title: Meta-analysis of several gene lists for distinct types of cancer: a simple way to reveal common prognostic markers.
abstract: BACKGROUND
  Although prognostic biomarkers specific for particular cancers have been discovered, microarray analysis of gene expression profiles, supported by integrative analysis algorithms, helps to identify common factors in molecular oncology. Similarities of Ordered Gene Lists (SOGL) is a recently proposed approach to meta-analysis suitable for identifying features shared by two data sets. Here we extend the idea of SOGL to the detection of significant prognostic marker genes from microarrays of multiple data sets. Three data sets for leukemia and the other six for different solid tumors are used to demonstrate our method, using established statistical techniques.
  
  
  RESULTS
  We describe a set of significantly similar ordered gene lists, representing outcome comparisons for distinct types of cancer. This kind of similarity could improve the diagnostic accuracies of individual studies when SOGL is incorporated into the support vector machine algorithm. In particular, we investigate the similarities among three ordered gene lists pertaining to mesothelioma survival, prostate recurrence and glioma survival. The similarity-driving genes are related to the outcomes of patients with lung cancer with a hazard ratio of 4.47 (p = 0.035). Many of these genes are involved in breakdown of EMC proteins regulating angiogenesis, and may be used for further research on prognostic markers and molecular targets of gene therapy for cancers.
  
  
  CONCLUSIONS
  The proposed method and its application show the potential of such meta-analyses in clinical studies of gene expression profiles.
authors.affiliation: State Key Laboratory of Bioelectronics, Southeast University, Nanjing, PR China. xnyang@seu.edu.cn
authors.forename: Xinan
authors.initials: X
authors.lastname: Yang
authors.forename: Xiao
authors.initials: X
authors.lastname: Sun
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D030562:Databases, Protein; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D015201:Meta-Analysis as Topic; D009363:Neoplasm Proteins / Q000032:analysis*; D009369:Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D010363:Pattern Recognition, Automated / Q000379:methods; D011379:Prognosis
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins