Identification of Metabolism-Related Hub Genes in Heart Failure via Comprehensive Transcriptome Analysis.

BACKGROUND: Metabolic dysfunction is a key driver of heart failure (HF) progression. Identifying metabolic hub genes in HF may reveal novel therapeutic targets. METHODS: Transcriptomic datasets from HF patients (GEO database) and metabolism-related genes (PathCards) were analyzed. Differentially expressed genes (DEGs) were intersected with metabolism-related genes, followed by the application of the LASSO, Random Forest, and XGBoost algorithms to prioritize hub genes. Candidate genes were validated via WGCNA, an HF mouse model, and plasma metabolomics. Diagnostic performance and metabolic associations were assessed using ROC analysis and ssGSEA. RESULTS: We identified 1115 HF-associated DEGs (701 upregulated, 414 downregulated), with 119 linked to metabolism. The machine learning algorithms prioritized five genes, including SDC2, which was also validated using WGCNA and the mouse HF model. SDC2 mRNA and protein expression levels were markedly elevated in HF and demonstrated strong diagnostic accuracy. ssGSEA revealed the expression of SDC2 was correlated with dysregulated metabolic pathways, including fatty acid biosynthesis and glycerolipid metabolism, which are potentially associated with metabolic alterations in HF. CONCLUSIONS: SDC2 emerges as a central regulator bridging metabolic dysfunction and HF pathogenesis, showing potential as a diagnostic biomarker and therapeutic target.