A ten-genes-based diagnostic signature for atherosclerosis

BMC Cardiovascular Disorders

Rui Hu¹  Limin Feng²  Xin Qi³  Jin Wang⁴  Lili Zuo⁵  Zhihua Yang⁶  Feng Zhu⁶ 

[1] Center for Drug Monitoring and Evaluation Department, Center for Drug Monitoring and Evaluation in Zhangjiakou;Department of Cardiology, The Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine;Department of Cardiology, Tianjin Union Medical Center;Department of Cardiovascular Disease, ZiBo Hospital of Traditional Chinese Medicine;Department of Neonatal, ZiBo Maternal and Child Health Hospital;Graduate School, Tianjin University of Traditional Chinese Medicine;

关键词: Atherosclerosis;    GO analysis;    KEGG analysis;    PPI network;    Logistic regression diagnostic mode;   

DOI  :  10.1186/s12872-021-02323-9

来源: DOAJ

【 摘 要 】

Abstract Background Atherosclerosis is the leading cause of cardiovascular disease with a high mortality worldwide. Understanding the atherosclerosis pathogenesis and identification of efficient diagnostic signatures remain major problems of modern medicine. This study aims to screen the potential diagnostic genes for atherosclerosis. Methods We downloaded the gene chip data of 135 peripheral blood samples, including 57 samples with atherosclerosis and 78 healthy subjects from GEO database (Accession Number: GSE20129). The weighted gene co-expression network analysis was applied to identify atherosclerosis-related genes. Functional enrichment analysis was conducted by using the clusterProfiler R package. The interaction pairs of proteins encoded by atherosclerosis-related genes were screened using STRING database, and the interaction network was further optimized with the cytoHubba plug-in of Cytoscape software. Results The logistic regression diagnostic model was constructed to predict normal and atherosclerosis samples. A gene module which included 532 genes related to the occurrence of atherosclerosis were screened. Functional enrichment analysis basing on the 532 genes identified 235 significantly enriched GO terms and 44 significantly enriched KEGG pathways. The top 50 hub genes of the protein–protein interaction network were identified. The final logistic regression diagnostic model was established by the optimal 10 key genes, which could distinguish atherosclerosis samples from normal samples. Conclusions A predictive model based on 10 potential atherosclerosis-related genes was obtained, which should shed light on the diagnostic research of atherosclerosis.

【 授权许可】

Unknown