Institute of Computational Science and Technology, Guangzhou University, Guangzhou, People's Republic of China.
Department of Information Engineering, Wenzhou Vocational College of Science and Technology, Wenzhou, People's Republic of China.
IET Syst Biol. 2018 Dec;12(6):273-278. doi: 10.1049/iet-syb.2018.5025.
MicroRNAs (miRNAs) are a class of small endogenous non-coding genes that play important roles in post-transcriptional regulation as well as other important biological processes. Accumulating evidence indicated that miRNAs were extensively involved in the pathology of cancer. However, determining which miRNAs are related to a specific cancer is problematic because one miRNA may target multiple genes and one gene may be targeted by multiple miRNAs. The authors proposed a new approach, named miR_SubPath, to identify cancer-associated miRNAs by three steps. The targeted genes were determined based on differentially expressed genes in significant dysfunctional subpathways. Then the candidate miRNAs were determined according to miRNA-genes associations. Finally, these candidate miRNAs were ranked based on their relations with some seed miRNAs in a functional similarity network. Results on real-world datasets showed that the proposed miR_SubPath method was more robust and could identify more cancer-related miRNAs than a prior approach, miR_Path, miR_Clust and Zhang's method.
微小 RNA（miRNAs）是一类小型内源性非编码基因，在转录后调控以及其他重要的生物学过程中发挥重要作用。越来越多的证据表明，miRNAs 广泛参与癌症的病理过程。然而，确定哪些 miRNAs 与特定的癌症有关是一个问题，因为一个 miRNA 可能靶向多个基因，一个基因可能被多个 miRNAs 靶向。作者提出了一种新的方法，名为 miR_SubPath，通过三个步骤来识别与癌症相关的 miRNAs。根据显著功能失调子路径中的差异表达基因来确定靶向基因。然后根据 miRNA-genes 关联来确定候选 miRNAs。最后，根据它们与功能相似网络中某些种子 miRNAs 的关系对这些候选 miRNAs 进行排序。在真实数据集上的结果表明，所提出的 miR_SubPath 方法比以前的方法 miR_Path、miR_Clust 和 Zhang 的方法更稳健，并且可以识别更多与癌症相关的 miRNAs。
