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ORIGINAL RESEARCH article
Front. Neurol.
Sec. Neurological Biomarkers
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1507855
This article is part of the Research TopicImmunity, Atherosclerosis and Cardiovascular Disease: An Interdisciplinary Approach to Cardiometabolic HealthView all 16 articles
Identification immune-related Hub genes in diagnosing atherosclerosis with ischemic stroke through comprehensive bioinformatics analysis and Machine Learning
Provisionally accepted- 1Gaoping District People's Hospital of Nanchong City, Nanchong, Shanxi Province, China
- 2People's Hospital of Yilong County, Yilong, Sichuan, China
- 3People's Hospital of Yilong County, Yilong, China
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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.We integrated Weighted Gene Co-expression Network Analysis (WGCNA), CIBERSORT, and machine learning (LASSO/Random Forest) to identify diseaseassociated modules and hub genes. Immune infiltration analysis evaluated hub geneimmune 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.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.
Keywords: ischemic stroke, Atherosclerosis, WGCNA, machine learning, Immune Cell Infiltration, bioinformatics
Received: 09 Oct 2024; Accepted: 07 Apr 2025.
Copyright: © 2025 Long, Tang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ming Zhang, People's Hospital of Yilong County, Yilong, China
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