Dataset Information

Analysis of susceptibility genes and myocardial infarction risk correlation of ischemic cardiomyopathy based on bioinformatics.

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.

SUBMITTER: Zhang N

PROVIDER: S-EPMC9562554 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Analysis of susceptibility genes and myocardial infarction risk correlation of ischemic cardiomyopathy based on bioinformatics.

Zhang Nai N Yang Chuang C Liu Yu-Juan YJ Zeng Peng P Gong Tao T Tao Lu L Li Xin-Ai XA

Journal of thoracic disease 20220901 9

<h4>Background</h4>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.<h4>Methods</h4>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 Encycloped ...[more]

PMID: 36245596

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Project description:BackgroundAcute myocardial infarction (AMI), a subset of acute coronary syndrome, remains the major cause of mortality worldwide. Mitochondrial dysfunction is critically involved in AMI progression, and mitophagy plays a vital role in eliminating damaged mitochondria. This study aimed to explore mitophagy-related biomarkers and their potential molecular basis in AMI.MethodsAMI datasets (GSE24519 and GSE34198) from the Gene Expression Omnibus database were combined and the batch effects were removed. Differentially expressed genes (DEGs) in AMI were selected, intersected with mitophagy-related genes for mitophagy-related DEGs (MRDEGs), and then subjected to enrichment analyses. Next, the MRDEGs were screened using machine learning methods (logistic regression analysis, RandomForest, least absolute shrinkage and selection operator) to construct a diagnostic risk model and select the key genes in AMI. The diagnostic efficacy of the model was evaluated using a nomogram. Moreover, the infiltration patterns of different immune cells in two risk groups were compared. We also explored the interactions between the key genes themselves or with miRNAs/transcription factors (TFs) and drug compounds and visualized the protein structure of the key genes. Finally, we explored and validated the expression of key genes in plasma samples of patients with an AMI and healthy individuals.ResultsWe screened 28 MRDEGs in AMI. Based on machine learning methods, 12 key genes were screened for the diagnostic risk model, including AGPS, CA2, CAT, LTA4H, MYO9B, PRDX6, PYGB, SIRT3, TFEB, TOM1, UBA52, and UBB. The nomogram further revealed the accuracy of the model for AMI diagnosis. Moreover, we found a lower abundance of immune cells such as gamma delta T and natural killer cells in the high-risk group, and the expression of key genes showed a significant correlation with immune infiltration levels in both groups. Finally, 64 miRNA-mRNA pairs, 75 TF-mRNA pairs, 119 RNA-binding protein-mRNA pairs, and 32 drug-mRNA pairs were obtained in the interaction networks.ConclusionsIn total, 12 key MRDEGs were identified and a risk model was constructed for AMI diagnosis. The findings of this study might provide novel biomarkers for improving the detection of AMI.

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Dataset Information

Analysis of susceptibility genes and myocardial infarction risk correlation of ischemic cardiomyopathy based on bioinformatics.

Background

Methods

Results

Conclusions

Publications

Analysis of susceptibility genes and myocardial infarction risk correlation of ischemic cardiomyopathy based on bioinformatics.

Similar Datasets

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