Risk prediction for late-stage ovarian cancer by meta-analysis of 1525 patient samples.

ABSTRACT:

Background

Ovarian cancer causes more than 15000 deaths per year in the United States. The survival of patients is quite heterogeneous, and accurate prognostic tools would help with the clinical management of these patients.

Methods

We developed and validated two gene expression signatures, the first for predicting survival in advanced-stage, serous ovarian cancer and the second for predicting debulking status. We integrated 13 publicly available datasets totaling 1525 subjects. We trained prediction models using a meta-analysis variation on the compound covariable method, tested models by a "leave-one-dataset-out" procedure, and validated models in additional independent datasets. Selected genes from the debulking signature were validated by immunohistochemistry and quantit

SUBMITTER: Riester M 

PROVIDER: S-EPMC4580556 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature