DataSheet_1_Predicting recurrence and metastasis risk of endometrial carcinoma via prognostic signatures identified from multi-omics data.pdf

ObjectivesEndometrial carcinoma (EC) is one of the three major gynecological malignancies, in which 15% - 20% patients will have recurrence and metastasis. Though there are many studies on the prognosis on this cancer, the performances of existing models evaluating the risk of its recurrence and metastasis are yet to be improved. In addition, a comprehensive multi-omics analyses on the prognostic signatures of EC are on demand. In this study, we aimed to construct a relatively stable and reliable model for predicting recurrence and metastasis of EC. This will help determine the risk level of patients and choose appropriate adjuvant therapy, thereby avoiding improper treatment, and improving the prognosis of patients.
MethodsThe mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), copy number variation (CNV) data and clinical information of patients with EC were downloaded from The Cancer Genome Atlas (TCGA). Differential expression analyses were performed between the recurrence or metastasis group and the non-recurrence/metastasis group. Then, we screened potential prognostic markers from the four kinds of omics data respectively and established prediction models using three classifiers.
ResultsWe achieved differential expressed mRNAs, lncRNAs, miRNAs and CNVs between the two groups. According to feature selection scores by the random forest algorithm, 275 CNV features, 50 lncRNA features, 150 miRNA features and 150 mRNA features were selected, respectively. And the prediction model constructed by the features of lncRNA data using random forest method showed the best performance, with an area under the curve of 0.763, and an accuracy of 0.819 under 10-fold cross-validation.
ConclusionWe developed a computational model using omics information, which is able to predicting recurrence and metastasis risk of EC accurately.

研究目的：子宫内膜癌（Endometrial carcinoma, EC）是三大妇科恶性肿瘤之一，约15%~20%的患者会出现复发与转移。尽管目前针对该癌症的预后研究已较为丰富，但现有评估其复发转移风险的模型性能仍有待提升。此外，目前学界对子宫内膜癌预后标志物的综合多组学分析仍存在迫切需求。本研究旨在构建一款相对稳定可靠的子宫内膜癌复发转移预测模型，以辅助临床医师明确患者的风险分层、选择适宜的辅助治疗方案，从而避免不当治疗，改善患者预后。
研究方法：本研究从癌症基因组图谱（The Cancer Genome Atlas, TCGA）下载了子宫内膜癌患者的信使RNA（mRNA）、微小RNA（microRNA, miRNA）、长链非编码RNA（long non-coding RNA, lncRNA）、拷贝数变异（copy number variation, CNV）数据及临床信息。以复发/转移组与非复发/转移组为对照开展差异表达分析，随后分别从四类组学数据中筛选潜在预后标志物，并采用三种分类器构建预测模型。
研究结果：本研究成功获取了两组间的差异表达mRNA、lncRNA、miRNA及CNV。依据随机森林算法的特征选择得分，分别筛选出275个CNV特征、50个lncRNA特征、150个miRNA特征及150个mRNA特征。其中，基于lncRNA数据特征结合随机森林方法构建的预测模型性能最优，在10折交叉验证下的曲线下面积（AUC）达0.763，分类准确率为0.819。
结论：本研究开发了一款基于多组学信息的计算模型，可精准预测子宫内膜癌的复发转移风险。