therapeutic potential of NETs-related biomarkers. These findings offer promising implications for precision stroke management.