Research Papers:

Urinary mRNA biomarker panel for the detection of urothelial carcinoma

Virginia Urquidi _, Mandy Netherton, Evan Gomes-Giacoia, Daniel Serie, Jeanette Eckel-Passow, Charles J. Rosser and Steve Goodison

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Oncotarget. 2016; 7:38731-38740. https://doi.org/10.18632/oncotarget.9587

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Virginia Urquidi1, Mandy Netherton1, Evan Gomes-Giacoia1, Daniel Serie2, Jeanette Eckel-Passow3, Charles J. Rosser4, Steve Goodison2,5

1Cancer Research Institute, MD Anderson Cancer Center, Orlando, FL, USA

2Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA

3Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA

4University of Hawaii Cancer Center, Honolulu, HI, USA

5Department of Urology, Mayo Clinic, Jacksonville, FL, USA

Correspondence to:

Steve Goodison, email: [email protected]

Keywords: diagnostic biomarkers, bladder cancer, multiplex, urinalysis, non-invasive

Received: March 21, 2016    Accepted: April 28, 2016    Published: May 25, 2016


The early detection of bladder cancer is important as the disease has a high rate of recurrence and progression. The development of accurate, non-invasive urinary assays would greatly facilitate detection. In previous studies, we have reported the discovery and initial validation of mRNA biomarkers that may be applicable in this context. In this study, we evaluated the diagnostic performance of proposed molecular signatures in an independent cohort.

Forty-four mRNA transcripts were monitored blindly in urine samples obtained from a cohort of 196 subjects with known bladder disease status (89 with active BCa) using quantitative real-time PCR (RT-PCR). Statistical analyses defined associations of individual biomarkers with clinical data and the performance of predictive multivariate models was assessed using ROC curves. The majority of the candidate mRNA targets were confirmed as being associated with the presence of BCa over other clinical variables. Multivariate models identified an optimal 18-gene diagnostic signature that predicted the presence of BCa with a sensitivity of 85% and a specificity of 88% (AUC 0.935). Analysis of mRNA signatures in naturally micturated urine samples can provide valuable information for the evaluation of patients under investigation for BCa. Additional refinement and validation of promising multi-target signatures will support the development of accurate assays for the non-invasive detection and monitoring of BCa.

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