10571 Background: The pre-clinical validation of prognostic gene candidates in large independent patient cohorts is a pre-requisite for the development of robust biomarkers. Today, curated microarray cohorts combined with appropriate clinical data offer a cost effective tool to prescreen potential new biomarkers. In present study we expanded our online Kaplan-Meier plotter tool to assess the effect of genes on ovarian cancer prognosis. Methods: Gene expression data and survival information of breast and ovarian cancer patients were downloaded from GEO and TCGA. To analyze the prognostic value of the selected gene in the various cohorts the patients are divided into two groups according to the quantile expression of the gene. Filtering is implemented for stage, grade, and histology subtypes. Follow-up threshold is implemented to exclude long-term effects. A Kaplan-Meier survival plot is generated and significance is computed in the R statistical environment using Bioconductor packages. The combination of several probe sets can be employed to assess the mean of their expression as a multigene predictor of survival. Results: All together 1,390 ovarian cancer patients and 2,472 breast cancer patients were entered into the database. These groups can be compared using relapse free survival (n=2,324 in breast cancer and 1,090 in ovarian cancer) or overall survival (n=464 and n=1,287). We used this integrative data analysis tool to validate the prognostic power of 37 ovarian cancer associated biomarkers identified in the literature. Of these, CA125 (p=3.7e-5, HR=1.4), CDKN1B (p=5.4e-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. Conclusions: We set up a global online biomarker validation platform to mine all available microarray data to assess the prognostic power of 22,277 genes in 2,472 breast and 1,390 ovarian cancer patients. Registration-free online access at: http://www.kmplot.com/.
Steroid metabolism gene polymorphisms and their implications on breast and ovarian cancer prognosis
