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

BMC Medical Genomics

Research

Chao Liu¹  Mincai Li²  Qingjie Li³  Qiaofeng Zhou³  Shihuan Li³  Suqin Li⁴  Wenli Liao⁴  Liangzhu Yu⁴  Changhan Ouyang⁵  Hongli Xia⁶ 

[1] Hubei Key Laboratory of Diabetes and Angiopathy, Xianning Medical College, Hubei University of Science and Technology, 437100, Xianning, People’s Republic of China;Hubei Key Laboratory of Diabetes and Angiopathy, Xianning Medical College, Hubei University of Science and Technology, 437100, Xianning, People’s Republic of China;School of Basic Medical Sciences, Xianning Medical College, Hubei University of Science and Technology, 437100, Xianning, People’s Republic of China;Hubei Key Laboratory of Diabetes and Angiopathy, Xianning Medical College, Hubei University of Science and Technology, 437100, Xianning, People’s Republic of China;School of Pharmacy, Xianning Medical College, Hubei University of Science and Technology, 437100, Xianning, People’s Republic of China;School of Basic Medical Sciences, Xianning Medical College, Hubei University of Science and Technology, 437100, Xianning, People’s Republic of China;School of Pharmacy, Xianning Medical College, Hubei University of Science and Technology, 437100, Xianning, People’s Republic of China;The Central Hospital of Xianning, Xianning Medical College, Hubei University of Science and Technology, 437100, Xianning, People’s Republic of China;

关键词: Atherosclerosis;    Differentially expressed genes (DEGs);    Network modules;    Biomarker;    Bioinformatics analysis;   

DOI  :  10.1186/s12920-023-01533-8

 received in 2022-10-15, accepted in 2023-05-05,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundAtherosclerosis (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.MethodsGSE100927 gene expression profiles, including 69 AS samples and 35 healthy controls, were downloaded from Gene Expression Omnibus database and indenfied for key genes and pathways in AS.ResultsA total of 443 differentially expressed genes (DEGs) between control and AS were identified, including 323 down-regulated genes and 120 up-regulated genes. The Gene ontology terms enriched by the up-regulated DEGs were associated with the regulation of leukocyte activation, endocytic vesicle, and cytokine binding, while the down-regulated DEGs were associated with 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. Using the modular analysis of Cytoscape, we identified 3 modules mainly involved in 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.ConclusionsOur data showed the pathway of Osteoclast differentiation and Leishmaniasis was involved in the AS process and we developed a 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.

【 授权许可】

CC BY   
© The Author(s) 2023

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