Liu Lixiao, Cai Luya, Liu Chuan, Yu Shanshan, Li Bingxin, Pan Luyao, Zhao Jinduo, Zhao Ye, Li Wenfeng, Yan Xiaojian
Department of Obstetrics and Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China.
Front Genet. 2020 Nov 12;11:585259. doi: 10.3389/fgene.2020.585259. eCollection 2020.
Among all fatal gynecological malignant tumors, ovarian cancer has the highest mortality rate. The purpose of this study was to develop a stable and personalized glycometabolism-related prognostic signature to predict the overall survival of ovarian cancer patients. The gene expression profiles and clinical information of ovarian cancer patients were derived from four public GEO datasets, which were divided into training and testing cohorts. Glycometabolism-related genes significantly associated with prognosis were selected. A risk score model was established and validated to evaluate its predictive value. We found 5 genes significantly related to prognosis and established a five-mRNA signature. The five-mRNA signature significantly divided patients into a low-risk group and a high-risk group in the training set and validation set. Survival analysis showed that high risk scores obtained by the model were significantly correlated with adverse survival outcomes and could be regarded as an independent predictor for patients with ovarian cancer. In addition, the five-mRNA signature can predict the overall survival of ovarian cancer patients in different subgroups. In summary, we successfully constructed a model that can predict the prognosis of patients with ovarian cancer, which provides new insights into postoperative treatment strategies, promotes individualized therapy, and provides potential new targets for immunotherapy.
在所有致命的妇科恶性肿瘤中，卵巢癌的死亡率最高。本研究的目的是开发一种稳定且个性化的糖代谢相关预后特征，以预测卵巢癌患者的总生存期。卵巢癌患者的基因表达谱和临床信息来自四个公共基因表达综合数据库（GEO）数据集，并分为训练队列和测试队列。选择与预后显著相关的糖代谢相关基因。建立并验证了一个风险评分模型，以评估其预测价值。我们发现5个与预后显著相关的基因，并建立了一个五信使核糖核酸（mRNA）特征。在训练集和验证集中，该五mRNA特征将患者显著分为低风险组和高风险组。生存分析表明，该模型获得的高风险评分与不良生存结果显著相关，可被视为卵巢癌患者的独立预测指标。此外，该五mRNA特征可预测不同亚组卵巢癌患者的总生存期。总之，我们成功构建了一个能够预测卵巢癌患者预后的模型，这为术后治疗策略提供了新的见解，促进了个体化治疗，并为免疫治疗提供了潜在的新靶点。
