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Copyright (c) 2023 Rui Yan, Jiahui Song, Ming Guo, Minghui Hao
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The undersigned hereby assign all rights, included but not limited to copyright, for this manuscript to CMB Association upon its submission for consideration to publication on Cellular and Molecular Biology. The rights assigned include, but are not limited to, the sole and exclusive rights to license, sell, subsequently assign, derive, distribute, display and reproduce this manuscript, in whole or in part, in any format, electronic or otherwise, including those in existence at the time this agreement was signed. The authors hereby warrant that they have not granted or assigned, and shall not grant or assign, the aforementioned rights to any other person, firm, organization, or other entity. All rights are automatically restored to authors if this manuscript is not accepted for publication.Bioinformatics Analysis of Differentially Expressed Genes in Ischemic Cardiomyopathy Using GEO Database
Corresponding Author(s) : Rui Yan
Cellular and Molecular Biology,
Vol. 69 No. 1: Issue 1
Abstract
It was to analyze differentially expressed genes and their expression characteristics in ischemic cardiomyopathy (ICM) by bioinformatics and provide targets for drug therapy of ICM. For this purpose, the gene expression data of ICM in the gene expression omnibus (GEO) database were used, the differentially expressed genes between healthy myocardium and ICM myocardium were screened by R language, and then the differentially expressed genes were analyzed by protein-protein interaction (PPI), gene ontology (GO), and KEGG to select the key genes. Results showed that the useful genes of ICM were successfully screened in the GEO database, and KEGG pathway analysis was performed for the differentially expressed genes in ICM tissues, including the main pathways: viral carcinogenesis, energy metabolism, viral response, oxidative phosphorylation, influenza A, extracellular matrix receptor interaction, Epstein-Barr virus infection, chemokine receptor pathway, phagosome, proteasome, and protein digestion and absorption. PPI network analysis showed that C3, F5, FCGR3A, APOB, PENK, LUM, CHRDL1, FCGR3A, CIQB, and FMOD were critical genes. In conclusion, bioinformatics can screen out the key genes in ICM, which is helpful to understand the treatment of drug targets in ICM patients.
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