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.
Furthermore, we also developed additional analysis options including the computation of multigenic prognosis predictors and the option of grouping patients based on applied treatment protocols.
We searched Gene Expression Omnibus (GEO; and The Cancer Genome Atlas (TCGA; to identify data sets suitable for the analysis.
We specifically used this tool to evaluate the effect of 37 previously published biomarkers on ovarian cancer prognosis.
With a mortality of 8.4 per 100 000 women, ovarian cancer is the most common cause of death among gynecological malignancies ( with a 5-year survival rate of 10–30%.
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 (=1287).
A Kaplan–Meier survival plot was generated and significance was computed. We used this integrative data analysis tool to validate the prognostic power of 37 biomarkers identified in the literature.
Of these, =0.00017, HR=0.75) were associated with survival.
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.
The server is hosted on Debian Linux ( and is powered by Apache (