Pancreatic cancer(PC) remains one of the most lethal malignancies in the digestive system, which is characterized by difficulties in early diagnosis, high surgical complexity, and a high likelihood of postoperative recurrence and metastasis. The disease progression encompasses precancerous lesions, early-stage cancer, and advanced cancer, where precancerous lesions and stage I PC demonstrate significantly better prognosis compared to advanced stages. This prognostic difference underscores the critical importance of accurately identifying high-risk populations and implementing effective surveillance strategies to achieve early diagnosis, which represents a cornerstone in optimizing contemporary PC management systems. To address this pressing need, our research initiative proposes to leverage the comprehensive UK Biobank database for investigating early detection and diagnostic approaches for PC, necessitating access to the complete spectrum of Tier 1 through Tier 3 datasets.
Existing epidemiological evidence has established that high-risk populations for PC development predominantly include carriers of cancer-associated germline mutations, individuals with pancreatic cystic neoplasms, etc. However, the relatively low age-standardized incidence rate of approximately 5.7 cases per 100,00 0 population demands analysis of extensive population-level data to enable precise risk stratification. The UK Biobank’s multidimensional data repository, encompassing demographic parameters, clinical laboratory measurements, Olink proteomic profiles, metabolomic signatures, genomic data, and radiomics including MRI, provides an unparalleled resource for this investigation.
Our proposed study will conduct integrative analysis of these heterogeneous datasets to develop a multifactorial risk prediction model for PC.
