Research Questions: Cancer etiology and progression involve complex interactions between genetic, environmental, and lifestyle factors. While individual elements have been studied, their synergistic effects and mechanisms-especially in contexts like comorbidities (e.g., obesity)-remain unclear. Advances in genomics (e.g., TfR/GPX4 biomarkers) and imaging enable holistic risk assessment, but integrating these data into clinically actionable insights requires large-scale integrative approaches. This study employs a prospective cohort design, leveraging retrospective data for model development and validation.
Objectives: This research aims to investigate interrelationships between lifestyle factors, biomarkers, genetics, and imaging features in cancer development and outcomes. It seeks to develop integrated predictive models for early high-risk identification, elucidate biological mechanisms (e.g., tumor microenvironment interactions), and inform personalized interventions to improve prognosis and survival rates.
Scientific Rationale: Multi-faceted data will be collected: lifestyle/comorbidity information via questionnaires/medical records, blood samples for biomarker/genetic analysis, and serial imaging for quantitative features. Advanced statistical methods (Cox regression, mediation analysis) and machine learning will integrate data to build robust predictive models. A large-scale cohort of 50,000-100,000 participants (aligning with biobanks like UK Biobank) is targeted, including substantial cancer cases and a sub-cohort with multi-omics/imaging data. This study will uncover novel pathways, enable early risk stratification, and inform public health strategies, ultimately reducing cancer burden and providing a paradigm for predictive oncology.
