Le Thuc Duy, Zhang Junpeng, Liu Lin, Li Jiuyong
School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, SA, 5095, Australia.
School of Engineering, Dali University, Dali, China.
Brief Bioinform. 2017 Jul 1;18(4):577-590. doi: 10.1093/bib/bbw042.
Recent findings show that coding genes are not the only targets that miRNAs interact with. In fact, there is a pool of different RNAs competing with each other to attract miRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The ceRNAs indirectly regulate each other via the titration mechanism, i.e. the increasing concentration of a ceRNA will decrease the number of miRNAs that are available for interacting with other targets. The cross-talks between ceRNAs, i.e. their interactions mediated by miRNAs, have been identified as the drivers in many disease conditions, including cancers. In recent years, some computational methods have emerged for identifying ceRNA-ceRNA interactions. However, there remain great challenges and opportunities for developing computational methods to provide new insights into ceRNA regulatory mechanisms.In this paper, we review the publically available databases of ceRNA-ceRNA interactions and the computational methods for identifying ceRNA-ceRNA interactions (also known as miRNA sponge interactions). We also conduct a comparison study of the methods with a breast cancer dataset. Our aim is to provide a current snapshot of the advances of the computational methods in identifying miRNA sponge interactions and to discuss the remaining challenges.
最近的研究结果表明,编码基因并非是与微小RNA(miRNA)相互作用的唯一靶点。事实上,存在一组相互竞争以吸引miRNA进行相互作用的不同RNA,因此它们作为竞争性内源RNA(ceRNA)发挥作用。ceRNA通过滴定机制间接相互调节,即ceRNA浓度的增加会减少可用于与其他靶点相互作用的miRNA数量。ceRNA之间的相互作用,即由miRNA介导的相互作用,已被确定为包括癌症在内的许多疾病状态的驱动因素。近年来,出现了一些用于识别ceRNA-ceRNA相互作用的计算方法。然而,开发计算方法以深入了解ceRNA调控机制仍面临巨大挑战和机遇。在本文中,我们回顾了公开可用的ceRNA-ceRNA相互作用数据库以及识别ceRNA-ceRNA相互作用(也称为miRNA海绵相互作用)的计算方法。我们还使用乳腺癌数据集对这些方法进行了比较研究。我们的目的是提供计算方法在识别miRNA海绵相互作用方面进展的当前概况,并讨论剩余的挑战。