Identifying potential targets in endothelial to mesenchymal transition-mediated atherosclerosis using bioinformatics and machine learning

Endothelial to mesenchymal transition (EndMT) is a key driver of atherosclerosis (AS). This study integrated multiple RNA transcriptomic and single-cell transcriptomic datasets to identify shared gene expression signatures between EndMT and AS.

GEO expression profiles were analyzed via differential expression, weighted gene co-expression network analysis (WGCNA), and functional enrichment analyses. A Random Forest model evaluated key genes, validating their expression and diagnostic performance. Single-cell RNA sequencing (scRNA-seq) further assessed their expression patterns, and a Transcription Factor (TF)-mRNA-miRNA regulatory network.

Fifteen overlapping genes were identified, from which six candidates were screened through a Protein-Protein Interaction (PPI) network and validated in vitro. The application of a Random Forest model identified two key hub genes, CCL5 and IL1B, which were significantly upregulated. Their diagnostic Area Under the Curve (AUC) was >0.7 when assessed separately and increased to >0.85 in a combined model. Three shared TFs (NFKBIA, NFKB1, RELA) of the Nuclear Factor-kappa B (NF-κB) pathway were identified, highlighting its central role in disease progression.

CCL5 and IL1B are key genes potentially regulating EndMT-mediated AS via inflammatory pathways including Nucleotide-binding oligomerization domain-like (NOD-like) receptor, Tumor Necrosis Factor (TNF), and NF-κB signaling, offering promising diagnostic and therapeutic targets for AS.