Data Sheet 1_Identification of glycolysis- and myocardial fibrosis-related diagnostic biomarkers in heart failure: an integrative bioinformatics and machine learning study.csv

Standard bioinformatics methods were used for functional enrichment analysis, construction of regulatory networks, and immune infiltration analysis.</p>Results<p>A total of 14 glycolysis- and myocardial fibrosis-related differentially expressed genes were identified, with five key genes (SERPINA3, HBB, NPPA, LUM, and OGN) screened via machine learning. The diagnostic model achieved excellent performance in both the training (AUC=0.983) and validation (AUC=0.938) sets.

Among individual genes, SERPINA3 demonstrated the highest diagnostic accuracy (AUC=0.998), while the combined model provided complementary advantages in terms of stability and multidimensional biomarker coverage. Enrichment analysis revealed involvement in blood pressure regulation and extracellular matrix organization. Immune infiltration analysis identified 11 immune cell types with differential infiltration, and regulatory networks were constructed including the potential therapeutic compounds identified.</p>Conclusions<p>In this study, we identified five key genes associated with glycolysis and myocardial fibrosis in HF and constructed a robust diagnostic model with excellent predictive performance.

These findings provide novel insights into the molecular mechanisms of HF and offer promising candidates for diagnostic biomarker development and therapeutic targeting.</p>