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MALDI MS profiling of post-DRE urine samples highlights the potential of b-Microseminoprotein as a marker for prostatic diseases

Flatley, B., Wilmott, K. G., Malone, P. and Cramer, R. (2014) MALDI MS profiling of post-DRE urine samples highlights the potential of b-Microseminoprotein as a marker for prostatic diseases. The Prostate, 74 (1). pp. 103-111. ISSN 1097-0045

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To link to this item DOI: 10.1002/pros.22736

Abstract/Summary

BACKGROUND. To use spectra acquired by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) from pre- and post-digital rectal examination (DRE) urine samples to search for discriminating peaks that can adequately distinguish between benign and malignant prostate conditions, and identify the peaks’ underlying biomolecules. METHODS. Twenty-five participants with prostate cancer (PCa) and 27 participants with a variety of benign prostatic conditions as confirmed by a 10-core tissue biopsy were included. Pre- and post-DRE urine samples were prepared for MALDI MS profiling using an automated clean-up procedure. Following mass spectra collection and processing, peak mass and intensity were extracted and subjected to statistical analysis to identify peaks capable of distinguishing between benign and cancer. Logistic regression was used to combine markers to create a sensitive and specific test. RESULTS. A peak at m/z 10,760 was identified as b-microseminoprotein (b-MSMB) and found to be statistically lower in urine from PCa participants using the peak’s average areas. By combining serum prostate-specific antigen (PSA) levels with MALDI MS-measured b-MSMB levels, optimum threshold values obtained from Receiver Operator characteristics curves gave an increased sensitivity of 96% at a specificity of 26%. CONCLUSIONS. These results demonstrate that with a simple sample clean-up followed by MALDI MS profiling, significant differences of MSMB abundance were found in post-DRE urine samples. In combination with PSA serum levels, obtained from a classic clinical assay led to high classification accuracy for PCa in the studied sample set. Our results need to be validated in a larger multicenter prospective randomized clinical trial.

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary centres and themes > Chemical Analysis Facility (CAF)
Faculty of Life Sciences > School of Chemistry, Food and Pharmacy > Department of Chemistry
ID Code:35780
Publisher:Wiley

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