This study aims to identify clinical and genetic risk factors involved in gynecologic tumors, including ovarian, cervical, endometrial, vaginal cancers, uterine sarcomas, and related malignancies. Key variables include demographics (age, BMI, menopausal age), metabolic and endocrine disorders, immunological factors, circulating biomarkers, and imaging phenotypes. These factors may independently or jointly influence tumorigenesis. Integrative analysis of clinical, biochemical, imaging, and genomic data aims to uncover pathogenic mechanisms and biomarkers for early diagnosis and targeted therapy, as well as modifiable risk factors for prevention.
Gynecologic cancers are heterogeneous diseases influenced by inherited genetic variants and clinical, metabolic, and environmental factors. High-penetrance mutations such as BRCA1/2 contribute significantly to ovarian cancer susceptibility. Polygenic risk also plays a role yet remains incompletely characterized.
Clinical risk factors, including demographics, metabolic comorbidities, biomarkers, and imaging traits, impact tumor development through inflammation, metabolic dysregulation, hormonal imbalance, and immune alterations.
Risk factor associations will be quantified using Cox regression and related survival models, adjusting for confounders and interactions, with validation to ensure robustness.
Genomic analyses will include GWAS, polygenic risk scores, Mendelian randomization, and integrative multi-omics approaches to dissect genetic contributions.
Computational modeling and bioinformatics methods will characterize genetic variant functions and pathways, emphasizing immunometabolic and epigenetic regulation. Experimental validation will employ molecular and cellular assays and relevant animal models.
This integrated approach aims to elucidate molecular mechanisms, identify therapeutic targets, and assess clinical utility. The study leverages UK Biobank data and gratefully acknowledges its support.
