title: Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  leixu@jhu.edu.
authors.affiliation: The Whitaker Biomedical Engineering Institute, The Johns Hopkins University, Baltimore, MD 21218, USA. leixu@jhu.edu
authors.forename: Lei
authors.initials: L
authors.lastname: Xu
authors.forename: Aik Choon
authors.initials: AC
authors.lastname: Tan
authors.forename: Daniel Q
authors.initials: DQ
authors.lastname: Naiman
authors.forename: Donald
authors.initials: D
authors.lastname: Geman
authors.forename: Raimond L
authors.initials: RL
authors.lastname: Winslow
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000378:metabolism*; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008297:Male; D009363:Neoplasm Proteins / Q000378:metabolism*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018511:Systems Integration
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins