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

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.

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.

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