The University of Southampton
University of Southampton Institutional Repository

Discovery of serum protein biomarkers for prostate cancer progression by proteomic analysis

Discovery of serum protein biomarkers for prostate cancer progression by proteomic analysis
Discovery of serum protein biomarkers for prostate cancer progression by proteomic analysis
Background: The incidence of prostate cancer (PCa) has increased in recent years due to the aging of the population and increased testing; however, mortality rates have remained largely unchanged. Studies have shown deficiencies in predicting patient outcome for both of the major PCa diagnostic tools, namely prostate specific antigen (PSA) and transrectal ultrasound-guided biopsy. Therefore, serum biomarkers are needed that accurately predict prognosis of PCa (indolent vs. aggressive) and can thus inform clinical management. Aim: This study uses surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) mass spectrometry analysis to identify differential serum protein expression between PCa patients with indolent vs. aggressive disease categorised by Gleason grade and biochemical recurrence. Materials and Methods: A total of 99 serum samples were selected for analysis. According to Gleason score, indolent (45 samples) and aggressive (54) forms of PCa were compared using univariate analysis. The same samples were then separated into groups of different recurrence status (10 metastatic, 15 biochemical recurrences and 70 non-recurrences) and subjected to univariate analysis in the same way. The data from Gleason score and recurrence groups were then analysed using multivariate statistical analysis to improve PCa biomarker classification. Results: The comparison between serum protein spectra from indolent and aggressive samples resulted in the identification of twenty-six differentially expressed protein peaks (p<0.05), of which twenty proteins were found with 99% confidence. A total of 18 differentially expressed proteins (p<0.05) were found to distinguish between recurrence groups; three of these were robust with p<0.01. Sensitivity and specificity within the Gleason score group was 73.3% and 60% respectively and for the recurrence group 70% and 62.5%. Conclusion: SELDI-TOF-MS technology has facilitated the discovery of prognostic biomarkers in serum that can successfully discriminate aggressive from indolent PCa and also differentiate between recurrence groups.
1109-6535
93-103
Al-Ruwaili, J.A.
ce2b12bb-4350-43eb-a020-08aa7a02484a
Larkin, S.E.
73ffb031-2115-47e3-a62f-907d687d108f
Zeidan, B.A.
acd18415-22ee-43b8-a102-a36ea22dd0af
Taylor, M.G.
039d82f0-1fd9-4df8-a08c-35db0ee71f81
Adra, C.N.
75e7d0df-b402-49c0-9160-304637113e11
Aukim-Hastie, C.L.
296c775f-26a7-46c8-bdf0-eec843a10c63
Townsend, P.A.
89300833-c898-4ae1-a3b2-03214c71da52
Al-Ruwaili, J.A.
ce2b12bb-4350-43eb-a020-08aa7a02484a
Larkin, S.E.
73ffb031-2115-47e3-a62f-907d687d108f
Zeidan, B.A.
acd18415-22ee-43b8-a102-a36ea22dd0af
Taylor, M.G.
039d82f0-1fd9-4df8-a08c-35db0ee71f81
Adra, C.N.
75e7d0df-b402-49c0-9160-304637113e11
Aukim-Hastie, C.L.
296c775f-26a7-46c8-bdf0-eec843a10c63
Townsend, P.A.
89300833-c898-4ae1-a3b2-03214c71da52

Al-Ruwaili, J.A., Larkin, S.E., Zeidan, B.A., Taylor, M.G., Adra, C.N., Aukim-Hastie, C.L. and Townsend, P.A. (2010) Discovery of serum protein biomarkers for prostate cancer progression by proteomic analysis. Cancer Genomics Proteomics, 7 (2), 93-103.

Record type: Article

Abstract

Background: The incidence of prostate cancer (PCa) has increased in recent years due to the aging of the population and increased testing; however, mortality rates have remained largely unchanged. Studies have shown deficiencies in predicting patient outcome for both of the major PCa diagnostic tools, namely prostate specific antigen (PSA) and transrectal ultrasound-guided biopsy. Therefore, serum biomarkers are needed that accurately predict prognosis of PCa (indolent vs. aggressive) and can thus inform clinical management. Aim: This study uses surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) mass spectrometry analysis to identify differential serum protein expression between PCa patients with indolent vs. aggressive disease categorised by Gleason grade and biochemical recurrence. Materials and Methods: A total of 99 serum samples were selected for analysis. According to Gleason score, indolent (45 samples) and aggressive (54) forms of PCa were compared using univariate analysis. The same samples were then separated into groups of different recurrence status (10 metastatic, 15 biochemical recurrences and 70 non-recurrences) and subjected to univariate analysis in the same way. The data from Gleason score and recurrence groups were then analysed using multivariate statistical analysis to improve PCa biomarker classification. Results: The comparison between serum protein spectra from indolent and aggressive samples resulted in the identification of twenty-six differentially expressed protein peaks (p<0.05), of which twenty proteins were found with 99% confidence. A total of 18 differentially expressed proteins (p<0.05) were found to distinguish between recurrence groups; three of these were robust with p<0.01. Sensitivity and specificity within the Gleason score group was 73.3% and 60% respectively and for the recurrence group 70% and 62.5%. Conclusion: SELDI-TOF-MS technology has facilitated the discovery of prognostic biomarkers in serum that can successfully discriminate aggressive from indolent PCa and also differentiate between recurrence groups.

Full text not available from this repository.

More information

Published date: 1 March 2010

Identifiers

Local EPrints ID: 147099
URI: https://eprints.soton.ac.uk/id/eprint/147099
ISSN: 1109-6535
PURE UUID: 1b797eef-e76f-4dab-b19f-ac7cedd3e780

Catalogue record

Date deposited: 23 Apr 2010 09:04
Last modified: 17 Jul 2019 00:04

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×