Hypoxia-Driven Mechanisms in Ischemic Heart Failure: A Bioinformatics, Machine Learning and Bayesian Network Study.

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 showed that the posterior probabilities were predominantly concentrated on the gene-expression-only hypothesis and did not provide sufficient evidence for shared causal variants between the candidate-gene eQTL and HF GWAS signals. 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.