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Detection of candidate biomarkers of prostate cancer progression in serum: a depletion-free 3D LC/MS quantitative proteomics pilot study

Larkin, SET; Johnston, HE; Jackson, TR; Jamieson, DG; Roumeliotis, TI; Mockridge, CI; Michael, A; ... Townsend, PA; + view all (2016) Detection of candidate biomarkers of prostate cancer progression in serum: a depletion-free 3D LC/MS quantitative proteomics pilot study. British Journal of Cancer , 115 (9) pp. 1078-1086. 10.1038/bjc.2016.291. Green open access

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Abstract

Background: Prostate cancer (PCa) is the most common male cancer in the United Kingdom and we aimed to identify clinically relevant biomarkers corresponding to stage progression of the disease. Methods: We used enhanced proteomic profiling of PCa progression using iTRAQ 3D LC mass spectrometry on high-quality serum samples to identify biomarkers of PCa. Results: We identified 41000 proteins. Following specific inclusion/exclusion criteria we targeted seven proteins of which two were validated by ELISA and six potentially interacted forming an ‘interactome’ with only a single protein linking each marker. This network also includes accepted cancer markers, such as TNF, STAT3, NF-kB and IL6. Conclusions: Our linked and interrelated biomarker network highlights the potential utility of six of our seven markers as a panel for diagnosing PCa and, critically, in determining the stage of the disease. Our validation analysis of the MS-identified proteins found that SAA alongside KLK3 may improve categorisation of PCa than by KLK3 alone, and that TSR1, although not significant in this model, might also be a clinically relevant biomarker.

Type: Article
Title: Detection of candidate biomarkers of prostate cancer progression in serum: a depletion-free 3D LC/MS quantitative proteomics pilot study
Open access status: An open access version is available from UCL Discovery
DOI: 10.1038/bjc.2016.291
Publisher version: http://doi.org/10.1038/bjc.2016.291
Language: English
Additional information: Copyright © 2018 Springer Nature Limited. This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
URI: http://discovery.ucl.ac.uk/id/eprint/10056093
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