The effective diagnosis of pancreatic cancer is often quite challenging, due to a lack of disease-specific symptoms, resulting in the majority of patients presenting with advanced disease, with an associated dismal prognosis. Earlier detection of pancreatic cancer, at a stage where surgery is feasible, would greatly increase the 5-year survival rate. Detecting pancreatic cancer early is therefore vital to improve the prognosis for these patients. Pre-cancerous pancreatic cysts are an early indicator of malignant transformation. The ideal screening test would be capable of detecting pancreatic cancer at these initial stages. Current procedures for pancreatic cancer diagnosis are invasive, uncomfortable and costly, and can be considered unnecessary in those cysts found to be benign. We propose to study a number of tumour regulatory molecules that have been the subject of research in laboratories at the University of Hull (e.g., tissue factor (TF), adrenomedullin (AM) using enzyme-linked immunosorbent assays (ELISA) tests) that have been studied in the context of carcinogenic transformation in more common malignancies but have yet to be fully tested in pancreatic malignant transformation. The recent introduction of platform technologies at the University of Hull has broadened this area of investigation by giving us access to next generation genomic sequencing and proteomic analyses of small amounts of tissue samples. We intend to analyse pancreatic cystic fluid samples using these technologies to discover new regulatory molecules. Altogether, his study will measure the levels of novel regulatory molecules and genetic changes involved with pancreatic cancer carcinogenesis using a combination of conventional techniques (e.g. ELISA) and state-of-the-art platform technologies in pancreatic cysts from those patients in whom cancer may be suspected, to determine the potential of these molecules to serve as markers to detect early changes towards pancreatic cancer.
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Measurement of the accuracy of protein markers (e.g., TF, AM) in detecting early stage pancreatic cancer by comparison of laboratory results from ELISA assays and platform technologies to patient clinical data.
Timeframe: During the recruitment phase.