Identification of key genes and pathways in atherosclerosis using integrated bioinformatics analysis

Abstract

Abstract Atherosclerosis (AS) is a chronic inflammatory disease that might induce severe cardiovascular events, such as myocardial infarction and cerebral infarction. These risk factors in the pathogenesis of AS remain uncertain and further research is needed. This study aims to explore the potential molecular mechanisms of AS by bioinformatics analyses. GSE100927 gene expression profiles, including 69 AS samples and 35 health controls, were downloaded from Gene Expression Omnibus (GEO) database. A total of 443 differentially expressed genes (DEGs) between control and AS were identified, including 323 down-regulated genes and 120 up-regulated genes. For GO term analyses, the up-regulated DEGs were enriched regulation of leukocyte activation, endocytic vesicle, and cytokine binding, while the down-regulated DEGs were enriched in negative regulation of cell growth, extracellular matrix, and G protein-coupled receptor binding. KEGG pathway analysis showed that the up-regulated DEGs were enriched in Osteoclast differentiation and Phagosome, while the down-regulated DEGs were enriched in vascular smooth muscle contraction and cGMP-PKG signaling pathway. The modular analysis of Cytoscape identified 3 modules were Leishmaniasis and Osteoclast differentiation. The GSEA analysis showed the up-regulated gene sets were enriched in the ribosome, ascorbated metabolism, and propanoate metabolism. The LASSO Cox regression analysis showed the top 3 genes were TNF, CX3CR1 and COL1R1. Finally, we found these immune cells were conferred significantly higher infiltrating density in the AS group. Our data showed the pathway of Osteoclast differentiation and Leishmaniasis were involved in the AS process and we developed three-gene model base on the prognosis of AS. These findings clarified the gene regulatory network of AS and may provide a novel target for AS therapy.