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Abstract：
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.
摘要：
血清前列腺特异性抗原(PSA),其衍生物，和磁共振断层扫描（MRI）缺乏足够的特异性和敏感性来预测主动监测（AS）的前列腺癌（PCa）患者的风险重新分类。我们调查了AS患者的尿细胞外囊泡（uEV）中的选定转录本，以预测对照活检中的PCa风险重新分类（由PSA>10ng/mL的ISUP1或任何PSA水平的ISUP2-5定义）。在对照活检之前，前瞻性地收集了72名患者的尿液样本，其中43%在AS期间被重新分类。从uEV中分离RNA后，多重逆转录，和预扩增，通过定量PCR对29个PCa相关转录物进行定量。通过计算曲线下面积(AUC),通过受试者工作特征(ROC)曲线分析评估转录物指示PCa风险重新分类的预测能力,然后与临床参数进行比较,然后进行多变量回归分析。ROC曲线分析揭示了AMACR的预测潜力，HPN,MALAT1、PCA3和PCAT29(AUC=0.614-0.655,p<0.1)。PSA,PSA密度,PSA速度,MRImaxPI-RADS显示AUC值为0.681-0.747（p<0.05），指示PCa风险重新分类的准确性为64-68%。包括AMACR的模型，MALAT1,PCAT29,PSA密度,MRImaxPI-RADS的AUC为0.867（p<0.001），特异性，准确率为87%，83%,85%,分别,从而超过了单个标记的预测能力。这些发现突出了uEV转录本与临床参数结合作为PCaAS期间监测标志物的潜力。
