Proteome characterization of human pancreatic cyst fluid from intraductal papillary mucinous neoplasm by liquid chromatography/tandem mass spectrometry. (30th October 2017)

• Record Type: Journal Article
• Authors: Park, Joonho; Han, Dohyun; Do, Misol; Woo, Jongmin; Wang, Joseph I.; Han, Youngmin; Kwon, Wooil; Kim, Sun‐Whe; Jang, Jin‐Young; Kim, Youngsoo
• Is Part Of: Rapid communications in mass spectrometry. Volume 31:Number 20(2017)
• Page Start: 1761
• Page End: 1772
• Publication Date: 2017-10-30
• Subjects: Mass spectrometry -- Periodicals; 543.65
• DOI: 10.1002/rcm.7959
• Languages: English
• ISSNs: 0951-4198

Abstract

Abstract : Rationale: In recent years, the molecular components of pancreatic cyst fluid have been used for diagnosis and prognosis. Because the protein markers that are currently used in clinical tests are unreliable, proteomic studies to find new protein markers are being conducted. However, such researches have been limited due to the complexity of pancreatic cyst fluid and the immaturity of proteomic techniques. Methods: To overcome these limitations and provide a pancreatic cyst proteome dataset, we examined cyst fluid proteome with tandem mass spectrometry. The proteomic analysis was performed using a Orbitrap‐based mass spectrometer (Q‐Exactive) coupled with a 50‐cm‐long nano‐liquid chromatography column. Protein mutations were identified using mutation sequence database search. Results: A total of 5850 protein groups were identified from microliters of cyst fluid. Among those, 3934 protein groups were reported for the first time in pancreatic cyst fluid. Although high‐abundance proteins were not depleted in the experiment, our dataset detected almost all pancreatic tumor markers such as mucin family members, S100 proteins, and CEA‐related proteins. In addition, 590 protein mutation marker candidates were discovered. Conclusions: We provide a comprehensive cyst proteome dataset that includes cystic cellular proteins and mutated proteins. Our findings would serve as a rich resource for further IPMN studies and clinical applications. The MS data have been deposited in the ProteomeXchange with identifier PXD005671 (http://proteomecentral.proteomexchange.org/dataset/PXD005671 ).