DDIT4 and ovarian cancer: As a result, 11 of the 39 recurrence genes were identified from the 524 ovarian cancer samples as the training samples, which included BIRC3, CDH2, CDH6, DDIT4, GAS1, IFIT1, IGF2, ISLR, MUC16, RSAD2, and DIRAS3. Considering the lasso coefficient of these genes, we further constructed a prognostic model named RMGS, recurrence marker gene signature.