ArticleFrontiers in endocrinology2024
Identification of important modules and biomarkers in diabetic cardiomyopathy based on WGCNA and LASSO analysis.
Article in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 10 citations in OpenAlex.
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- Integrated Identification of NAD⁺ Metabolism-Associated Candidate Genes.Molecular neurobiology · 2026Article
- 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
- The significance of the ketolytic gene OXCT1 in metabolism, intracellular signaling and disease development.Cellular and molecular life sciences : CMLS · 2026Review
- Multi-omics profiling of the diabetic human heart reveals coupled dysregulation in lipid metabolism, mitophagy, and extracellular matrix remodeling.Genome medicine · 2026Article
- Integrative metabolome and transcriptome of different tree size reveal wood formation in Corymbia citriodora.BMC genomics · 2026Article
- Overexpression of MFAP4 inhibits the proliferation, migration, and invasion of bladder cancer cells.Discover oncology · 2026Article
- EGLN1 inhibition reverses angiogenesis impairment in hyperglycemia by activating autophagy.Scientific reports · 2025Article
- Deep phenotyping of a modified diabetic cardiomyopathy mouse model which reflects clinical disease progression.Diabetology & metabolic syndrome · 2025Article
- Identification biomarkers and therapeutic targets of disulfidptosis-related in rheumatoid arthritis via bioinformatics, molecular dynamics simulation, and experimental validation.Scientific reports · 2025Article
- Article
- Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning.Frontiers in endocrinology · 2025Article
- Exploring the key target molecules of angiogenesis in diabetic cardiomyopathy based on bioinformatics analysis.Frontiers in endocrinology · 2025Article
- Article
- Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Frontiers in plant science · 2024Article
- Construction of a potentially functional long noncoding RNA-microRNA-mRNA network in diabetic cardiomyopathy.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2024Article
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6 authors at 3 institutions in 1 country.
Funding
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Abstract
Background: Diabetic cardiomyopathy (DCM) lacks specific and sensitive biomarkers, and its diagnosis remains a challenge. Therefore, there is an urgent need to develop useful biomarkers to help diagnose and evaluate the prognosis of DCM. This study aims to find specific diagnostic markers for diabetic cardiomyopathy. Methods: Two datasets (GSE106180 and GSE161827) from the GEO database were integrated to identify differentially expressed genes (DEGs) between control and type 2 diabetic cardiomyopathy. We assessed the infiltration of immune cells and used weighted coexpression network analysis (WGCNA) to construct the gene coexpression network. Then we performed a clustering analysis. Finally, a diagnostic model was built by the least absolute shrinkage and selection operator (LASSO). Results: A total of 3066 DEGs in the GSE106180 and GSE161827 datasets. There were differences in immune cell infiltration. According to gene significance (GS) > 0.2 and module membership (MM) > 0.8, 41 yellow Module genes and 1474 turquoise Module genes were selected. Hub genes were mainly related to the "proteasomal protein catabolic process", "mitochondrial matrix" and "protein processing in endoplasmic reticulum" pathways. LASSO was used to construct a diagnostic model composed of OXCT1, CACNA2D2, BCL7B, EGLN3, GABARAP, and ACADSB and verified it in the GSE163060 and GSE175988 datasets with AUCs of 0.9333 (95% CI: 0.7801-1) and 0.96 (95% CI: 0.8861-1), respectively. H9C2 cells were verified, and the results were similar to the bioinformatics analysis. Conclusion: We constructed a diagnostic model of DCM, and OXCT1, CACNA2D2, BCL7B, EGLN3, GABARAP, and ACADSB were potential biomarkers, which may provide new insights for improving the ability of early diagnosis and treatment of diabetic cardiomyopathy.
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