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Proteomics. 2014 Apr;14(7-8):945-55. doi: 10.1002/pmic.201300402. Epub 2014 Mar 3.

Proteomic identification of potential prognostic biomarkers in resectable pancreatic ductal adenocarcinoma.

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  • 1Department of Pharmaceutical Analysis, Faculty of Pharmacy, University of Medicine and Pharmacy "Iuliu Haţieganu", Cluj-Napoca, Romania; Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.

Abstract

Pancreatic cancer is a devastating disease with a mortality rate almost identical with its incidence. In this context, the investigation of the pancreatic cancer proteome has gained considerable attention because profiles of proteins may be able to identify disease states and progression more accurately. Therefore, our objective was to investigate the changes in the proteome of patients suffering from pancreatic ductal adenocarcinoma (PDAC) by a comprehensive quantitative approach. Comparative proteomic profiling by label-free LC-MS/MS analysis of nine matched pairs of tumor and nontumor pancreas samples was used to identify differences in protein levels characteristic for PDAC. In this analysis, 488 proteins were quantified by at least two peptides of which 99 proteins displayed altered levels in PDAC (p < 0.01, fold change >1.3). Screening of data revealed a number of molecules that had already been related to PDAC such as galectin-1 (LEG1), major vault protein, adenylyl cyclase-associated protein 1 (CAP1), but also a potential new prognostic biomarker prolargin (PRELP). The Kaplan-Meier survival analysis revealed a significant correlation of protein abundance of PRELP with postoperative survival of patients with PDAC. For selected proteins the findings were verified by targeted proteomics (SRM), validated by immunohistochemistry and Western blotting and their value as candidate biomarkers is discussed.

© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

KEYWORDS:

Immunohistochemistry; Pancreatic ductal adenocarcinoma; Prognostic biomarkers; Shotgun proteomics; Targeted proteomics; Technology

PMID:
24459066
[PubMed - indexed for MEDLINE]
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