ArticleJournal of clinical laboratory analysis2026
Multi-Omics and Machine Learning Integration Identifies Key Endothelial Modules in Carotid Artery Stenosis.
Article in Journal of clinical laboratory analysis, 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
backgroundCarotid artery stenosis (CAS) is a major cause of ischemic stroke, yet reliable molecular biomarkers for early identification remain limited.
methodsWe integrated single-cell RNA sequencing (scRNA-seq), bulk transcriptomics, and in-house multi-omics data, applying WGCNA and machine learning to identify endothelial cell-derived diagnostic biomarkers, validated across independent GEO and ZZ cohorts at single-cell, transcriptomic, and proteomic levels.
resultsscRNA-seq identified endothelial enrichment in CAS and yielded 836 markers. Integrative analysis (DEGs + WGCNA) defined 80 candidates, with NRP1 and XAF1 selected by machine learning. Both were consistently upregulated and validated across multi-omics datasets, showing strong diagnostic performance.
conclusionNRP1 and XAF1 represent novel endothelial cell-derived biomarkers with potential utility for early CAS screening and clinical diagnosis.
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