Identification of IL1R1 as a potential key PANoptosis-related gene in myocardial ischemia-reperfusion injury using machine learning

Myocardial ischemia-reperfusion injury (MIRI) involves multifaceted pathogenic mechanisms, including inflammatory responses, immune dysregulation, and emerging forms of programmed cell death such as PANoptosis. However, the precise regulatory genes linking PANoptosis to MIRI remain largely unexplored. Transcriptomic data from public MIRI datasets were analyzed to identify PANoptosis-related genes. Key genes were screened via univariate logistic regression and three machine learning algorithms (LASSO, SVM-RFE, and Random Forest). The top candidate was evaluated through ROC analysis, GSEA, immune infiltration profiling, and drug prediction. Experimental validation was performed using both OGD/R cell models and a murine MIRI mode. Five DPRGs were identified, of which IL1R1 was consistently selected as a key PRG (KPRG) across all three machine learning methods. ROC analysis demonstrated a high diagnostic performance for IL1R1 (AUC = 0.956). IL1R1 was enriched in MAPK and ECM-receptor pathways and negatively correlated with CD8 + T cell infiltration. Molecular docking suggested IL1R1 could be targeted by cardioprotective drugs such as Atorvastatin and Pioglitazone. In both the OGD/R model and murine MIRI model, IL1R1 and p-P65 expression were significantly upregulated, supporting the activation of the IL1R1/p-P65 axis. Notably, siRNA-mediated IL1R1 knockdown suppressed P65 expression, indicating that inhibition of the IL1R1/p-P65 axis may mitigate PANoptosis. This study suggests that IL1R1 may play a role in PANoptosis during myocardial ischemia-reperfusion injury. Based on bioinformatics, machine learning, and preliminary experimental validation, IL1R1 could be a potential biomarker or therapeutic target, although further research is needed to confirm its clinical significance.