Background Ovarian cancer (OC) is a highly malignant disease with a poor prognosis and high recurrence rate. At present, there is no accurate strategy to predict the prognosis and recurrence of OC. The aim of this study was to identify gene-based signatures to predict OC prognosis and recurrence. Methods mRNA expression profiles and corresponding clinical information regarding OC were collected from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) and LASSO analysis were performed, and Kaplan–Meier curves, time-dependent ROC curves, and nomograms were constructed using R software and GraphPad Prism7. Results We first identified several key signalling pathways that affected ovarian tumorigenesis by GSEA. We then established a nine-gene-based signature for overall survival (OS) and a five-gene-based-signature for relapse-free survival (RFS) using LASSO Cox regression analysis of the TCGA dataset and validated the prognostic value of these signatures in independent GEO datasets. We also confirmed that these signatures were independent risk factors for OS and RFS by multivariate Cox analysis. Time-dependent ROC analysis showed that the AUC values for OS and RFS were 0.640, 0.663, 0.758, and 0.891, and 0.638, 0.722, 0.813, and 0.972 at 1, 3, 5, and 10 years, respectively. The results of the nomogram analysis demonstrated that combining two signatures with the TNM staging system and tumour status yielded better predictive ability. Conclusion In conclusion, the two-gene-based signatures established in this study may serve as novel and independent prognostic indicators for OS and RFS.

中文翻译：

---

卵巢癌预后和复发的综合通路相关基因特征

背景 卵巢癌（OC）是一种恶性程度高的疾病，预后差，复发率高。目前，还没有准确的策略来预测 OC 的预后和复发。本研究的目的是确定基于基因的特征来预测 OC 的预后和复发。方法从癌症基因组图谱（TCGA）数据库中收集有关 OC 的 mRNA 表达谱和相应的临床信息。进行基因集富集分析（GSEA）和LASSO分析，并使用R软件和GraphPad Prism7构建Kaplan-Meier曲线、时间依赖性ROC曲线和列线图。结果 我们首先通过 GSEA 确定了影响卵巢肿瘤发生的几个关键信号通路。然后，我们使用 TCGA 数据集的 LASSO Cox 回归分析建立了基于 9 基因的总体生存 (OS) 特征和基于 5 基因的无复发生存 (RFS) 特征，并验证了这些特征的预后价值在独立的 GEO 数据集中。我们还通过多变量 Cox 分析证实这些特征是 OS 和 RFS 的独立风险因素。时间依赖性 ROC 分析显示，OS 和 RFS 的 AUC 值在 1、3、5 和 10 年分别为 0.640、0.663、0.758 和 0.891，以及 0.638、0.722、0.813 和 0.972。列线图分析的结果表明，将两个特征与 TNM 分期系统和肿瘤状态相结合可以产生更好的预测能力。结论 总之，