This article is part of the supplement: The 2007 International Conference on Bioinformatics & Computational Biology (BIOCOMP'07) .

Research

An integrative approach to characterize disease-specific pathways and their coordination: a case study in cancer

Min Xu¹ , Ming-Chih J Kao² , Juan Nunez-Iglesias¹ , Joseph R Nevins³ , Mike West⁴ and Xianghong Jasmine Zhou¹

¹  Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA

²  School of Medicine, University of Michigan, Ann Arbor, MI, USA

³  Institute for Genome Sciences and Policy, Duke University, Durham, NC, USA

⁴  Institute of Statistics and Decision Sciences, Duke University, Durham, NC, USA

author email corresponding author email

BMC Genomics 2008, 9(Suppl 1):S12doi:10.1186/1471-2164-9-S1-S12

Published:	20 March 2008

Abstract

Background

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.

Results

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.

Conclusion

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.