Published April 11, 2022
| Version v3
Dataset
Open
Development of a multivariable risk model integrating urinary peptide metabolites and Extracellular Vesicle RNA data to detect significant prostate cancer
Authors/Creators
- 1. Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, United Kingdom
- 2. Department of Biomarker research, Mosaiques diagnostics GmbH, Hannover, Germany
- 3. Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
- 4. Institute of Cancer Sciences, University of Glasgow, Glasgow, United Kingdom
- 5. Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, United Kingdom and The Earlham Institute, Norwich Research Park, Norwich, Norfolk, UK
Description
The aim of this study was to investigate whether the robust integration of expression data from urinary extracellular vesicle RNA (EV-RNA) with urine proteomic metabolites can accurately predict PCa biopsy outcome. Urine samples were analyzed by mass spectrometry and NanoString gene-expression analysis. As a result, four classifiers were generated: ‘MassSpec’ (CE-MS proteomics), ‘EV-RNA’, ‘SoC’ (standard of care) and ‘ExoSpec’. The best prediction for Gs³3+4 at initial biopsy (AUC=0.83, 95% CI:0.77-0.88) was achieved by applying ‘ExoSpec’ classifier and he outperformed other predictive classifiers. In addition, the results showed that the performance of ‘ExoSpec’ could reduce unnecessary biopsies by 30%.
Files
Pca_v2.zip
Files
(126.8 GB)
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