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,p< 0.1). PSA, PSA density, PSA velocity, and MRI maxPI-RADS showed AUC values of 0.681–0.747 (p< 0.05), with accuracies for indicating a PCa risk reclassification of 64–68%. A model including AMACR, MALAT1, PCAT29, PSA density, and MRI maxPI-RADS resulted in an AUC of 0.867 (p< 0.001) with a sensitivity, specificity, and accuracy of 87%, 83%, and 85%, respectively, thus surpassing the predictive power of the individual markers. These findings highlight the potential of uEV transcripts in combination with clinical parameters as monitoring markers during the AS of PCa.
血清前列腺特异性抗原（PSA）及其衍生指标与磁共振成像（MRI）在前列腺癌（PCa）主动监测（AS）患者的风险再分层预测中缺乏足够的特异性和敏感性。本研究通过分析AS患者尿液细胞外囊泡（uEV）中的特定转录本，旨在预测对照活检中的PCa风险再分层（定义为ISUP 1级伴PSA > 10 ng/mL或ISUP 2-5级伴任意PSA水平）。在对照活检前，前瞻性收集了72例患者的尿液样本，其中43%在AS期间发生风险再分层。从uEV中分离RNA并进行多重逆转录与预扩增后，通过定量PCR对29种PCa相关转录本进行定量分析。采用受试者工作特征（ROC）曲线分析，通过计算曲线下面积（AUC）评估转录本预测PCa风险再分层的能力，并与临床参数进行比较，随后进行多变量回归分析。ROC曲线分析显示AMACR、HPN、MALAT1、PCA3和PCAT29具有预测潜力（AUC = 0.614–0.655, p < 0.1）。PSA、PSA密度、PSA速率及MRI最大PI-RADS的AUC值为0.681–0.747（p < 0.05），预测PCa风险再分层的准确率为64–68%。整合AMACR、MALAT1、PCAT29、PSA密度和MRI最大PI-RADS的联合模型获得AUC为0.867（p < 0.001），其敏感性、特异性和准确率分别为87%、83%和85%，显著超越单一标志物的预测效能。这些发现凸显了uEV转录本联合临床参数作为PCa主动监测过程中监测标志物的潜力。
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