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Title 

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

Authors 

Jung Kyoon ChoiUngsik YuO J YooS Kim

Publisher 

Oxford University Press (OUP)

Issue Date 

2005

Citation 

Bioinformatics, vol. 21, no. 24, pp. 4348-4355

Keywords 

cancer geneticscancer invasioncancer tissuecluster analysisDNA microarrayhumandata interpretation, statisticaldatabases, genetichumansoligonucleotide array sequence

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 pu blished 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.

ISSN 

1367-4803

Link 

http://dx.doi.org/10.1093/bioinformatics/bti722

Appears in Collections

1. Journal Articles > Journal Articles

Registered Date

2019-05-02


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