Integrated Multi-Cohort Transcriptomic Analysis Reveals Molecular Networks and Signaling Pathway Landscape of Aconitine-Induced Cardiotoxicity and Myocardial Injury.

Abstract

Aconitine-induced cardiotoxicity is a serious adverse effect of Aconitum-containing medicines, with myocardial injury and malignant ventricular arrhythmias representing important clinical manifestations. However, the molecular responses underlying aconitine-induced myocardial injury remain incompletely understood. This study aimed to construct an integrated molecular network and signaling pathway landscape of aconitine-induced cardiotoxicity through multi-cohort transcriptomic analysis and to identify key hub genes associated with myocardial injury and ventricular tachyarrhythmias. Rat myocardial transcriptomic datasets related to aconitine-induced cardiotoxicity were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the thresholds of |log₂FC| > 1 and a false discovery rate (FDR) < 0.05. Weighted gene co-expression network analysis (WGCNA) was performed to identify cardiotoxicity-associated gene modules, and candidate genes were obtained by intersecting DEGs with module genes. Protein-protein interaction (PPI) network analysis was subsequently conducted to identify hub genes, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to characterize their biological functions and signaling pathways. The expression patterns of the hub genes showed broadly concordant trends in the independent doxorubicin-induced cardiotoxicity dataset, suggesting that these genes may represent conserved transcriptional responses to myocardial injury (GSE42177). A total of 265 DEGs were identified, and WGCNA revealed one gene module significantly associated with cardiotoxicity (r = - 0.33, P < 0.001). Integration of DEGs with the cardiotoxicity-associated module yielded 18 candidate genes, from which 12 hub genes (Abcb11, Abcc6, Fez1, Ldha, Nr3c1, Nr3c2, Pc, Pdk2, Prkcz, Slc9a1, Vcam1, and Vtn) were identified through PPI network analysis. Functional enrichment analysis indicated that these genes were primarily involved in inflammatory responses, metabolic regulation, and stress-related signaling pathways, particularly the MAPK and TGF-β signaling pathways.