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Bioinformatics Advance Access originally published online on September 27, 2005
Bioinformatics 2005 21(23):4205-4208; doi:10.1093/bioinformatics/bti688
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oxfordjournals.org

Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues

Shinichiro Wachi *, Ken Yoneda and Reen Wu

Center for Comparative Respiratory Biology and Medicine and Division of Pulmonary/Critical Care Medicine, University of California Davis, CA 95616, USA

*To whom correspondence should be addressed.

Motivation: Global protein interaction network (interactome) analysis provides an effective way to understand the relationships between genes. Through this approach, it was demonstrated that the essential genes in yeast tend to be highly connected as well as connected to other highly connected genes. This is in contrast to the genes that are not essential, which share neither of these properties. Using a similar interactome-transcriptome approach, the topological features in the interactome of differentially expressed genes in lung squamous cancer tissues are assessed.

Results: This analysis reveals that the genes that are differentially elevated, as obtained from the microarray gene profiling data, in cancer are well connected, whereas the suppressed genes and randomly selected ones are less so. These results support the notion that a topological analysis of cancer genes using protein interaction data will allow the placement of the list of genes, often of the disparate nature, into the global, systematic context of the cell. The result of this type of analysis may provide the rationale for therapeutic targets in cancer treatment.

Contact: swachi{at}ucdavis.edu

Supplementary information: Supplementary data for this paper are available on Bioinformatics online.

Received on June 9, 2005; revised on September 7, 2005; accepted on September 21, 2005

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