Identification of breast cancer prognosis markers via integrative analysis

Shuangge Ma, Ying Dai, Jian Huang, Yang Xie

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


In breast cancer research, it is of great interest to identify genomic markers associated with prognosis. Multiple gene profiling studies have been conducted for such a purpose. Genomic markers identified from the analysis of single datasets often do not have satisfactory reproducibility. Among the multiple possible reasons, the most important one is the small sample sizes of individual studies. A cost-effective solution is to pool data from multiple comparable studies and conduct integrative analysis. In this study, we collect four breast cancer prognosis studies with gene expression measurements. We describe the relationship between prognosis and gene expressions using the accelerated failure time (AFT) models. We adopt a 2-norm group bridge penalization approach for marker identification. This integrative analysis approach can effectively identify markers with consistent effects across multiple datasets and naturally accommodate the heterogeneity among studies. Statistical and simulation studies demonstrate satisfactory performance of this approach. Breast cancer prognosis markers identified using this approach have sound biological implications and satisfactory prediction performance.

Original languageEnglish (US)
Pages (from-to)2718-2728
Number of pages11
JournalComputational Statistics and Data Analysis
Issue number9
StatePublished - Sep 2012


  • 2-norm group bridge
  • Breast cancer prognosis
  • Gene expression
  • Integrative analysis
  • Marker identification

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


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