Investigating the toxicological impacts of thiabendazole exposure on uterine corpus endometrial carcinoma utilizing network toxicology, machine learning, and molecular docking.

Uterine corpus endometrial carcinomas (UCEC) are common cancers of the female reproductive system linked to environmental chemicals. Thiabendazole (TBZ), a fungicide used on produce, has been associated with cancer and liver damage, but its role in endometrial cancer is unclear. This study employs network toxicology, machine learning, and bioinformatics to explore TBZ's potential impact on endometrial cancer development. Through analysis of four comprehensive databases, we identified 69 potential molecular targets. Gene ontology analysis suggested that TBZ may induce UCEC by disrupting cellular metabolic homeostasis and signal transduction pathways. This hypothesis was further corroborated by KEGG pathway analysis. Machine learning techniques identified two pivotal targets: CA5B and CDK1. The study conducted survival analysis, independent prognostic analysis, single-gene genomic enrichment analysis, pan-cancer expression analysis, and immune infiltration analysis on the core targets. Molecular docking and kinetic simulations validated stable interaction between TBZ and the core targets. This research proposes a potential mechanistic model for TBZ-induced UCEC: TBZ directly targets key proteins such as CA5B and CDK1, disrupting cellular metabolism and pH homeostasis, aberrantly driving cell cycle progression, and interfering with the tumor immune microenvironment. These actions collectively and synergistically facilitate the initiation and progression of endometrial cancer.