Meta-analysis of cancer microarray data reveals signaling pathway hotspots

Pankaj Chopra, Jaewoo Kang, Seung Mo Hong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent studies have shown that the identification of deregulated bio-molecular pathways in cancer may be more important than identification of individual genes through differential expression. Since the same pathway can be deregulated by a different subset of genes, it is critical to study pathways as a whole, rather than focus on individual genes. Most papers on meta-analysis of cancer datasets focus on the identification of a set of individual genes, and not on pathways, protein families and gene ontology terms. We have analyzed data from 87 microarray datasets consisting of 5,126 samples and spanning 25 different types of cancer. We have identified 212 KEGG pathways, 578 protein families and 1,717 gene ontology terms that are statistically significant (p < 0.01), and deregulated in cancer. Many of the top pathways and proteins from our meta-analysis, e.g., Jak-Stat pathway, Annexin proteins etc. are already known to play a pivotal role in carcinogenesis. To the best of our knowledge this is the largest meta analysis of cancer pathways, protein families and gene ontology terms to date.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009
Pages214-219
Number of pages6
DOIs
Publication statusPublished - 2009
Event2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009 - Washington, DC, United States
Duration: 2009 Nov 12009 Nov 4

Publication series

NameProceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009

Other

Other2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009
CountryUnited States
CityWashington, DC
Period09/11/109/11/4

Keywords

  • Cancer
  • GO
  • Gene expression
  • KEGG
  • Meta-analysis
  • Microarray
  • PFAM
  • Pathway
  • Signaling

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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