Detection of gene pathways with predictive power for breast cancer prognosis Public Deposited

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  • Kosorok, Michael
    • Affiliation: Gillings School of Global Public Health, Department of Biostatistics
  • Ma, Shuangge
    • Other Affiliation: School of Public Health, Yale University, New Haven, CT 06520, USA
  • Abstract Background Prognosis is of critical interest in breast cancer research. Biomedical studies suggest that genomic measurements may have independent predictive power for prognosis. Gene profiling studies have been conducted to search for predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions. The goal of this study is to identify gene pathways with predictive power for breast cancer prognosis. Since our goal is fundamentally different from that of existing studies, a new pathway analysis method is proposed. Results The new method advances beyond existing alternatives along the following aspects. First, it can assess the predictive power of gene pathways, whereas existing methods tend to focus on model fitting accuracy only. Second, it can account for the joint effects of multiple genes in a pathway, whereas existing methods tend to focus on the marginal effects of genes. Third, it can accommodate multiple heterogeneous datasets, whereas existing methods analyze a single dataset only. We analyze four breast cancer prognosis studies and identify 97 pathways with significant predictive power for prognosis. Important pathways missed by alternative methods are identified. Conclusions The proposed method provides a useful alternative to existing pathway analysis methods. Identified pathways can provide further insights into breast cancer prognosis.
Date of publication
  • doi:10.1186/1471-2105-11-1
  • 20043860
Resource type
  • Article
Rights statement
  • In Copyright
Rights holder
  • Shuangge Ma et al.; licensee BioMed Central Ltd.
Journal title
  • BMC Bioinformatics
Journal volume
  • 11
Journal issue
  • 1
Page start
  • 1
  • English
Is the article or chapter peer-reviewed?
  • Yes
  • 1471-2105
Bibliographic citation
  • BMC Bioinformatics. 2010 Jan 01;11(1):1
  • Open Access
  • BioMed Central Ltd

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