학술논문

Deciphering the ovarian cancer ascites fluid peptidome
Document Type
Academic Journal
Source
Clinical Proteomics. April 2, 2014, Vol. 11
Subject
Oncology, Experimental -- Analysis
Ascites -- Analysis
Mass spectrometry -- Analysis
Liver -- Analysis
Proteins -- Analysis
Ovarian cancer -- Analysis
Cancer -- Research
Liver cirrhosis -- Analysis
Language
English
ISSN
1542-6416
Abstract
Background Conventional proteomic approaches have thus far been unable to identify novel serum biomarkers for ovarian cancer that are more sensitive and specific than the current clinically used marker, CA-125. Because endogenous peptides are smaller and may enter the circulation more easily than proteins, a focus on the low-molecular-weight region may reveal novel biomarkers with enhanced sensitivity and specificity. In this study, we deciphered the peptidome of ascites fluid from 3 ovarian cancer patients and 3 benign individuals (ascites fluid from patients with liver cirrhosis). Results Following ultrafiltration of the ascites fluids to remove larger proteins, each filtrate was subjected to solid phase extraction and fractionated using strong cation exchange chromatography. The resultant fractions were analyzed using an Orbitrap mass spectrometer. We identified over 2000 unique endogenous peptides derived from 259 proteins. We then catalogued over 777 peptides that were found only in ovarian cancer ascites. Our list of peptides found in ovarian cancer specimens includes fragments derived from the proteins vitronectin, transketolase and haptoglobin. Conclusions Peptidomics may uncover previously undiscovered disease-specific endogenous peptides that warrant further investigation as biomarkers for ovarian cancer. Keywords: Biomarker, Early diagnosis, Mass spectrometry, Ovarian cancer, Ascites fluid, Peptidome
Author(s): Anand Bery[sup.1,2] , Felix Leung[sup.1,3] , Christopher R Smith[sup.3] , Eleftherios P Diamandis[sup.1,2,3] and Vathany Kulasingam[sup.1,2] Background The advent of high-throughput, mass spectrometry-based proteomics for biomarker discovery was met [...]