Exploring Hidden Dangers: Predicting Mycotoxin-like Toxicity and Mapping Toxicological Networks in Hepatocellular Carcinoma

Mycotoxins are potent triggers of hepatocellular carcinoma (HCC) due to their intricate interplay with cellular macromolecules and signaling pathways. This study integrates machine learning and biomolecular analyses to elucidate the mechanisms underlying mycotoxin-induced hepatocarcinogenesis. Using a data set of 1767 mycotoxins and 1706 non-mycotoxin fungal metabolites, we evaluated 51 machine learning models. The KPGT model achieved optimal performance with an ROC-AUC of 0.979 and balanced accuracy of 0.930. Clustering analysis identified six distinct mycotoxin clusters with unique structural features. Network toxicology analysis revealed distinct protein-protein interaction patterns across different mycotoxin clusters, identifying key regulatory proteins including EGFR, SRC, and ESR1. GO enrichment analysis uncovered cluster-specific effects on protein complexes and macromolecular assemblies, particularly in membrane organization and vesicular transport. KEGG pathway analysis demonstrated systematic perturbation of major signaling cascades, with each mycotoxin cluster distinctly modulating protein kinase networks and receptor tyrosine kinase pathways. Molecular docking analyses validated these interactions, with binding affinities ranging from -9.6 to -4.7 kcal/mol. Notably, cluster 5 showed strong binding to SRC (-9.6 kcal/mol), EGFR (-9.5 kcal/mol), and ESR1 (-7.8 kcal/mol), providing structural insights into toxin-macromolecule recognition. These findings enhance our understanding of mycotoxin-protein interactions in HCC development and suggest potential therapeutic strategies targeting these macromolecular interfaces.