Transcript Markers from Urinary Extracellular Vesicles for Predicting Risk Reclassification of Prostate Cancer Patients on Active Surveillance.
active surveillance
biomarker
liquid biopsy
monitoring
prediction
prostate cancer
quantitative PCR
risk reclassification
transcripts
urinary extracellular vesicles
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
04 Jul 2024
04 Jul 2024
Historique:
received:
31
05
2024
revised:
25
06
2024
accepted:
02
07
2024
medline:
13
7
2024
pubmed:
13
7
2024
entrez:
13
7
2024
Statut:
epublish
Résumé
Serum prostate-specific antigen (PSA), its derivatives, and magnetic resonance tomography (MRI) lack sufficient specificity and sensitivity for the prediction of risk reclassification of prostate cancer (PCa) patients on active surveillance (AS). We investigated selected transcripts in urinary extracellular vesicles (uEV) from PCa patients on AS to predict PCa risk reclassification (defined by ISUP 1 with PSA > 10 ng/mL or ISUP 2-5 with any PSA level) in control biopsy. Before the control biopsy, urine samples were prospectively collected from 72 patients, of whom 43% were reclassified during AS. Following RNA isolation from uEV, multiplexed reverse transcription, and pre-amplification, 29 PCa-associated transcripts were quantified by quantitative PCR. The predictive ability of the transcripts to indicate PCa risk reclassification was assessed by receiver operating characteristic (ROC) curve analyses via calculation of the area under the curve (AUC) and was then compared to clinical parameters followed by multivariate regression analysis. ROC curve analyses revealed a predictive potential for AMACR, HPN, MALAT1, PCA3, and PCAT29 (AUC = 0.614-0.655,
Identifiants
pubmed: 39001515
pii: cancers16132453
doi: 10.3390/cancers16132453
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Else Kröner-Fresenius-Stiftung
ID : 2020_EKEA.10 and 2020_EKEA.11
Organisme : MeDDriveStart
ID : 60.392