Our project aims to uncover novel, modifiable environmental risk factors in pan-cancer based on two-stage population studies. With the advance in understanding cancer risk factors, translating lifestyle, environmental, and genetic risk factor information into actionable clinical information is the next step in developing personalized prevention. Therefore, we will incorporate these environmental lifestyle factors, with the known common genetic variants to develop risk prediction models using machine learning and deep learning methods. We will further expand the risk prediction analysis to define the optimal starting age for screening. These models may be useful to prioritize those at high risk for targeted prevention or intervention and to reduce emphasis on those at low risk of developing cancers, thereby optimizing utilization of screening in clinical practice with individually tailored prevention strategies.
The project is scheduled to begin in October 2022 and be completed in October 2025. The research plan is as follows:
1)October 2022-April 2023: data application and processing.
2)May 2023-December 2024: (1) identifying novel risk factors in pan-cancer; (2) building a risk prediction model; (3) evaluating risk prediction models.
3)January 2025-October 2025: summarizing research results and writing research papers.