Abstract

Background

Atheroma plaques are major etiological factors in the pathogenesis of ischemic stroke (IS). Emerging evidence highlights the critical involvement of the immune microenvironment and dysregulated inflammatory responses throughout IS progression. Consequently, therapeutic strategies targeting specific immune-related markers or signaling pathways within this microenvironment hold significant promise for IS management.

Methods

We integrated Weighted Gene Co-expression Network Analysis (WGCNA), CIBERSORT, and machine learning (LASSO/Random Forest) to identify disease-associated modules and hub genes. Immune infiltration analysis evaluated hub gene-immune cell correlations, while protein-protein interaction (PPI) and ROC curve analyses assessed diagnostic performance.

Results

Comprehensive bioinformatics analysis identified three hub genes-OAS2, TMEM106A, and ABCB1-with high prognostic value for ischemic stroke. Immune infiltration profiling revealed significant correlations between these genes and distinct immune cell populations, underscoring their roles in modulating the immune microenvironment. The diagnostic performance of the gene panel was robust, achieving an area under the curve (AUC) was calculated as 0.9404 (p < 0.0001; 95% CI: 0.887-0.9939) for atherosclerotic plaques, demonstrating superior accuracy compared to conventional biomarkers.

Conclusion

By integrating machine learning with multi-omics bioinformatics, we established a novel three-gene signature (OAS2, TMEM106A, ABCB1) for precise diagnosis of atherosclerosis and ischemic stroke. These genes exhibit dual diagnostic utility and may influence disease progression through immune cell modulation. Our findings provide a foundation for developing targeted therapies and biomarker-driven clinical tools.

MeSH

Cells, Genes, Ischemic Stroke, Stroke, Biomarkers, Machine Learning, Atherosclerosis, Therapeutics, Learning