Key result
Bioinformatics analysis of ischemic cardiomyopathy identified differentially expressed genes enriched in metabolic and inflammatory pathways, suggesting a risk correlation with myocardial infarction.
Why the study?
To investigate differentially expressed genes in ischemic cardiomyopathy (ICM), construct regulatory networks, and evaluate the correlation with myocardial infarction risk.
Bioinformatics analysis identified key susceptibility genes and regulatory networks associated with ischemic cardiomyopathy and myocardial infarction risk.
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.
No takes yet. Share an insight, caveat, or question.
Zhang et al. (2022) studied Ischemic cardiomyopathy. Ischemic cardiomyopathy vs. Non-heart failure donors was evaluated on Differentially expressed genes and Hub gene regulatory network. Bioinformatics analysis of ischemic cardiomyopathy identified differentially expressed genes enriched in metabolic and inflammatory pathways, suggesting a risk correlation with myocardial infarction.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: