Abstract
BACKGROUND: Diabetic Cardiomyopathy (DCM) is a heart condition that arises specifically from diabetes mellitus, characterized by cardiac dysfunction in the absence of coronary artery disease or hypertension. The prevalence of DCM is rising in tandem with the global increase in diabetes, necessitating the development of early diagnostic markers and therapeutic targets. This study integrates bioinformatics analysis with experimental validation to identify potential biomarkers for DCM. METHODS: We performed gene expression data mining from the Gene Expression Omnibus (GEO) database. We employed Weighted Gene Co-expression Network Analysis (WGCNA) coupled with machine learning techniques to sift through hub differentially expressed genes (DEGs). Functional enrichment and protein-protein interaction (PPI) network analysis were also conducted to pinpoint key genes functions. Subsequent in vitro and in vivo experiments were performed to validate the findings. RESULTS: Our analysis revealed six core genes significantly associated with DCM. The expression of Dusp15 was notably downregulated and validated in both high-glucose cultured cardiomyocytes and DCM animal models, suggesting its potential role in DCM pathogenesis. CONCLUSION: The integration of bioinformatics with experimental approaches has identified Dusp15 as a promising candidate for a DCM biomarker, offering valuable insights for early diagnosis and potential therapeutic development.