Development of a multi-indicator risk prediction model for cervical cancer associated with benzo[a]pyrene and nicotine exposure: A multi-omics study integrating toxicological analyses and molecular docking

Risk prediction models based on multi-omics data and machine learning algorithms provide potential reference targets for prognosis prediction and personalised treatment of cervical cancer patients. The results of this study provide important insights into the understanding of the health risks of cervical cancer associated with Benzo[a]pyrene and Nicotine exposures and the development of preventive and therapeutic strategies for cervical cancer, which may contribute to the development of precision medicine for cervical cancer.