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

• Published in: Herz Vol. 49; no. 3; pp. 198 - 209
• Main Authors: Ni, Jiajun, Huang, Kaijian, Xu, Jialin, Lu, Qi, Chen, Chu
• Format: Journal Article
• Language: English
• Published: Heidelberg Springer Medizin 01.06.2024; Springer Nature B.V
• Subjects: Adaptor Proteins, Signal Transducing; Arteriosclerosis; Atherosclerosis; Atherosclerosis - genetics; Biomarkers; Biomarkers - metabolism; Cardiology; CD14 antigen; CD163 antigen; Databases, Genetic; Encyclopedias; Gene expression; Gene Expression Profiling; Gene Ontology; Gene Regulatory Networks - genetics; Genes; Genomes; Humans; Immune system; Internal Medicine; Medicine; Medicine & Public Health; Membrane Proteins; Network analysis; Network hubs; Ontology; Original Articles; Protein Interaction Maps - genetics; Proteins; Receptor, Macrophage Colony-Stimulating Factor; Receptors, Granulocyte-Macrophage Colony-Stimulating Factor - genetics; Strings; TLR2 protein; Toll-like receptors; Gene-Ontology- und Kyoto-Encyclopedia-of-Genes-and-Genomes-Analyse; Klinische Diagnose; Immune infiltration; Inflammation; Immuninfiltration; Clinical diagnosis; Entzündung; Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway; Protein–protein interaction network; Protein-Protein-Interaktions-Netzwerk
• ISSN: 0340-9937; 1615-6692
• DOI: 10.1007/s00059-023-05204-3

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