Identification of hub genes in unstable atherosclerotic plaque by conjoint analysis of bioinformatics.

Unstable atherosclerotic plaque is the main pathological basis of acute coronary syndrome, which is the leading cause of death and disability worldwide. Therefore, we combined multiple bioinformatics tools to identify key genes related to unstable plaque. GSE94605 contained 7 plasma sample pools of 175 healthy and 6 sample pools of 150 unstable angina pectoris (UAP) patients, and detected with miRNA array while GSE60993 collected peripheral blood from 7 normal and 9 UAP, and detected with mRNA array. GSE120521 collected carotid plaques from 4 patients and dissected in stable and unstable regions, then detected with RNA-seq. Differentially expressed miRNAs (DEMs) and genes (DEGs) in UAP were re-analyzed. Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) and Protein-protein interaction (PPI) network were applied on top 10 up-regulated or down-regulated DEMs targets, and whole DEGs. MiRNAs-mRNAs network was constructed with these DEMs and DEGs, and the expression profile of genes within the network was finally validated in GSE120521. Totally, 263 up-regulated and 201 down-regulated DEMs were identified in GSE94605, and 78 up-regulated and 29 down-regulated DEGs were identified in GSE60993. Subsequently, a miRNAs-mRNAs network was constructed with 6 up-regulated miRNAs targeted to 12 down-regulated genes, and 4 down-regulated miRNAs targeted to 8 up-regulated genes. Finally, MORF4L2, RAB3IL1 and MMP9 within the network were considered as hub genes in unstable plaque progression after being validated in GSE120521. These 3 genes may provide new targets for diagnosis and therapy of unstable atherosclerotic plaque. Copyright © 2020. Published by Elsevier Inc.

Citation

Rui Zhang, Zhenjun Ji, Yuyu Yao, Wenjie Zuo, Mingming Yang, Yangyang Qu, Yamin Su, Genshan Ma, Yongjun Li. Identification of hub genes in unstable atherosclerotic plaque by conjoint analysis of bioinformatics. Life sciences. 2020 Dec 01;262:118517

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PMID: 33011223

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