A five‐microRNA model (pCaP) for predicting prostate cancer aggressiveness using cell‐free urine

Improved biomarkers for prostate cancer (PC) risk stratification are urgently needed. Here, we aimed to develop a novel multimarker model for prediction of biochemical recurrence (BCR) after curatively intended radical prostatectomy (RP), based on minimally invasive sampling of blood and urine. We i...

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Bibliographic Details
Published in:International journal of cancer 2019-11, Vol.145 (9), p.2558-2567
Main Authors: Fredsøe, Jacob, Rasmussen, Anne K.I., Mouritzen, Peter, Borre, Michael, Ørntoft, Torben, Sørensen, Karina D.
Format: Article
Language:English
Subjects:
Adult
Aged
biomarker
Biomarkers, Tumor - genetics
Biomarkers, Tumor - urine
Cancer
Cancer surgery
Cohort Studies
Health risks
Humans
Kaplan-Meier Estimate
Male
Medical research
microRNA
MicroRNAs - genetics
MicroRNAs - urine
Middle Aged
miRNA
Neoplasm Grading - methods
Neoplasm Recurrence, Local - genetics
Neoplasm Recurrence, Local - pathology
Neoplasm Recurrence, Local - urine
Nomograms
Patients
pCaP
Prognosis
Prostate - pathology
Prostate cancer
Prostate-Specific Antigen - genetics
Prostatectomy
Prostatectomy - methods
Prostatic Neoplasms - genetics
Prostatic Neoplasms - pathology
Prostatic Neoplasms - urine
Regression analysis
Urine
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Summary:Improved biomarkers for prostate cancer (PC) risk stratification are urgently needed. Here, we aimed to develop a novel multimarker model for prediction of biochemical recurrence (BCR) after curatively intended radical prostatectomy (RP), based on minimally invasive sampling of blood and urine. We initially measured the levels of 45 selected miRNAs by RT‐qPCR in exosome enriched cell‐free urine samples collected prior to RP from 215 PC patients (Cohort 1, training). We trained a novel logistic regression model (pCaP), comprising five urine miRNAs (miR‐151a‐5p, miR‐204‐5p, miR‐222‐3p, miR‐23b‐3p and miR‐331‐3p) and serum prostate‐specific antigen (PSA), which significantly predicted time to BCR in Cohort 1 (univariate Cox regression analysis: HR = 3.12, p 
ISSN:0020-7136
1097-0215
DOI:10.1002/ijc.32296