Ovarian cancer is one of the leading causes of cancer-related deaths in gynecologic malignancies. Over 70 % of ovarian cancer cases are high-grade serous ovarian cancers (HGSC) and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good and accurate prediction of prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve patient's prognosis through proper treatment, we present a prognostic prediction model by integrating the high dimensional RNA sequencing data with their clinical data through the following steps: (1) gene filtration, (2) pre-screening, (3) gene marker selection (4) integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.
Keywords: Ovarian Cancer; Penalized Cox regression; Prediction model; RNA-sequencing data.