Abstract
To improve the prognosis of patients with pancreatic cancer, more accurate serum diagnostic methods are required. We used serum metabolomics as a diagnostic method for pancreatic cancer. Sera from patients with pancreatic cancer, healthy volunteers, and chronic pancreatitis were collected at multiple institutions. The pancreatic cancer and healthy volunteers were randomly allocated to the training or the validation set. All of the chronic pancreatitis cases were included in the validation set. In each study, the subjects' serum metabolites were analyzed by gas chromatography mass spectrometry (GC/MS) and a data processing system using an in-house library. The diagnostic model constructed via multiple logistic regression analysis in the training set study was evaluated on the basis of its sensitivity and specificity, and the results were confirmed by the validation set study. In the training set study, which included 43 patients with pancreatic cancer and 42 healthy volunteers, the model possessed high sensitivity (86.0%) and specificity (88.1%) for pancreatic cancer. The use of the model was confirmed in the validation set study, which included 42 pancreatic cancer, 41 healthy volunteers, and 23 chronic pancreatitis; that is, it displayed high sensitivity (71.4%) and specificity (78.1%); and furthermore, it displayed higher sensitivity (77.8%) in resectable pancreatic cancer and lower false-positive rate (17.4%) in chronic pancreatitis than conventional markers. Our model possessed higher accuracy than conventional tumor markers at detecting the resectable patients with pancreatic cancer in cohort including patients with chronic pancreatitis. It is a promising method for improving the prognosis of pancreatic cancer via its early detection and accurate discrimination from chronic pancreatitis.
Highlights
Pancreatic cancer is characterized by rapid tumor progression and early metastasis, and is one of the leading causes of cancer-related death
The only curative treatment for pancreatic cancer is surgical resection, more than 80% of patients with pancreatic cancer have a locally advanced or metastatic tumor that is unresectable at the time of Authors' Affiliations: 1Divisions of Gastroenterology and 2Medical Oncology/Hematology, and 3Metabolomics Research, Department of Internal Medicine and 4The Integrated Center for Mass Spectrometry, Kobe University Graduate School of Medicine; 5Department of Clinical Laboratory, Kobe University Hospital; and 6Division of Gastroenterology, Hyogo Cancer Center, Akashi, Hyogo, Japan
We previously found that human serum metabolomics using gas chromatography/mass spectrometry (GC/MS) is able to discriminate patients with pancreatic cancer from healthy volunteers [20]
Summary
Pancreatic cancer is characterized by rapid tumor progression and early metastasis, and is one of the leading causes of cancer-related death. There is no effective method for its early detection. CA19-9, which is usually used as a tumor marker, is unsuitable for the early detection of pancreatic cancer due to the low sensitivity for resectable stages of disease. Imaging examinations are not costeffective and cannot discriminate pancreatic cancer from benign pancreatic diseases such as tumor-forming pancreatitis. Endoscopic examinations are not appropriate for screening because of their low throughput and the high risk of complications. A novel screening and diagnostic method for pancreatic cancer is required. To improve the prognosis of patients with pancreatic cancer, more accurate serum diagnostic methods are required. We used serum metabolomics as a diagnostic method for pancreatic cancer
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
