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

Shinichiro Wachi, Ken Y Yoneda, Reen Wu

Research output: Contribution to journalArticle

276 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)4205-4208
Number of pages4
JournalBioinformatics
Volume21
Issue number23
DOIs
StatePublished - Dec 2005

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Lung Cancer
Centrality
Gene Expression Profiling
Lung Neoplasms
Genes
Tissue
Gene
Essential Genes
Cancer
Protein Interaction Maps
Neoplasm Genes
Transcriptome
Proteins
Oncology
Neoplasms
Microarrays
Protein Interaction Networks
Electric network analysis
Yeasts
Network Analysis

ASJC Scopus subject areas

  • Clinical Biochemistry
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues. / Wachi, Shinichiro; Yoneda, Ken Y; Wu, Reen.

In: Bioinformatics, Vol. 21, No. 23, 12.2005, p. 4205-4208.

Research output: Contribution to journalArticle

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