Identification and validation of diagnostic markers of atherosclerosis progression via bioinformatics strategies

Arteriosclerosis (AS) is a leading cause of cardiovascular disease, imposing significant burdens on families and society. However, the underlying mechanisms remain unclear. This study aims to elucidate these mechanisms and explore potential pharmacological treatments through the integration of bioinformatics.

The GSE28829 dataset was retrieved from the GEO database. Differential gene expression between early- and late-stage AS plaques in GSE28829 was identified via the limma package. We focused on the intersecting genes associated with endoplasmic reticulum stress and mitochondrial damage. LASSO regression analysis was applied to pinpoint potential core genes associated with AS. An protein interaction network was constructed, with a focus on the hub genes within this network. Assessment of immune cell infiltration levels was achieved via the ssGSEA method and confirmed via CIBERSORT. We used the DrugBank database to predict small molecule drugs that could intervene in AS progression.

In this study, DEGs associated with AS were identified, and hub genes, including FCGR3A, ITGB2, TYROBP, FCGR2B, CTSS, FCER1G, CD86, TLR2, C1QB, and C1QA, were identified. We also found a strong correlation between the hub genes and immune processes, indicating that ER stress and mitochondrial damage were correlated with the activation of immune processes. Additionally, a diagnostic model for AS was established, demonstrating the substantial predictive value of these hub genes.

This study reveals the involvement of genes related to endoplasmic reticulum stress and mitochondrial damage in the pathogenesis of AS. These hub genes offer new directions for further research and the development of pharmacological treatments.