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Bioinformatics Advance Access originally published online on October 18, 2005
Bioinformatics 2005 21(24):4348-4355; 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{at}oxfordjournals.org
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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,*

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

*To whom correspondence should be addressed.

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 2 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 and 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.

Supplementary information: Supplementary data are available at Bioinformatics online.

Contact: sskimb{at}ssu.ac.kr

Received on May 19, 2005; revised on September 2, 2005; accepted on October 16, 2005

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