An investigation of clinical outcome prediction from integrative genomic profiles in ovarian cancer

Integrative clinical outcome prediction models that combines gene expression and methylation profiles are investigated in this paper in order to reveal genomic features and models that bear important prognostic value. The models all include the integration and feature selection steps. In the integration step, a method to combine gene expression and methylation profiles is introduced. In the feature selection step, several approaches were investigated including the supervised principal component and the elastic net method to identify genes, whose expression or associate CpG methylation contribute to the clinical outcome. A set of 87 ovarian cancer patients was used in this study to evaluate the proposed methods. The test results showed that the integrative methods improved the prediction performance over those based on gene expression alone.