| Title: | Exploration of risk and prognostic factors for urologic cancers and construction of precise prediction models: A large-scale cohort study and mendelian randomization analysis |
| Lead Institution: | Peking University First Hospital |
| Principal investigator: |
Application 672457
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About
Prostate cancer, renal cell carcinoma and bladder cancer are prevalent urologic cancers, with a combined incidence of 12.3% and mortality of 7.7%. Annually, around 2.4 million new cases are diagnosed globally. However, our understanding of these cancers is limited, particularly regarding precise and effective prediction models. This project aims to address this gap by conducting multi-dimensional analyses leveraging the extensive genetic, metabolomic, proteomic, and epidemiological data from the UK Biobank. Our objectives are as follows:1. To investigate the impact of lifestyle factors (e.g., anthropometric measurements, diet, sleep, physical activity, electronic device usage), environmental factors, genetic susceptibility, and biomarkers (such as blood and urine proteins, metabolites, and inflammatory markers) on urologic cancer risk and prognosis.2. Conducting mediation and subgroup analyses to elucidate interrelationships among identified factors and explore underlying mechanisms.3. Employing multiple machine learning algorithms for variable screening and model construction, enabling precise prediction of urologic cancer risk and prognosis.4. Calculate polygenic risk scores for risk stratification and investigate gene-environment interactions to pinpoint high-risk populations.5. Perform Mendelian randomization analyses to clarify causal associations between risk factors and urologic cancers. Scientific rationale: With the aging and growth of population, urologic cancers pose a significant global health threat, accounting for over 30% of male cancer cases and 10% of cancer deaths. While some studies have suggested links between unhealthy lifestyles, environmental factors, genetic alterations, and urologic cancer risk, their limited sample sizes and data make the findings unreliable. The lack of precise prediction models further underscores the need for large-scale studies to provide robust evidence. UK Biobank provides a valuable resource for such studies.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 18876 | Dietary patterns, polygenic risk, and kidney cancer incidence: a UK Biobank cohort study | Mingrui Zou (+9) | 2025 | Journal of Translational Medicine |
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