Unveiling the Hypoxia-Driven Molecular Mechanisms in Ischemic Heart Failure Based on Bioinformatics, Machine Learning and Bayesian Networks

Abstract Hypoxia is a critical determinant in the etiology and progression of ischemic heart failure, but its underlying molecular mechanisms and regulatory interactions remain largely enigmatic. In our study, we employed differential analysis, coupled with WGCNA and the MSigDB database, to identify nine genes associated with hypoxia in heart failure. GO and KEGG enrichment analyses indicated that these genes are predominantly involved in hypoxia, immune responses, inflammation, apoptosis, and aging processes.Employing Bayesian networks, we elucidated four regulatory relationships among the hypoxia genes: PIM1-CDKN1A, IL6-CDKN1A, PLIN2-ANGPTL4, and SERPINE1-PLAUR. We amalgamated 12 machine learning algorithms, comprising 113 distinct combinations, to refine our findings and identified four pivotal hypoxia genes: SLC2A1, PLIN2, FOSL2, and PIM1. The SHAP analysis was instrumental in interpreting the predictive outcomes of the optimal model, the Random Forest algorithm, SLC2A1 was the most influential gene in the model.Immune infiltration analysis revealed the presence of seven types of dysregulated immune cells within the failing myocardium. Colocalization analysis suggested that hypoxia genes and heart failure are not likely to be independently influenced by the same genetic factors, although the potential for intermediary factors or shared biological pathways was not excluded.Overall, our research offers significant insights into the molecular mechanisms of hypoxia genes in the pathophysiology of heart failure, paving the way for the development of targeted and immunomodulatory therapies.