基于生物信息学筛选子宫内膜癌预后相关的lncRNAs分子标签

目的：寻找与子宫内膜癌预后相关的lncRNAs分子标签，为预测子宫内膜癌患者的预后及个体化治疗提供有效指导。方法：下载TCGA数据库中的523例子宫内膜癌患者样本，随机分为训练集（n =262）和测试集（n =261）。在训练集中，采用单因素Cox回归结合LASSO回归分析筛选与子宫内膜癌预后相关的lncRNAs分子标签，构建lncRNAs风险评分模型，预测子宫内膜癌预后，并在测试集中验证其预测的有效性。最后，采用基因集富集分析（GSEA）研究lncRNAs风险评分模型预测的高、低风险组之间生物学通路富集的差异。结果：基于LASSO Cox回归分析，一共筛选出13个与子宫内膜癌预后显著相关的差异lncRNAs（P ＜0.001），并以它们作为分子标签构建lncRNAs风险评分模型，将子宫内膜癌患者划分为高风险组和低分险组；生存曲线分析表明，低风险组患者的总生存期在训练集（P ＜0.001）和测试集（P ＜0.001）中均显著优于高风险组。多因素Cox回归分析显示，这13个lncRNAs在训练集（HR =1.08，95%CI：1.06～1.10，P ＜0.001）和测试集（HR =1.54，95%CI：1.34～1.78，P ＜0.001）中均为影响子宫内膜癌预后的独立危险因素。进一步构建lncRNAs分子标签联合临床指标模型并绘制ROC曲线发现，lncRNAs分子标签联合临床指标的模型可进一步提高预测效能。GSEA富集分析表明，细胞周期调控相关的基因集在高风险组中有显著富集，免疫和代谢相关通路则更多地在低风险组富集。结论：本研究确定了与子宫内膜癌预后相关的lncRNAs，基于13 个lncRNAs构建的风险评估模型可作为预测子宫内膜癌预后标志物的分子标签。

Bioinformatics-based prognostic lncRNAs signature study in endometrial cancer

Objective: To find prognostic lncRNA signature of endometrial cancer, and to provide useful guidance for predicting the prognosis and individualized therapy of patients with endometrial cancer. Methods: A total of 523 endometrial cancer tissues were downloaded from the TCGA database and then randomly randomly assigned to a training set (n=262) and a testing set (n=261). Univariate Cox regression and LASSO regression analysis were employed to screen prognosis-related lncRNA signature in the training set, and lncRNA risk score models were constructed to predict the prognosis of endometrial cancer, which was validated in the testing set (n=261). Finally, gene set enrichment analysis (GSEA) was used to discover the differences in biological pathways between the high and low risk groups predicted by the lncRNA signature model. Results: Based on LASSO Cox regression analysis, 13 differential lncRNAs (P<0.001) were significantly associated with the prognosis of endometrial cancer, and lncRNA risk score models were constructed to divide patients with endometrial cancer as high risk group and low risk group; survival curve analysis showed that the overall survival of patients in the low risk group was significantly better than that in the high risk group in the training set (P<0.001) and the testing set (P<0.001), respectively. Multivariate Cox regression analysis showed that the 13 lncRNAs in both the training set (HR: 1.08, 95%CI: 1.06-1.10, P<0.001) and the testing set (HR: 1.54, 95%CI: 1.34-1.78, P<0.001) were independent risk factors affecting the prognosis of endometrial cancer. Moreover, a combined model oflncRNAs signatureand clinical features was constructed. ROC curve demonstrated that the combinedmodel could improve the prediction efficiency. GSEA enrichment analysis revealed that cell cycle regulation-related genes were significantly enriched in the high-risk group, while immune and metabolic-related pathways were enriched in the low-risk group. Conclusion: This study has identified prognostic lncRNAs signature of endometrial cancer. A risk assessment model based on 13 lncRNAs could be viewed as a potential biomarker for predicting the prognosis of endometrial cancer.