ArticleFrontiers in molecular biosciences2026
Discovery and validation of programmed cell death-associated key biomarker genes in ischemic stroke via ssGSEA/WGCNA and LASSO-SVM-RFE.
Article in Frontiers in molecular biosciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Ischemic stroke (IS) currently lacks well-characterized peripheral-blood biomarkers that capture early, pathway-level biology. Programmed cell death (PCD) pathways may shape post-stroke neuroinflammation and could yield clinically informative transcriptional signatures. Methods: Public cohorts (GSE16561 discovery; GSE58294 external test) were analyzed. We quantified sample-level pan-PCD activity using ssGSEA based on a curated PCD gene set,identified PCD-associated modules via WGCNA, and intersected with limma-derived DEGs. Two complementary machine learning (LASSO and SVM-RFE) were used to select compact candidate biomarkers. Diagnostic performance was evaluated by ROC analysis. Immune infiltration was inferred by ssGSEA (28 immune signatures) and correlated with candidate genes. Drug candidates were prioritized using Enrichr/DSigDB and explored by molecular docking. Results: A pan-PCD score was higher in IS than controls and guided WGCNA to a PCD-associated module. Intersection with DEGs yielded 58 PCD-related genes. LASSO and SVM-RFE converged on three biomarkers-CREBBP, ANTXR2, and ARG1. These genes showed consistent discriminative performance in both discovery (AUCs: 0.937-0.981) and external test cohorts (AUCs: 0.656-0.931) and were associated with neutrophil-skewed immune infiltration. Conclusion: An integrative network-ML framework delineated a peripheral-blood pan-PCD-related transcriptional pattern in IS and prioritized three biomarkers with consistent diagnostic performance and a neutrophil-skewed immune context. The exploratory pathway-gene-drug framework proposed here nominates testable compounds and provides a basis for prospective multi-cohort validation and mechanistic studies.
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