A urine extracellular vesicle lncRNA classifier for high-grade prostate cancer and increased risk of progression: A multi-center study.
A urine extracellular vesicle lncRNA classifier for high-grade prostate cancer and increased risk of progression: A multi-center study.
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DOI：
10.1016/j.xcrm.2023.101240
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发表时间：
2023-10-17
影响因子：
14.3
通讯作者：
He, Ya-Di
中科院分区：
文献类型：
--
作者：
Tao, Wen;Wang, Bang-Yu;Luo, Liang;Li, Qing;Meng, Zhan-Ao;Xia, Tao-Lin;Deng, Wei-Ming;Yang, Ming;Zhou, Jing;Zhang, Xin;Gao, Xin;Li, Liao-Yuan;He, Ya-Di
为了构建一个尿液细胞外囊泡长非编码 RNA (lncRNA) 分类器，可以检测 2 级或更高级别的高级别前列腺癌 (PCa)，并估计主动监测期间进展的风险，我们通过对 TAHSY、TCGA 和 GEO 数据库的队列进行组合分析来识别高级别 PCa 特异性 lncRNA。我们开发并验证了可以检测高级 PCa 的 3-lncRNA 诊断模型（Clnc，由 AC015987.1、CTD-2589M5.4、RP11-363E6.3 制成）。在训练队列 (n = 350)、两个独立队列 (n = 232；n = 251) 和 TCGA 队列中，Clnc 显示出比前列腺癌抗原 3 (PCA3)、多参数磁共振成像 (mpMRI) 和两个风险计算器（前列腺癌预防试验 [PCPT]-RC 2.0 和欧洲前列腺癌筛查随机研究 [ERSPC]-RC）更高的准确性（n = 499）。在前瞻性主动监测队列 (n = 182) 中，诊断时的 Clnc 仍然是整体主动监测进展的强大独立预测因子。因此，Clnc 是高级别 PCa 的潜在生物标志物，也可以作为改进主动监测候选者选择的生物标志物。收集隔夜尿液样本进行 lncRNA 检测 开发了用于检测高级别前列腺癌的 lncRNA 诊断模型 该模型可以识别 PSA 范围不明确的患者的高级别癌症 该模型可以帮助选择主动监测的候选者鉴定并验证一系列尿液长非编码RNA标记物，可辅助高级别前列腺癌的准确诊断。此外，这些尿液 RNA 标记物对于选择患有前列腺癌的候选者进行主动监测也很有用。
To construct a urine extracellular vesicle long non-coding RNA (lncRNA) classifier that can detect high-grade prostate cancer (PCa) of grade group 2 or greater and estimate the risk of progression during active surveillance, we identify high-grade PCa-specific lncRNAs by combined analyses of cohorts from TAHSY, TCGA, and the GEO database. We develop and validate a 3-lncRNA diagnostic model (Clnc, being made of AC015987.1, CTD-2589M5.4, RP11-363E6.3) that can detect high-grade PCa. Clnc shows higher accuracy than prostate cancer antigen 3 (PCA3), multiparametric magnetic resonance imaging (mpMRI), and two risk calculators (Prostate Cancer Prevention Trial [PCPT]-RC 2.0 and European Randomized Study of Screening for Prostate Cancer [ERSPC]-RC) in the training cohort (n = 350), two independent cohorts (n = 232; n = 251), and TCGA cohort (n = 499). In the prospective active surveillance cohort (n = 182), Clnc at diagnosis remains a powerful independent predictor for overall active surveillance progression. Thus, Clnc is a potential biomarker for high-grade PCa and can also serve as a biomarker for improved selection of candidates for active surveillance. An overnight urine sample is collected for lncRNA testing An lncRNA diagnostic model for detecting high-grade prostate cancer is developed This model can identify high-grade cancer in patients with an equivocal PSA range This model can assist in the selection of candidates for active surveillance Tao et al. identify and validate a series of urine long non-coding RNA markers, which can assist in the accurate diagnosis of high-grade prostate cancer. In addition, these urine RNA markers are also useful for the selection of candidates with prostate cancer for active surveillance.
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