School of Computer, Wuhan University, Wuhan, People's Republic of China.
IET Syst Biol. 2014 Jun;8(3):96-103. doi: 10.1049/iet-syb.2013.0025.
It has been proved and widely acknowledged that messenger RNAs can talk to each other by competing for a limited pool of miRNAs. The competing endogenous RNAs are called as ceRNAs. Although some researchers have recently used ceRNAs to do biological function annotations, few of them have investigated the ceRNA network on specific disease systematically. In this work, using both miRNA expression data and mRNA expression data of breast cancer patient as well as the miRNA target relations, the authors proposed a computational method to construct a breast-cancer-specific ceRNA network by checking whether the shared miRNA sponges between the gene pairs are significant. The ceRNA network is shown to be scale-free, thus the topological characters such as hub nodes and communities may provide important clues for the biological mechanism. Through investigation on the communities (the dense clusters) in the network, it was found that they are related to cancer hallmarks. In addition, through function annotation of the hub genes in the network, it was found that they are related to breast cancer. Moreover, classifiers based on the discriminative hubs can significantly distinguish breast cancer patients' risks of distant metastasis in all the three independent data sets.
已经有大量证据证明，信使 RNA 可以通过竞争有限的 miRNA 库进行相互交流。这些竞争性内源 RNA 被称为 ceRNA。尽管最近一些研究人员已经使用 ceRNA 进行生物学功能注释，但很少有人系统地研究特定疾病的 ceRNA 网络。在这项工作中，作者使用 miRNA 表达数据和乳腺癌患者的 mRNA 表达数据以及 miRNA 靶关系，提出了一种通过检查基因对之间是否存在共享 miRNA 海绵来构建乳腺癌特异性 ceRNA 网络的计算方法。结果表明，ceRNA 网络是无标度的，因此拓扑特征（如枢纽节点和社区）可能为生物机制提供重要线索。通过对网络中的社区（密集聚类）进行调查，发现它们与癌症特征有关。此外，通过对网络中枢纽基因的功能注释，发现它们与乳腺癌有关。此外，基于判别性枢纽基因的分类器可以显著区分三个独立数据集的乳腺癌患者发生远处转移的风险。
