Identification of the potential biomarker OLFML2A in carotid atherosclerotic plaque rupture using bioinformatics and machine learning

This study aimed to identify potential biomarkers associated with ruptured plaques in carotid artery stenosis (CAS) using bioinformatics and machine learning approaches and to elucidate the underlying molecular mechanisms of symptomatic CAS. Three GEO datasets related to symptomatic CAS were analyzed. Key genes were identified using LASSO, SVM-RFE, and random forest algorithms. Diagnostic performance was assessed using ROC analysis. A miRNA-mRNA regulatory axis was constructed with Cytoscape, and interactions were validated by dual-luciferase reporter assays. An ox-LDL-induced endothelial cell injury model was established to explore molecular mechanisms. The expression of inflammatory cytokines, adhesion molecules, apoptosis-related genes, and Wnt1/β-catenin pathway components was detected by qRT-PCR and Western blotting. Validation analysis confirmed OLFML2A as a potential biomarker for symptomatic CAS and as a regulatory target of miR-331-3p. OLFML2A was confirmed to directly interact with miR-331-3p. Silencing OLFML2A increased the expression of pro-inflammatory cytokines (IL-6, TNF-α, MCP-1), adhesion molecules (VCAM-1, ICAM-1), and the pro-apoptotic gene Bax, while decreasing the anti-apoptotic gene Bcl-2. Moreover, OLFML2A knockdown suppressed activation of the Wnt1/β-catenin signaling pathway. This study identifies OLFML2A as a novel biomarker and potential regulator of endothelial injury in symptomatic CAS. OLFML2A may promote ox-LDL-induced endothelial dysfunction by modulating the Wnt/β-catenin pathway.