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crossrefToxins2025-08-04Cited by 2

The Potential Mechanisms of Ochratoxin A in Prostate Cancer Development: An Integrated Study Combining Network Toxicology, Machine Learning, and Molecular Docking

Hong Cai, Dandan Shen, Xiangjun Hu, Hongwei Yin, Zhangren Yan

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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crossrefToxins2025-08-05Cited by 7

Mechanistic Exploration of Aristolochic Acid I-Induced Hepatocellular Carcinoma: Insights from Network Toxicology, Machine Learning, Molecular Docking, and Molecular Dynamics Simulation

Tiantaixi Tu, Tongtong Zheng, Hangqi Lin, Peifeng Cheng, Ye Yang, Bolin Liu, et al.

This study explores how aristolochic acid I (AAI) drives hepatocellular carcinoma (HCC). We first employ network toxicology and machine learning to map the key molecular target genes. Next, our research utilizes molecular docking to evaluate how AAI binds to these targets, and fi…

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