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
    Country/TerritoryUnited 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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