ArticleFrontiers in cardiovascular medicine2026
Identification of glycolysis- and myocardial fibrosis-related diagnostic biomarkers in heart failure: an integrative bioinformatics and machine learning study.
Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Heart failure (HF) is a leading cause of global morbidity and mortality, yet the crosstalk between energy metabolism reprogramming and myocardial fibrosis remains poorly understood. This study aimed to identify glycolysis- and myocardial fibrosis-related diagnostic biomarkers in HF through integrative bioinformatics analysis. Methods: Datasets GSE57338 and GSE76701 were obtained from the Gene Expression Omnibus database. Glycolysis-related genes and myocardial fibrosis-related genes were sourced from GeneCards and PubMed. Differentially expressed genes were identified, intersected with gene sets, and screened using three machine learning algorithms. A logistic regression diagnostic model was constructed and validated. Standard bioinformatics methods were used for functional enrichment analysis, construction of regulatory networks, and immune infiltration analysis. Results: A total of 14 glycolysis- and myocardial fibrosis-related differentially expressed genes were identified, with five key genes ( Conclusions: 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.
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