Why the study?
The ability of transcriptome analysis to identify dysregulated pathways and outcome-related genes following myocardial infarction in diabetic patients was unknown.
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Population
Left ventricular wall samples from rats
Comparison
Post-infarction diabetic vs healthy samples across two GEO datasets
Design
Bioinformatics and gene expression dataset analysis
Answers are grounded in this paper and cite their sources.
Transcriptome biomarkers for post-MI complications in diabetes remain exploratory; leaves open any role in risk stratification or therapy.
Background: The ability of transcriptome analysis to identify dysregulated pathways and outcome-related genes following myocardial infarction in diabetic patients remains unknown. The present study was designed to detect possible biomarkers associated with the incidence of post-infarction complications in diabetes to assist thedevelopment of novel treatments for this condition. Methods: Two gene expression datasets, GSE12639 and GSE6880, were downloaded from the Gene Expression Omnibus (GEO) database, and then differentially expressed genes (DEGs) were identified between post-infarction diabetics and healthy samples from the left ventricular wall of rats. These DEGs were then arranged into a protein–protein interaction (PPI) network, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were performed to explore the functional roles of these genes. Results: In total, 30 DEGs (14 upregulated and 16 downregulated) were shared between these two datasets, as identified through Venn diagram analyses. GO analyses revealed these DEGs to be significantly enriched in ovarian steroidogenesis, fatty acid elongation, biosynthesis of unsaturated fatty acids, synthesis and degradation of ketone bodies, and butanoate metabolism. The PPI network of the DEGs had 14 genes and 70 edges. We identified two key proteins, 3-hydroxymethylglutaryl-CoA synthase 2 (Hmgcs2) and Δ3, Δ2-Enoyl-CoA Delta Isomerase 1 (ECI1), and the upregulated gene Hmgcs2 with the highest score in the MCC method. We generated a co-expression network for the hub genes and obtained the top ten medications suggested for infarction with diabetes. Conclusion: Taken together, the findings of these bioinformatics analyses identified key hub genes associated with the development of myocardial infarction in diabetics. These hub genes and potential drugs may become novel biomarkers for prognosis and precision treatment in the future.
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Wu et al. (2023) studied this question.
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