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Bioinformatics Advance Access published online on October 18, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti722
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received May 19, 2005
Revised September 2, 2005
Accepted October 16, 2005

Article

Differential coexpression analysis using microarray data and its application to human cancer

Jung Kyoon Choi 1, Ungsik Yu 1, Ook Joon Yoo 2, and Sangsoo Kim 3*

1 National Genome Information Center, Korea Research Institute of Bioscience and Biotechnology, 52 Ueun-dong, Yuseong-gu, Daejeon, Korea
2 Department of Biological Sciences, Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong, Yuseong-gu, Daejeon, Korea
3 Department of Bioinformatics and Life Science, Soongsil University, Seoul, Korea

* To whom correspondence should be addressed.
Sangsoo Kim, E-mail: sskimb{at}ssu.ac.kr


  Abstract

Motivation: Microarrays have been used to identify differential expression of individual genes or cluster genes that are coexpressed over various conditions. However, alteration in coexpression relationships has not been studied. Here we introduce a model for finding differential coexpression from microarrays and test its biological validity with respect to cancer.

Results: We collected 10 published gene expression datasets from cancers of 13 different tissues and constructed two distinct coexpression networks: a tumor network and normal network. Comparison of the two networks showed that cancer affected many coexpression relationships. Functional changes such as alteration in energy metabolism, promotion of cell growth, enhanced immune activity were accompanied with coexpression changes. Coregulation of collagen genes that may control invasion and metastatic spread of tumor cells was also found. Cluster analysis in the tumor network identified groups of highly interconnected genes related to ribosomal protein synthesis, the cell cycle, and antigen presentation. Metallothionein expression was also found to be clustered, which may play a role in apoptosis control in tumor cells. Our results show that this model would serve as a novel method for analyzing microarrays beyond the specific implications for cancer.


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