This project aims to investigate how clinical characteristics, blood proteomics, and metabolomics influence the incidence and prognosis of urological cancers (kidney, prostate, and bladder) in the UK Biobank cohort. We will address three key research questions: 1.Which clinical factors (e.g., age, BMI, lifestyle variables) are significantly associated with urological cancer incidence and survival? 2.How do proteomics and metabolomics markers improve risk stratification and prediction of disease outcomes? 3.Can machine learning models leveraging these multi-dimensional data offer clinically valuable diagnostic and prognostic tools?Objectives:Conduct Cox proportional hazards analyses to identify associations between baseline clinical features and cancer outcomes.Evaluate the predictive utility of selected protein and metabolic biomarkers using both traditional and machine learning approaches.Develop and validate diagnostic/prognostic models, comparing their accuracy to established clinical risk scores.Scientific Rationale:Although multiple studies have examined risk factors for urological cancers, few have integrated comprehensive proteomic and metabolomic data at a large scale. By leveraging the breadth and depth of UK Biobank data, our research will clarify the interplay between traditional risk factors and molecular biomarkers. The resulting findings may guide personalized risk assessment, improve early detection, and inform targeted prevention strategies for urological malignancies.
