Construction and validation of a robust epigenetic gene-set based signature in ovarian cancer

Abstract Background Epigenetic factors play a critical role in tumor development and progression. The aim of this study was to construct and validate a robust epigenetic gene-set based signature for predicting prognosis of ovarian cancer (OC). Methods Public microarray data of OC patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were identified and patients from TCGA dataset were randomized 3:1 into discovery and internal validation series. GSE14764 and GSE26712 from GEO database were combined as the external validation set. LASSO Cox regression model was performed in the discovery set to filter the most useful prognostic epigenetic factors. Results Based on LASSO Cox regression model, we built a 26 epigenetic factors based prognostic signature. In the discovery set, patients in high risk group showed significantly poorer overall survival than that patients in low risk group (HR: 2.11, 95% CI: 1.65–2.72, P < 0.001). The results were further validated in the internal validation set (HR: 1.69, 95% CI: 1.07–2.63, P = 0.020) and external validation set (HR 1.95, 95% CI 1.41–2.69; p < 0.001). Survival ROC at 5 year suggested that the epigenetic signature (AUC = 0.700) had better prognostic accuracy than any other clinicopathological factors in the entire cohort. In addition, survival decision curve analysis unveiled a considerable value of clinical utility of the epigenetic signature. Conclusions We successfully developed a robust epigenetic signature that can accurately predict prognosis in OC.