Analysis of Susceptibility Genes and Myocardial Infarction Risk Correlation of Ischemic Cardiomyopathy Based on Bioinformatics

Overview

Journal J Thorac Dis

Publisher AME Publishing Company

Specialty Pulmonary Medicine

Date 2022 Oct 17

PMID 36245596

Authors

Nai Zhang

Chuang Yang

Yu-Juan Liu

Peng Zeng

Tao Gong

Lu Tao

Xin-Ai Li

Affiliations

Soon will be listed here.

Abstract

Background: The present study was to investigated differential expressed genes (GEGs) in ischemic cardiomyopathy (ICM), and to construct regulation networks, and to study the correlation between myocardial infarction risk.

Methods: Data sets were downloaded from the Gene Expression Omnibus (GEO) to screen out messenger RNA (mRNA) and long non-coding RNA (lncRNA) differentially expressed between ICM samples and normal samples. Gene Ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Differentially expressed mRNA and lncRNA were analyzed, and bioinformatics methods were used to predict and analyze microRNA (miRNA), and a competing endogenous RNA (Hub gene) regulatory network was constructed. Using the Limma software package in R language, DEGs of ICM were screened with non-heart failure donors as the control group under the conditions that the differential expression ratio was not less than 2 times, and the corrected P value was <0.05. The ClusterProfiler software package was used for GO enrichment analysis and KEGG enrichment analysis. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) 11.0 online database was used to screen key genes for protein-protein interaction (PPI) network analysis.

Results: The GO function analysis and KEGG pathway analysis showed that the DEGs were significantly enriched in metabolic pathways, oxidative phosphorylation, extracellular matrix receptor interactions, and other pathways, and were closely related to fibrosis, collagen catabolic process, and inflammatory response function, and a Hub gene regulatory network related to ICM lncRNA was constructed. Bioinformatics methods were used to effectively analyze the DEGs of ICM, and the Hub gene regulatory network of ICM was successfully constructed.

Conclusions: This study identified a certain risk correlation between ICM susceptibility genes and myocardial infarction.

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