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Multi-platform biomarker discovery for bladder cancer recurrence diagnosis

De Paoli, M, Gogalic, S, Sauer, U, Preininger, C, Pandha, HS, Simpson, G, Horvath, A and Marquette, C (2016) Multi-platform biomarker discovery for bladder cancer recurrence diagnosis Disease Markers, 4591910.

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Purpose. Nonmuscle invasive bladder cancer (BCa) has a high recurrence rate requiring lifelong surveillance. Urinary biomarkers are promising as simple alternatives to cystoscopy for the diagnosis of recurrent bladder cancer. However, no single marker can achieve the required accuracy. The purpose of this study was to select a multiparameter panel, comprising urinary biomarkers and clinical parameters, for BCa recurrence diagnosis. Experimental Design. Candidate biomarkers were measured in urine samples of BCa patients with recurrence and BCa patients without recurrence. A multiplatform strategy was used for marker quantification comprising a multiplexed microarray and an automated platform for ELISA analysis. A multivariate statistical analysis combined the results from both platforms with the collected clinical data. Results. The best performing combination of biomarkers and clinical parameters achieved an AUC value of 0.91, showing better performance than individual parameters. This panel comprises six biomarkers (cadherin-1, IL-8, ErbB2, IL-6, EN2, and VEGF-A) and three clinical parameters (number of past recurrences, number of BCG therapies, and stage at time of diagnosis). Conclusions. The multiparameter panel could be a useful noninvasive tool for BCa surveillance and potentially impact the clinical management of this disease. Validation of results in an independent cohort is warranted.

Item Type: Article
Subjects : Medical Science
Divisions : Faculty of Health and Medical Sciences > School of Biosciences and Medicine > Department of Clinical and Experimental Medicine
Authors :
De Paoli, M
Gogalic, S
Sauer, U
Preininger, C
Pandha, HS
Simpson, G
Horvath, A
Marquette, C
Date : September 2016
DOI : 10.1155/2016/4591910
Copyright Disclaimer : Copyright © 2016 Marine De Paoli et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Depositing User : Symplectic Elements
Date Deposited : 26 Sep 2016 11:25
Last Modified : 31 Oct 2017 18:45

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