Establishment of a prediction model and immune infiltration characteristics of atherosclerosis progression based on neutrophil extracellular traps-related genes

Neutrophil extracellular traps (NETs) are a novel regulatory mechanism of neutrophils, which can promote endothelial cell inflammation through direct or indirect pathways and play a crucial role in the occurrence and development of atherosclerosis (AS). This study aimed to explore the mechanism of NETs in AS progression using bioinformatics methods. We acquired datasets from Gene Expression Omnibus (GEO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) and used Weighted Gene Co-expression Network Analysis (WGCNA) to identify communal genes shared by NET-related genes. Gene Ontology (GO) and KEGG enrichment analyses were conducted. Machine learning algorithms were used to identify hub genes, then protein-protein interaction (PPI), CO-expression network construction, nomogram model building and validation, and immune infiltration analysis were performed. Data were verified by qPCR. Four datasets related to AS progression were included. Module genes shared 27 genes with NRGs. Pathways related to immune regulation, leukocyte migration, and others were identified. Machine learning revealed SLC25A4 and C5AR1 as hub genes. SLC25A4 and C5AR1 were confirmed to have predictive value for intraplaque hemorrhage (IPH), advanced AS plaques, ruptured plaques, and unstable plaques. These pathologic changes are closely related to AS progression and are the main contents of AS progression. Immune infiltration analysis revealed 4 immune cells associated with IPH, among them resting dendritic cells, which were closely related to SLC25A4. In qPCR validation, SLC25A4 and C5AR1 were shown to be consistent with the bioinformatic analysis results. These findings provided novel insights into the molecular characteristics of NRGs and potential therapies for AS progression.