ArticleJournal of thoracic disease2022
Analysis of susceptibility genes and myocardial infarction risk correlation of ischemic cardiomyopathy based on bioinformatics.
Article in Journal of thoracic disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- Screening and identification of hub genes for ischemic cardiomyopathy and construction and validation of a clinical prognosis model using bioinformatics analysis.Journal of thoracic disease · 2024Article
- Association between regulatory T cells and ischemic heart disease: a Mendelian randomization study.Journal of thoracic disease · 2024Article
- Expression Pattern and Molecular Mechanism of Oxidative Stress-Related Genes in Myocardial Ischemia-Reperfusion Injury.Journal of cardiovascular development and disease · 2023Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.