Wang Kevin R, McGeachie Michael J
Roxbury Latin, Boston, MA 02132, USA.
Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
Noncoding RNA. 2022 Jun 23;8(4):45. doi: 10.3390/ncrna8040045.
MiRNAs have been shown to play a powerful regulatory role in the progression of serious diseases, including cancer, Alzheimer's, and others, raising the possibility of new miRNA-based therapies for these conditions. Current experimental methods, such as differential expression analysis, can discover disease-associated miRNAs, yet many of these miRNAs play no functional role in disease progression. Interventional experiments used to discover disease causal miRNAs can be time consuming and costly. We present DisiMiR: a novel computational method that predicts pathogenic miRNAs by inferring biological characteristics of pathogenicity, including network influence and evolutionary conservation. DisiMiR separates disease causal miRNAs from merely disease-associated miRNAs, and was accurate in four diseases: breast cancer (0.826 AUC), Alzheimer's (0.794 AUC), gastric cancer (0.853 AUC), and hepatocellular cancer (0.957 AUC). Additionally, DisiMiR can generate hypotheses effectively: 78.4% of its false positives that are mentioned in the literature have been confirmed to be causal through recently published research. In this work, we show that DisiMiR is a powerful tool that can be used to efficiently and flexibly to predict pathogenic miRNAs in an expression dataset, for the further elucidation of disease mechanisms, and the potential identification of novel drug targets.
微小RNA（miRNAs）已被证明在包括癌症、阿尔茨海默病等严重疾病的进展中发挥着重要的调控作用，这为基于miRNA的针对这些病症的新疗法带来了可能性。当前的实验方法，如差异表达分析，可以发现与疾病相关的miRNAs，但其中许多miRNAs在疾病进展中并无功能作用。用于发现疾病因果性miRNAs的干预实验可能既耗时又昂贵。我们提出了DisiMiR：一种通过推断致病性的生物学特征（包括网络影响和进化保守性）来预测致病性miRNAs的新型计算方法。DisiMiR将疾病因果性miRNAs与仅仅是疾病相关的miRNAs区分开来，并且在四种疾病中表现准确：乳腺癌（曲线下面积[AUC]为0.826）、阿尔茨海默病（AUC为0.794）、胃癌（AUC为0.853）和肝细胞癌（AUC为0.957）。此外，DisiMiR能够有效地生成假设：其文献中提到的78.4%的假阳性已通过最近发表的研究被证实具有因果关系。在这项工作中，我们表明DisiMiR是一种强大的工具，可用于在表达数据集中高效且灵活地预测致病性miRNAs，以进一步阐明疾病机制，并潜在地识别新的药物靶点。
