An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer.
An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer.
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DOI：
10.1186/1471-2164-9-s1-s12
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发表时间：
2008
期刊：
影响因子：
4.4
通讯作者：
Zhou XJ
中科院分区：
文献类型：
--
作者：
Xu M;Kao MC;Nunez-Iglesias J;Nevins JR;West M;Zhou XJ
微阵列技术在疾病研究中最常见的应用是识别在疾病组织和正常组织中差异表达的基因。然而，众所周知，在复杂的疾病中，表型不仅由基因决定，还由遗传网络结构决定。通常，是许多基因的相互作用导致了表型变异。在这项工作中，以癌症为例，我们开发了基于图的方法来整合多个微阵列数据集，以发现与疾病相关的共表达网络模块。我们提出了一种同时考虑共表达动态和网络拓扑信息的非监督方法来同时推断网络模块和它们被激活或去激活的表型条件。使用我们的方法，我们已经发现了针对癌症或癌症亚型的网络模块。这些模块中的许多与它们的功能注释或先前已知的参与癌症有关的模块一致或得到它们的支持。特别是，我们确定了一个在乳腺癌中主要激活并参与肿瘤抑制的模块。虽然这个模块的个别成分被认为与肿瘤抑制有关，但它们的协调功能从未被阐明。在这里，通过采用网络的观点，我们已经确定了它们之间的相互关系，特别是一个中枢基因PDGFRL，它可能在这个肿瘤抑制网络中发挥重要作用。使用基于网络的方法，我们的方法为表征癌症和癌症亚型的复杂细胞机制提供了新的见解。通过引入共表达动力学信息，我们的方法不仅可以提取出比单纯基于网络拓扑的功能同质模块更多的功能同质模块，而且还可以揭示共表达之外的途径协调。
The most common application of microarray technology in disease research is to identify genes differentially expressed in disease versus normal tissues. However, it is known that, in complex diseases, phenotypes are determined not only by genes, but also by the underlying structure of genetic networks. Often, it is the interaction of many genes that causes phenotypic variations. In this work, using cancer as an example, we develop graph-based methods to integrate multiple microarray datasets to discover disease-related co-expression network modules. We propose an unsupervised method that take into account both co-expression dynamics and network topological information to simultaneously infer network modules and phenotype conditions in which they are activated or de-activated. Using our method, we have discovered network modules specific to cancer or subtypes of cancers. Many of these modules are consistent with or supported by their functional annotations or their previously known involvement in cancer. In particular, we identified a module that is predominately activated in breast cancer and is involved in tumor suppression. While individual components of this module have been suggested to be associated with tumor suppression, their coordinated function has never been elucidated. Here by adopting a network perspective, we have identified their interrelationships and, particularly, a hub gene PDGFRL that may play an important role in this tumor suppressor network. Using a network-based approach, our method provides new insights into the complex cellular mechanisms that characterize cancer and cancer subtypes. By incorporating co-expression dynamics information, our approach can not only extract more functionally homogeneous modules than those based solely on network topology, but also reveal pathway coordination beyond co-expression.