This integrated analysis combines network toxicology and machine learning to identify key targets and mechanisms in prostate cancer development.
Ochratoxin A (OTA), a prevalent food contaminant, has been proposed as a potential contributor to the development of prostate cancer, although its precise mechanisms remain unclear. This study employed a comprehensive approach that integrated network toxicology, machine learning, and molecular docking to clarify the role of OTA in prostate cancer. The findings indicated that OTA interacts with 364 targets related to prostate cancer, and machine learning was employed to identify five key molecular targets as priorities (ESR1, TP53, TNF, INS, and EGFR). In conjunction with the results of a functional enrichment analysis, OTA was found to possibly facilitate cancer progression by disrupting endocrine function, activating oncogenic signaling pathways, reprogramming metabolism, and modulating the tumor microenvironment.
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Cai et al. (2025) studied this question.
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