The Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai 600 113, India.
J Biosci. 2022;47.
The role played by the topological structure of biological networks in their dynamics and function is receiving increasing attention over the last decade as large-throughput experiments have provided large volumes of highly resolved data on the interactions between the components of such networks. This has provided new perspectives on systems diseases: for example, there has been a gradual shift in cancer research away from the study of individual molecules and of single gene mutations to the emerging consensus that it is a complex disease involving large-scale disruptions in the intracellular signaling network. One of the drawbacks of a systems- or network-based approach is the large number of cellular agents whose interactions need to be investigated. We tried to solve this problem by taking a mesoscopic view of the cancer diseases-genes network, whose modular organization we studied after projecting it onto two networks, one comprising only disease types and the other consisting of only genes related to one or more categories of cancer. Using community partitioning, we identified several modules in these networks. Projecting cancer gene clusters onto an abstract 'modular space' allows us to infer the relations between different tumor types. By classifying the functional role of particular genes in terms of their inter- and intra-modular connectivity, we identified a number of genes that play the key role of 'connector hubs' in the network. Using data from the human protein- protein interaction network we showed that genes that are 'connector hubs' or 'global hubs' are, in fact, much more likely to be related to cancer than other genes. More important from a therapeutic point of view, we showed that the connector hubs in the cancer gene network are involved in a significantly larger number of human signaling pathways associated with cancer than other types of cancer genes. Furthermore, the types of cancer linked to connector hub genes have significantly reduced survival rates compared with other types of cancer, thereby enhancing their importance in the search for potential therapeutic targets.
在过去的十年中，随着高通量实验提供了大量关于这些网络组件之间相互作用的高度解析数据，生物网络的拓扑结构在其动力学和功能中的作用受到了越来越多的关注。这为系统疾病提供了新的视角：例如，癌症研究已经逐渐从研究单个分子和单个基因突变转向新兴共识，即这是一种涉及细胞内信号网络大规模中断的复杂疾病。系统或基于网络的方法的一个缺点是需要研究大量的细胞因子，我们试图通过采用癌症疾病-基因网络的介观视图来解决这个问题，我们研究了将其投影到两个网络之一后的模块化组织，一个网络仅包含疾病类型，另一个网络仅包含与一种或多种癌症类别相关的基因。使用社区划分，我们在这些网络中识别出了几个模块。将癌症基因簇投射到一个抽象的“模块化空间”上，使我们能够推断不同肿瘤类型之间的关系。通过根据其模块间和模块内的连通性对特定基因的功能作用进行分类，我们确定了一些在网络中起关键作用的“连接器枢纽”基因。使用来自人类蛋白质-蛋白质相互作用网络的数据，我们表明，作为“连接器枢纽”或“全局枢纽”的基因实际上比其他基因更有可能与癌症有关。从治疗的角度来看，更重要的是，我们表明，癌症基因网络中的连接器枢纽涉及与癌症相关的人类信号通路的数量明显多于其他类型的癌症基因。此外，与连接器枢纽基因相关的癌症类型的存活率明显低于其他类型的癌症，从而提高了它们在寻找潜在治疗靶点方面的重要性。
