The Laboratory of Molecular Medicine, Institute of Chemical Biology and Fundamental Medicine SB RAS, Novosibirsk, Russia.
The Laboratory of Biomedical Technologies, Centre of New Medical Technologies,"E. Meshalkin National Medical Research Center" of the Ministry of Health of the Russian Federation, Novosibirsk, Russia.
PLoS One. 2019 Apr 10;14(4):e0215003. doi: 10.1371/journal.pone.0215003. eCollection 2019.
Urine of prostate cancer (PCa) carries miRNAs originated from prostate cancer cells as a part of both nucleoprotein complexes and cell-secreted extracellular vesicles. The analysis of such miRNA-markers in urine can be a convenient option for PCa screening. The aims of this study were to reveal miRNA-markers of PCa in urine and design a robust and precise diagnostic test, based on miRNA expression analysis. The expression analysis of the 84 miRNAs in paired urine extracellular vesicles (EVs) and cell free urine supernatant samples from healthy donors, patients with benign and malignant prostate tumours was done using miRCURY LNA miRNA qPCR Panels (Exiqon, Denmark). Sets of miRNAs differentially expressed between the donor groups were found in urine EVs and urine supernatant. Diagnostically significant miRNAs were selected and algorithm of data analysis, based on expression data on 24-miRNA in urine and obtained using 17 analytical systems, was designed. The developed algorithm of data analysis describes a series of steps necessary to define cut-off values and sequentially analyze miRNA expression data according to the cut-offs to facilitate classification of subjects in case/control groups and allows to detect PCa patients with 97.5% accuracy.
前列腺癌(PCa)尿液携带有源自前列腺癌细胞的 miRNA,作为核蛋白复合物和细胞分泌的细胞外囊泡的一部分。对尿液中此类 miRNA 标志物的分析可能是 PCa 筛查的一种便捷选择。本研究旨在揭示尿液中 PCa 的 miRNA 标志物,并基于 miRNA 表达分析设计一种强大而精确的诊断测试。使用 miRCURY LNA miRNA qPCR 面板(丹麦 Exiqon)对来自健康供体、良性和恶性前列腺肿瘤患者的配对尿细胞外囊泡(EVs)和无细胞尿上清样本中的 84 个 miRNA 的表达进行了分析。在尿 EVs 和尿上清中发现了供体组之间差异表达的 miRNA 集。选择具有诊断意义的 miRNA,并基于在尿中使用 17 个分析系统获得的 24 个 miRNA 的表达数据设计数据分析算法。开发的数据分析算法描述了根据截止值对 miRNA 表达数据进行连续分析以对病例/对照组进行分类的必要步骤,并允许以 97.5%的准确度检测 PCa 患者。