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Bioinformatics Advance Access originally published online on August 30, 2005
Bioinformatics 2005 21(20):3905-3911; doi:10.1093/bioinformatics/bti647
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org

Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data

Lei Xu 1,2,*, Aik Choon Tan 1,2, Daniel Q. Naiman 1,2,3, Donald Geman 1,2,3,4 and Raimond L. Winslow 1,2

1The Whitaker Biomedical Engineering Institute, The Johns Hopkins University Baltimore MD 21218, USA
2Center for Cardiovascular Bioinformatics and Modeling, The Johns Hopkins University Baltimore MD 21218, USA
3Department of Applied Mathematics and Statistics, The Johns Hopkins University Baltimore MD 21218, USA
4Center for Imaging Sciences, The Johns Hopkins University Baltimore MD 21218, USA

*To whom correspondence should be addressed.

Motivation: DNA microarray data analysis has been used previously to identify marker genes which discriminate cancer from normal samples. However, due to the limited sample size of each study, there are few common markers among different studies of the same cancer. With the rapid accumulation of microarray data, it is of great interest to integrate inter-study microarray data to increase sample size, which could lead to the discovery of more reliable markers.

Results: We present a novel, simple method of integrating different microarray datasets to identify marker genes and apply the method to prostate cancer datasets. In this study, by applying a new statistical method, referred to as the top-scoring pair (TSP) classifier, we have identified a pair of robust marker genes (HPN and STAT6) by integrating microarray datasets from three different prostate cancer studies. Cross-platform validation shows that the TSP classifier built from the marker gene pair, which simply compares relative expression values, achieves high accuracy, sensitivity and specificity on independent datasets generated using various array platforms. Our findings suggest a new model for the discovery of marker genes from accumulated microarray data and demonstrate how the great wealth of microarray data can be exploited to increase the power of statistical analysis.

Contact: leixu{at}jhu.edu

Received on May 19, 2005; revised on June 21, 2005; accepted on August 25, 2005

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