Single-Cell and Bulk RNA-Seq Profiling Identifies Cell Adhesion-Related Gene Signatures in Cerebral...

Cerebral ischemia-reperfusion injury (CIRI) involves complex secondary damage mechanisms, including oxidative stress, inflammation, and blood-brain barrier disruption, in which cell adhesion-related genes (CARGs) play a central role. Therefore, systematically elucidating the expression and regulation of CARGs is crucial for identifying new therapeutic targets to mitigate inflammatory infiltration and preserve barrier integrity. Both single-cell and bulk transcriptomic data were obtained from the Gene Expression Omnibus database. The single-cell data were processed using Seurat, and CARG activity was comprehensively assessed using five scoring algorithms, including UCell and AUCell. A combined machine learning framework incorporating Boruta algorithm, LASSO regression, and random forest was employed to screen for reliable diagnostic biomarkers. Furthermore, immune infiltration profiles were evaluated using CIBERSORT, regulatory networks were constructed, and molecular subtypes were identified through consensus clustering. Single-cell analysis revealed high CARG activity in endothelial cells, smooth muscle cells, and astrocytes. Machine learning identified four robust diagnostic biomarkers (Itgb1, Fn1, Pdlim5, Kdr) with excellent predictive accuracy (AUC, 1) across multiple datasets. These biomarkers strongly correlated with immune cell infiltration, particularly macrophage polarization and mast cell activation, and successfully stratified CIRI samples into two distinct molecular subtypes. By integrating single-cell and transcriptomic data, this study systematically reveals the critical role of CARGs in CIRI. It identifies four core diagnostic genes that are closely associated with immune microenvironment remodeling and neuronal damage, thereby providing new molecular markers and a theoretical foundation for clinical diagnosis and treatment.