基于生物信息学的子宫内膜癌预后模型构建

目的 筛选与子宫内膜癌（endometrial carcinoma，EC）预后相关的差异基因并构建预后模型。方法 从癌症基因图谱（The Cancer Genome Atlas，TCGA）数据库和基因表达谱数据库（Gene Expression Omnibus，GEO）的数据集GSE63678中下载EC和正常对照样本的基因表达数据，筛选出共有差异基因。采用LASSO回归分析筛选出具有预后意义的基因，并构建预后特征。从TCGA数据库中获取具有完整信息的EC患者，按1∶1的比例随机分为训练组和验证组。对训练组患者基于预后特征构建生存曲线；采用基因本体论（gene ontology，GO）分析和京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes，KEGG）对预后特征进行功能注释和通路富集分析；结合预后特征及临床危险因素构建列线图，采用校准曲线和C指数评估列线图性能。最后使用验证组加以验证。结果 从TCGA和GEO数据库分别筛选出4800个和257个差异基因，其中共表达的上调基因73个，下调基因52个；LASSO回归分析筛选出6个预后基因，分别为ORMDL2、BNC2、TTK、MAMLD1、KCTD7、DCLK2；生存曲线结果表明高风险组患者的总生存率显著低于低风险组（P<0.01）；GO分析和KEGG结果显示预后特征与细胞周期相关。列线图在训练组与验证组中均显示出良好的预测能力。结论 本研究构建一种基于预后特征的预测模型，可准确预测EC患者的预后，为临床诊疗提供新的理论支持。

Construction of prognostic model for endometrial carcinoma based on bioinformatics

Objective Differential genes related to prognosis of endometrial carcinoma (EC) were screened and prognostic models were constructed. Methods Gene Expression data of EC and normal control samples were downloaded from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) dataset GSE63678 to screen out common differential genes. LASSO regression analysis was used to screen out the genes with prognostic significance and construct prognostic characteristics. EC patients with complete information were obtained from the TCGA database and randomly divided into the training group and the validation group in a ratio of 1:1. In the training group, survival curves were constructed based on prognostic characteristics. Functional annotation and pathway enrichment analysis were conducted using gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Combined with prognostic features and clinical risk factors, a calibration curve and C-index were used to evaluate the performance of the histogram. Finally, use a verification group for validation. Results A total of 4800 and 257 differentially expressed genes were screened from TCGA and GEO databases respectively, of which 73 up-regulated genes and 52 down-regulated genes were co-expressed. 6 prognostic genes (ORMDL2, BNC2, TTK, MAMLD1, KCTD7 and DCLK2) were screened out by LASSO regression analysis. The survival curve showed that the overall survival of patients in the high-risk group was significantly lower than that in the low-risk group (P<0.01). GO analysis and KEGG results exhibited that prognostic signature was associated with cell cycle. The nomogram showed powerful predictive ability in the training and validation groups. Conclusion We constructed a predictive model based on prognostic genes, which can accurately predict the prognosis of patients with EC and provide new theoretical support for clinical diagnosis and treatment.