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


CONCLUSIONS
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.
Xu
Min
M
Program in Molecular and Computational Biology, University of Southern California, Los Angeles, CA, USA. mxu@usc.edu
Kao
Ming-Chih J
MC
Nunez-Iglesias
Juan
J
Nevins
Joseph R
JR
West
Mike
M
Zhou
Xianghong Jasmine
XJ
D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D017479:Receptors, Platelet-Derived Growth Factor / Q000235:genetics / Q000378:metabolism; D015398:Signal Transduction / Q000235:genetics*; D014157:Transcription Factors / Q000378:metabolism
D014157:Transcription Factors; D017479:Receptors, Platelet-Derived Growth Factor