Clinical Chemistry and Laboratory Medicine (CCLM)
Published in Association with the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)
Editor-in-Chief: Plebani, Mario
Ed. by Gillery, Philippe / Lackner, Karl J. / Lippi, Giuseppe / Melichar, Bohuslav / Payne, Deborah A. / Schlattmann, Peter / Tate, Jillian R.
IMPACT FACTOR increased in 2015: 3.017
Rank 5 out of 30 in category Medical Laboratory Technology in the 2014 Thomson Reuters Journal Citation Report/Science Edition
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Pancreatic cancer biomarkers discovery by surface-enhanced laser desorption and ionization time-of-flight mass spectrometry
1Department of Laboratory Medicine, University of Padova, Padova, Italy
2Department of Medical and Surgical Sciences, University of Padova, Padova, Italy
3CNR-ISTM, Padova, Italy
Citation Information: Clinical Chemistry and Laboratory Medicine. Volume 47, Issue 6, Pages 713–723, ISSN (Online) 1437-4331, ISSN (Print) 1434-6621, DOI: 10.1515/CCLM.2009.158, May 2009
- Published Online:
Background: Surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF/MS), a laboratory-friendly technique, is used to identify biomarkers for cancer. The aim of the present study was to explore the application of SELDI proteomic patterns in serum for distinguishing between cases of pancreatic cancer, chronic pancreatitis, type 2 diabetes mellitus and healthy controls.
Methods: Sera from 12 healthy controls, 24 patients with type 2 diabetes mellitus, 126 with pancreatic cancer, including 84 with diabetes, and 61 with chronic pancreatitis, 32 of which were diabetics, were analyzed using SELDI-TOF/MS. Spectra (IMAC-30) were clustered and classified using Biomarker Wizard and Biomarker Pattern software.
Results: Two decision tree classification algorithms, one with and one without CA 19-9, were constructed. In the absence of CA 19-9, the splitting protein peaks were: m/z 1526, 1211, and 3519; when CA 19-9 was used in the analysis, it replaced the m/z 3519 splitter. The two algorithms performed equally for classifying patients. A classification tree that considered diabetic patients only was constructed; the main splitters were: 1211, CA 19-9, 7903, 3359, 1802. With this algorithm, 100% of patients with type 2 diabetes mellitus, 97% with chronic pancreatitis and 77% of patients with pancreatic cancer were correctly classified. SELDI-TOF/MS features improved the diagnostic accuracy of CA 19-9 (AUC=0.883 for CA 19-9; AUC=0.935 for CA 19-9 and SELDI-TOF/MS features combined).
Conclusions: SELDI-TOF/MS allows identification of new peptides which, in addition to CA 19-9, enable the correct classification of the vast majority of patients with pancreatic cancer, which can be distinguished from patients with chronic pancreatitis or type 2 diabetes mellitus.
Clin Chem Lab Med 2009;47:713–23.
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