This study identifies four biomarkers (CDKN1A, GPX4, PRDX1, and PRDX6) associated with oxidative stress and ferroptosis in ischemic stroke (IS) using bioinformatics methods. The researchers analyzed gene expression data from the Gene Expression Omnibus (GEO) database to identify differentially expressed mRNAs (DEmRNAs) between IS and control groups. They used weighted gene co-expression network analysis (WGCNA) to identify critical modules and cross-referenced DEmRNAs with oxidative stress-related genes (ORGs) and ferroptosis-related genes (FRGs) to find overlapping mRNAs. Candidate mRNAs were further analyzed using protein-protein interaction (PPI) networks and machine learning algorithms (LASSO and SVM-RFE) to identify potential biomarkers. The four biomarkers were validated using gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA) to assess their association with oxidative stress and immune cell infiltration. The study also constructed an miRNA-mRNA-TF regulatory network and performed drug sensitivity analysis to explore therapeutic targets. Quantitative real-time PCR (qRT-PCR) confirmed the upregulation of CDKN1A, PRDX1, and PRDX6 in IS samples compared to control samples. The results suggest that these four biomarkers are significantly associated with IS and may provide new insights for the diagnosis and treatment of IS. The study highlights the role of oxidative stress and ferroptosis in IS pathogenesis and offers a theoretical basis for further research and clinical applications.This study identifies four biomarkers (CDKN1A, GPX4, PRDX1, and PRDX6) associated with oxidative stress and ferroptosis in ischemic stroke (IS) using bioinformatics methods. The researchers analyzed gene expression data from the Gene Expression Omnibus (GEO) database to identify differentially expressed mRNAs (DEmRNAs) between IS and control groups. They used weighted gene co-expression network analysis (WGCNA) to identify critical modules and cross-referenced DEmRNAs with oxidative stress-related genes (ORGs) and ferroptosis-related genes (FRGs) to find overlapping mRNAs. Candidate mRNAs were further analyzed using protein-protein interaction (PPI) networks and machine learning algorithms (LASSO and SVM-RFE) to identify potential biomarkers. The four biomarkers were validated using gene set enrichment analysis (GSEA) and single-sample GSEA (ssGSEA) to assess their association with oxidative stress and immune cell infiltration. The study also constructed an miRNA-mRNA-TF regulatory network and performed drug sensitivity analysis to explore therapeutic targets. Quantitative real-time PCR (qRT-PCR) confirmed the upregulation of CDKN1A, PRDX1, and PRDX6 in IS samples compared to control samples. The results suggest that these four biomarkers are significantly associated with IS and may provide new insights for the diagnosis and treatment of IS. The study highlights the role of oxidative stress and ferroptosis in IS pathogenesis and offers a theoretical basis for further research and clinical applications.