Department of Urology, Hospital Universitario Reina Sofía, Universidad de Córdoba, Investigación Biomédica de Córdoba, Córdoba, Spain.
Department of Urology, Hospital Infanta Margarita, Córdoba, Spain.
Prostate. 2023 Oct;83(14):1323-1331. doi: 10.1002/pros.24594. Epub 2023 Jul 6.
Current pathways in early diagnosis of prostate cancer (PCa) can lead to unnecessary biopsy procedures. Here, we used telomere analysis to develop and evaluate ProsTAV®, a risk model for significant PCa (Gleason score >6), with the objective of improving the PCa diagnosis pathway.
This retrospective, multicentric study analyzed telomeres from patients with serum PSA 3-10 ng/mL. High-throughput quantitative fluorescence in-situ hybridization was used to evaluate telomere-associated variables (TAVs) in peripheral blood mononucleated cells. ProsTAV® was developed by multivariate logistics regression based on three clinical variables and six TAVs. The predictive capacity and accuracy of ProsTAV® were summarized by receiver operating characteristic (ROC) curves and its clinical benefit with decision curves analysis.
Telomeres from 1043 patients were analyzed. The median age of the patients was 63 years, with a median PSA of 5.2 ng/mL and a percentage of significant PCa of 23.9%. A total of 874 patients were selected for model training and 169 patients for model validation. The area under the ROC curve of ProsTAV® was 0.71 (95% confidence interval [CI], 0.62-0.79), with a sensitivity of 0.90 (95% CI, 0.88-1.0) and specificity of 0.33 (95% CI, 0.24-0.40). The positive predictive value was 0.29 (95% CI, 0.21-0.37) and the negative predictive value was 0.91 (95% CI, 0.83-0.99). ProsTAV® would make it possible to avoid 33% of biopsies.
ProsTAV®, a predictive model based on telomere analysis through TAV, could be used to increase the prediction capacity of significant PCa in patients with PSA between 3 and 10 ng/mL.
当前前列腺癌（PCa）早期诊断的途径可能导致不必要的活检。在这里，我们使用端粒分析开发并评估了 ProsTAV®，这是一种用于显著前列腺癌（Gleason 评分>6）的风险模型，目的是改善前列腺癌诊断途径。
本回顾性多中心研究分析了血清 PSA 为 3-10ng/mL 的患者的端粒。使用高通量荧光原位杂交技术评估外周血单核细胞中端粒相关变量（TAV）。ProsTAV®是通过多变量逻辑回归基于三个临床变量和六个 TAV 开发的。通过接收者操作特征（ROC）曲线和决策曲线分析总结 ProsTAV®的预测能力和准确性及其临床获益。
分析了 1043 例患者的端粒。患者的中位年龄为 63 岁，中位 PSA 为 5.2ng/mL，显著前列腺癌的比例为 23.9%。共有 874 例患者用于模型训练，169 例患者用于模型验证。ProsTAV®的 ROC 曲线下面积为 0.71（95%置信区间 [CI]，0.62-0.79），敏感性为 0.90（95% CI，0.88-1.0），特异性为 0.33（95% CI，0.24-0.40）。阳性预测值为 0.29（95% CI，0.21-0.37），阴性预测值为 0.91（95% CI，0.83-0.99）。ProsTAV®可使 33%的活检得以避免。
基于端粒分析通过 TAV 构建的预测模型，可用于提高 PSA 为 3-10ng/mL 之间的患者显著前列腺癌的预测能力。
