Division of Functional Genomics, Chair for Biochemistry and Molecular Medicine, Witten/Herdecke University, Witten, Germany.
Centre for Biomedical Education and Research (ZBAF), Witten/Herdecke University, Witten, Germany.
PLoS One. 2021 Mar 24;16(3):e0247930. doi: 10.1371/journal.pone.0247930. eCollection 2021.
Prostate cancer (PCa) is the most common cancer and the third most frequent cause of male cancer death in Germany. MicroRNAs (miRNA) appear to be involved in the development and progression of PCa. A diagnostic differentiation from benign prostate hyperplasia (BPH) is often only possible through transrectal punch biopsy. This procedure is described as painful and carries risks. It was investigated whether urinary miRNAs can be used as biomarkers to differentiate the prostate diseases above. Therefore urine samples from urological patients with BPH (25) or PCa (28) were analysed using Next-Generation Sequencing to detect the expression profile of total and exosomal miRNA/piRNA. 79 miRNAs and 5 piwi-interacting RNAs (piRNAs) were significantly differentially expressed (adjusted p-value < 0.05 and log2-Fc > 1 or < -1). Of these, 6 miRNAs and 2 piRNAs could be statistically validated (AUC on test cohort > = 0.7). In addition, machine-learning algorithms were used to identify a panel of 22 additional miRNAs, whose interaction makes it possible to differentiate the groups as well. There are promising individual candidates for potential use as biomarkers in prostate cancer. The innovative approach of applying machine learning methods to this kind of data could lead to further small RNAs coming into scientific focus, which have so far been neglected.
前列腺癌（PCa）是德国最常见的癌症和男性癌症死亡的第三大常见原因。 microRNAs（miRNA）似乎参与了 PCa 的发展和进展。从良性前列腺增生（BPH）中进行诊断区分通常只能通过经直肠穿刺活检来完成。该过程被描述为疼痛且具有风险。研究人员调查了尿液中的 miRNA 是否可作为生物标志物来区分上述前列腺疾病。因此，使用下一代测序分析了来自患有 BPH（25 个）或 PCa（28 个）的泌尿科患者的尿液样本，以检测总 miRNA/piRNA 和外泌体 miRNA/piRNA 的表达谱。 79 个 miRNA 和 5 个 piwi 相互作用 RNA（piRNA）的表达水平存在显著差异（调整后的 p 值<0.05，log2-FC > 1 或 <-1）。其中，6 个 miRNA 和 2 个 piRNA 可以通过统计学验证（在测试队列中 AUC>=0.7）。此外，还使用机器学习算法来识别 22 个额外 miRNA 的组合，其相互作用也可以区分这些组。有一些有前途的个体候选物可作为前列腺癌的潜在生物标志物。应用机器学习方法来处理这种数据的创新方法可能会导致更多的小 RNA 受到科学界的关注，而这些小 RNA 迄今为止一直被忽视。
