Integrative network pharmacology and machine learning identify NLRP3 as a critical therapeutic target for liensinine in acute myocardial infarction

Abstract

Acute myocardial infarction (AMI) is a severe cardiovascular disease with complex pathogenesis, posing significant threats to human health. Liensinine, an alkaloid derived from lotus plumule, has shown potential cardioprotective effects in preliminary studies, but its specific molecular targets and mechanisms in MI treatment remain unclear. Differential expression genes (DEGs) associated with AMI were identified from the GSE166780 dataset. Liensinine targets were predicted via SwissTargetPrediction and CTD database. After removing duplicates, the resulting targets were intersected with the AMI DEGs. Protein-protein interactions (PPI) networks were constructed using STRING. KEGG and GO enrichment analyses were performed on Sangebox, and results were visualized in Cytoscape. Key genes were screened via CytoHubba (DMNC, MCC) and MCODE plugins, and intersected with genes selected by LASSO, SVM, and RF. Algorithms. An ischemia-reperfusion (I/R) model was established in AC16 cells. After treatment with different concentrations of liensinine, the optimal dose was determined by CCK8. Gene expression (qRT-PCR), apoptosis (flow cytometry), and levels of IL-6, IL-1β, and TNF-α (ELISA) were detected. DEGs in MI were identified from the GSE166780 dataset, with 27 genes overlapping with liensinine targets and enriched in key pathways (AGE-RAGE, TNF signaling). Network analysis and machine learning refined these to 4 core genes. In vitro validation using an AC16 I/R model showed NLRP3 was significantly upregulated following AMI; treatment with liensinine (0.5 μM) suppressed NLRP3, alleviated cell injury, and reduced pro-inflammatory cytokines (IL-1β, IL-6, and TNF-α), confirming its protective role via NLRP3 regulation. Liensinine exerted protective effects against MI by suppressing NLRP3, thereby mitigating cell injury and inflammation.