Systematic identification of transcription factors associated with patient survival in cancers
1 Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
2 Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
3 Department of Mathematics, University of Southern California, Los Angeles, CA 90089, USA
4 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
5 Department of Computer Science, Yale University, New Haven, CT, USA
BMC Genomics 2009, 10:225 doi:10.1186/1471-2164-10-225Published: 15 May 2009
Aberrant activation or expression of transcription factors has been implicated in the tumorigenesis of various types of cancer. In spite of the prevalent application of microarray experiments for profiling gene expression in cancer samples, they provide limited information regarding the activities of transcription factors. However, the association between transcription factors and cancers is largely dependent on the transcription regulatory activities rather than mRNA expression levels.
In this paper, we propose a computational approach that integrates microarray expression data with the transcription factor binding site information to systematically identify transcription factors associated with patient survival given a specific cancer type. This approach was applied to two gene expression data sets for breast cancer and acute myeloid leukemia. We found that two transcription factor families, the steroid nuclear receptor family and the ATF/CREB family, are significantly correlated with the survival of patients with breast cancer; and that a transcription factor named T-cell acute lymphocytic leukemia 1 is significantly correlated with acute myeloid leukemia patient survival.
Our analysis identifies transcription factors associating with patient survival and provides insight into the regulatory mechanism underlying the breast cancer and leukemia. The transcription factors identified by our method are biologically meaningful and consistent with prior knowledge. As an insightful tool, this approach can also be applied to other microarray cancer data sets to help researchers better understand the intricate relationship between transcription factors and diseases.