Abstract
Background: Autophagy exerts a vital role in the development of atherosclerotic lesions. Mounting evidence suggests a significant link between autophagy and atherosclerosis. Methods: Two atherosclerotic plaque datasets were integrated from the Gene Expression Omnibus (GEO) database. After differentially expressed genes (DEGs) were determined, enrichment analyses were subsequently performed on DEGs. We employed weighted gene coexpression network analysis (WGCNA) and cross-linked these modules with DEGs and autophagy-related genes. Subsequently, a prediction model was established for evaluation. RT-PCR was adopted to identify hub gene expression. The consensus clustering analysis on the overlapping genes was executed. Evaluation of immune infiltration was conducted on the merged dataset. A TF-miRNA-mRNA regulatory network was then established for the hub genes. Results: The differential gene expression analysis uncovered 259 DEGs. Enrichment analysis showed that immune and inflammatory reactions were related to atherosclerosis. By intersecting DEGs, WGCNA module genes, and ARGs, 13 overlapping genes were obtained. Four machine learning models identified seven hub genes. Furthermore, six of the seven genes demonstrated potential for disease diagnosis. The prediction model, based on the expression levels of these six genes, yielded satisfactory results. RT-PCR analysis demonstrated that the mRNA expression of six genes meets expectations. Consensus clustering divides 13 overlapping genes into two clusters, C1 and C2, with significant differences in immune infiltration. Immune cell infiltration demonstrated heightened immune activity within the atherosclerotic plaque group. A TF-miRNA-mRNA regulatory network was established for the six genes. Conclusion: It is anticipated that these six genes may serve as significant and valuable targets for future research into atherosclerosis.