Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Harvard College, Cambridge, MA, USA.
Sci Rep. 2020 May 26;10(1):8705. doi: 10.1038/s41598-020-65633-6.
With critical roles in regulating gene expression, miRNAs are strongly implicated in the pathophysiology of many complex diseases. Experimental methods to determine disease related miRNAs are time consuming and costly. Computationally predicting miRNA-disease associations has potential applications in finding miRNA therapeutic pathways and in understanding the role of miRNAs in disease-disease relationships. In this study, we propose the MiRNA-disease Association Prediction (MAP) method, an in-silico method to predict and prioritize miRNA-disease associations. The MAP method applies a network diffusion approach, starting from the known disease genes in a heterogenous network constructed from miRNA-gene associations, protein-protein interactions, and gene-disease associations. Validation using experimental data on miRNA-disease associations demonstrated superior performance to two current state-of-the-art methods, with areas under the ROC curve all over 0.8 for four types of cancer. MAP is successfully applied to predict differential miRNA expression in four cancer types. Most strikingly, disease-disease relationships in terms of shared miRNAs revealed hidden disease subtyping comparable to that of previous work on shared genes between diseases, with applications for multi-omics characterization of disease relationships.
miRNAs 在调节基因表达方面发挥着关键作用，强烈提示它们与许多复杂疾病的病理生理学有关。用于确定与疾病相关的 miRNAs 的实验方法既耗时又昂贵。计算预测 miRNA 与疾病的关联具有在寻找 miRNA 治疗途径和理解 miRNA 在疾病-疾病关系中的作用方面的潜在应用。在这项研究中，我们提出了 miRNA 疾病关联预测 (MAP) 方法，这是一种用于预测和优先考虑 miRNA 与疾病关联的计算方法。MAP 方法应用网络扩散方法，从从 miRNA-基因关联、蛋白质-蛋白质相互作用和基因-疾病关联构建的异质网络中的已知疾病基因开始。使用 miRNA 与疾病关联的实验数据进行验证表明，该方法的性能优于两种当前最先进的方法，四种类型癌症的 ROC 曲线下面积均超过 0.8。MAP 成功应用于预测四种癌症类型中差异表达的 miRNA。最引人注目的是，根据共享 miRNAs 的疾病-疾病关系揭示了隐藏的疾病亚型，与疾病之间共享基因的先前工作相当，可用于对疾病关系进行多组学特征描述。
