Novel biomarkers identified by weighted gene co-expression network analysis for atherosclerosis

This study aimed to screen out the potential diagnostic biomarkers for atherosclerosis (AS).

We downloaded the gene expression profiles GSE66360, GSE28829, GSE41571, GSE71226, and GSE100927 from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the "limma" package in R. Weighted gene co-expression network analysis (WGCNA) was applied to reveal the correlation between genes in different samples. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. The interaction pairs of proteins were retained by the STRING database, and the protein-protein interaction (PPI) network was visualized with the hub genes. Finally, the R packages "ggpubr" and "preprocessCore" were used to analyze immune cell infiltration.

In total, 40 overlapping genes both in GSE66360 and GSE28829 were found to be related to the occurrence of AS. Further, the top 10 network hub genes including TYROBP, CSF1R, TLR2, CD14, CCL4, FCER1G, CD163, TREM1, PLEK, and C5AR1 were identified as significant key genes. Moreover, four genes (TYROBP, CSF1R, FCGR1B, and CD14) were verified that could efficiently diagnose AS. Finally, the gene TYROBP was found to have a strong correlation with immune-infiltrating cells.

Our study identified four genes (TYROBP, CSF1R, FCGR1B, and CD14) that may be effective biomarkers for AS, with the potential to guide the clinical diagnosis of AS.