The incidence of pancreatic cancer is insidious. Most patients were in advanced stage when diagnosed and could not be cured by surgery. Early diagnosis of pancreatic cancer through screening is so important. Early screening detection projects derived from liquid biopsy technology are not only limited to circulating DNA methylation markers, but have developed into multi-dimensional indicators for joint evaluation. This large-scale early detection study will randomly enroll 260 stage I/II/III pancreatic patients, 80 patients with pancreatic benign diseases and 156 age- and sex-matched healthy individuals upon providing written informed consent. Plasma samples will be collected and extracted cell-free DNA will be subjected to whole genome sequencing. We aimed to incorporate genome-wide copy number variations, cell-free DNA fragmentomics, and status of KRAS gene mutation into the development of a multimodal biomarker-based prediction model.
The purpose of this study is to enable non-invasive early detection of pancreatic cancer in high-risk populations through the establishment of a machine learning model using plasma cell-free DNA fragmentomics. Plasma cell-free DNA from early stage pancreatic cancer patients and healthy individuals will be subjected to whole-genome sequencing. Features, such as cell-free DNA fragmentation, copy number variations and the status of KRAS gene mutation, will be assessed to generate this model.