ArticleScientific reports2024
Identification of hub genes associated with diabetic cardiomyopathy using integrated bioinformatics analysis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Integrated transcriptomic and Mendelian randomization analysis identifies novel biomarkers for type 2 diabetes-associated cardiac dysfunction: cynaropicrin as a candidate intervention.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- In silico design of the multi-epitope vaccine for lung adenocarcinoma based on hub gene-derived neoantigens.BMC cancer · 2026Article
- Integrative multi-omics analysis unveils the regulatory landscape of diabetic cardiomyopathy: from chromatin accessibility to transcript isoforms and epitranscriptome.Frontiers in endocrinology · 2026Article
- Aerobic exercise inhibits oxidative stress and improves diabetic cardiomyopathy in rats by activating the PROC/PAR1/Nrf2/HO-1 signaling pathway.Frontiers in physiology · 2026Article
- Article
- Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets.Scientific reports · 2025Article
- Murine Model Insights: Identifying Dusp15 as a Novel Biomarker for Diabetic Cardiomyopathy Uncovered Through Integrated Omics Analysis and Experimental Validation.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
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7 authors.
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Abstract
Diabetic cardiomyopathy (DCM) is a common cardiovascular complication of diabetes, which may threaten the quality of life and shorten life expectancy in the diabetic population. However, the molecular mechanisms underlying the diabetes cardiomyopathy are not fully elucidated. We analyzed two datasets from Gene Expression Omnibus (GEO). Differentially expressed and weighted gene correlation network analysis (WGCNA) was used to screen key genes and molecules. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI) network analysis were constructed to identify hub genes. The diagnostic value of the hub gene was evaluated using the receiver operating characteristic (ROC). Quantitative real-time PCR (RT-qPCR) was used to validate the hub genes. A total of 13 differentially co-expressed modules were selected by WGCNA and differential expression analysis. KEGG and GO analysis showed these DEGs were mainly enriched in lipid metabolism and myocardial hypertrophy pathway, cytomembrane, and mitochondrion. As a result, six genes were identified as hub genes. Finally, five genes (Pdk4, Lipe, Serpine1, Igf1r, and Bcl2l1) were found significantly changed in both the validation dataset and experimental mice with DCM. In conclusion, the present study identified five genes that may help provide novel targets for diagnosing and treating DCM.
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