Atherosclerosis is a chronic inflammatory disease, and the role of mitophagy in its pathogenesis remains unclear. To address this, this study integrated atherosclerosis-related transcriptome data and single-cell sequencing data from the GEO database, obtained mitophagy-related genes from MSigDB, and assessed mitophagy activity via GSEA and ssGSEA; WGCNA was used to screen disease- and mitophagy-associated module genes, and their intersection with differentially expressed genes yielded 286 candidate genes. A protein-protein interaction (PPI) network and cytoHubba algorithms were subsequently used to prioritize key genes, and dual machine learning algorithms (LASSO regression and SVM-RFE) to screen potential biomarkers, which were designated as CCR5, CD163, CSF1R, and FCGR3A. The expression patterns of all four candidates were further assessed in the independent GSE43292 dataset and were concordant with those observed in the discovery dataset. GSEA of samples stratified by candidate-gene expression identified enrichment of gene sets related to myogenesis, allograft rejection, and inflammatory responses; candidate drug-target associations were predicted using DSigDB, and exploratory AutoDock analysis suggested a feasible interaction between sunitinib and CSF1R. Additionally, exploratory single-cell analysis of three atherosclerosis patients identified seven major cell populations, with CD163, CSF1R, and FCGR3A showing macrophage-enriched expression within this dataset. Collectively, this study provides hypothesis-generating evidence for an association between mitophagy-related transcriptional patterns and atherosclerosis. Four candidate biomarkers (CCR5, CD163, CSF1R, and FCGR3A) were prioritized, and a potential CSF1R-sunitinib interaction was proposed for future experimental validation.
