Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients

    1. Zoltán Szállási4,5
    1. 1Research Laboratory of Pediatrics and Nephrology, Hungarian Academy of Sciences, Budapest, Hungary
      2Laboratory of Functional Genomics, Institute of Pathology, Charité, Berlin, Germany
      32nd Department of Pathology, Semmelweis University Budapest, Budapest, Hungary
      4Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark
      5Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
    1. (Correspondence should be addressed to B Győrffy who is now at First Department of Pediatrics, Semmelweis University, Bókay János u. 53-54, H-1083 Budapest, Hungary; Email: gyorffy{at}


    The validation of prognostic biomarkers in large independent patient cohorts is a major bottleneck in ovarian cancer research. We implemented an online tool to assess the prognostic value of the expression levels of all microarray-quantified genes in ovarian cancer patients. First, a database was set up using gene expression data and survival information of 1287 ovarian cancer patients downloaded from Gene Expression Omnibus and The Cancer Genome Atlas (Affymetrix HG-U133A, HG-U133A 2.0, and HG-U133 Plus 2.0 microarrays). After quality control and normalization, only probes present on all three Affymetrix platforms were retained (n=22 277). To analyze the prognostic value of the selected gene, we divided the patients into two groups according to various quantile expressions of the gene. These groups were then compared using progression-free survival (n=1090) or overall survival (n=1287). A Kaplan–Meier survival plot was generated and significance was computed. The tool can be accessed online at We used this integrative data analysis tool to validate the prognostic power of 37 biomarkers identified in the literature. Of these, CA125 (MUC16; P=3.7×10−5, hazard ratio (HR)=1.4), CDKN1B (P=5.4×10−5, HR=1.4), KLK6 (P=0.002, HR=0.79), IFNG (P=0.004, HR=0.81), P16 (P=0.02, HR=0.66), and BIRC5 (P=0.00017, HR=0.75) were associated with survival. The combination of several probe sets can further increase prediction efficiency. In summary, we developed a global online biomarker validation platform that mines all available microarray data to assess the prognostic power of 22 277 genes in 1287 ovarian cancer patients. We specifically used this tool to evaluate the effect of 37 previously published biomarkers on ovarian cancer prognosis.

    • Revision received 17 January 2012
    • Accepted 24 January 2012
    • Made available online as an Accepted Preprint 25 January 2012
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