Fiscon Giulia, Conte Federica, Farina Lorenzo, Paci Paola
Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, via dei Taurini 19, 00185 Rome, Italy.
SysBio Centre of Systems Biology, Piazza della Scienza, 3, 20126 Milan, Italy.
Genes (Basel). 2018 Aug 31;9(9):437. doi: 10.3390/genes9090437.
Network medicine relies on different types of networks: from the molecular level of protein⁻protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein⁻protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs-including long non-coding RNAs (lncRNAs) -competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes-called switch genes-critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes.
从蛋白质 - 蛋白质相互作用的分子水平到基因调控网络以及基因表达的相关性研究。在基于蛋白质 - 蛋白质相互作用（PPI）网络拓扑特性分析的网络方法中，我们讨论广泛应用的DIAMOnD（疾病模块检测）算法。DIAMOnD算法基于这样的假设：PPI网络可被视为地图，其中疾病可通过特定邻域内的局部扰动（即疾病模块）来识别。该算法通过利用连接重要性而非连接密度，对人类PPI网络进行系统分析，以发现新的疾病相关基因。在过去几年中，人们越来越关注理解转录后调控的分子机制，尤其侧重于非编码RNA，因为它们在生理和病理状态下正成为许多细胞过程的关键调节因子。最近的研究结果表明，编码基因并非微小RNA相互作用的唯一靶点。事实上，存在一组不同的RNA，包括长链非编码RNA（lncRNA），它们相互竞争以吸引微小RNA进行相互作用，从而作为竞争性内源RNA（ceRNA）发挥作用。调控网络框架为深入了解ceRNA调控机制提供了强大工具。在此，我们描述了一个最近开发的数据驱动模型，用于探索乳腺浸润性癌中与lncRNA相关的ceRNA活性。另一方面，共表达网络的一个非常有前景的例子是由SWIM（开关挖掘器）软件实现的网络，它将相关网络的拓扑特性与基因表达数据相结合，以识别一小部分与细胞表型剧烈变化密切相关的基因——称为开关基因。在此，我们介绍SWIM工具及其在癌症研究中的应用，并将其预测结果与DIAMOnD疾病基因进行比较。
