目的 应用生物信息学工具探索糖酵解相关基因(glycolysis-related genes,GRGs)在胃癌中的表达及基于其所构建的风险评分模型与胃癌患者预后的关系.方法 从癌症基因组图谱(the cancer genome atlas,TC-GA)数据库下载胃癌组织转录组和临床特征数据.从基因集富集分析(gene set enrichment analysis,GSEA)数据库中获得GRGs集.使用limma包来识别胃癌组织中差异表达的GRGs.使用单因素Cox回归分析筛选预后相关的GRGs,应用LASSO回归分析构建基于GRGs的预后预测模型.根据Cox回归分析确定的独立预后危险因素构建Nomogram图.最后,分析22种浸润性免疫细胞与GRGs、风险评分模型相关性.结果 鉴定出21个差异表达的GRGs,其主要富集在酒精代谢过程和酪氨酸代谢途径.利用LASSO和Cox模型筛选出5个预后相关的糖酵解基因(ADH1B、ADH4、CLDN9、VCAN及LHX90),构建预测胃癌预后风险评分模型.低风险评分的胃癌患者总生存时间明显优于高风险评分的胃癌患者,ROC曲线分析显示该模型预测胃癌患者1年、3年及5年生存期曲线下面积(area under curve,AUC)值分别为0.602、0.680和0.802,发现该模型预测患者生存期的效能优于肿瘤分期及分级.单因素和多因素Cox分析显示构建的模型、患者的年龄、性别及肿瘤分期、分级是胃癌患者独立预后因素.基于独立危险因素构建了用来预测胃癌患者生存Nomogram图.最后,发现CD8 T细胞和辅助性滤泡T细胞比例在高危组中明显降低,而M0型和M2型巨噬细胞比例在高危组中明显高于低危组.辅助性滤泡T细胞与ADH1B表达水平、VCAN表达水平和风险评分呈负相关.M2型巨噬细胞与VCAN表达水平和风险评分呈正相关.结论 研究筛选出胃癌预后相关的5个GRGs构建的模型可作为评估胃癌患者预后的可靠工具,为胃癌提供潜在的糖酵解治疗靶点.
Identification of Differentially Expressed Glycolysis-related Genes and Establishment of Prognostic Model for Gastric Cancer by Bioinforma-tics
Objective To explore the expression of glycolysis-related genes(GRGs)in gas-tric cancer by bioinformatics,and the relationship between the established risk scoring model and prognosis of gastric cancer.Methods The genes expression profiles and clinical feature data of gas-tric cancer samples were downloaded from the Cancer Genome Atlas(TCGA)database.The GRGs set was obtained from the GSEA database.We used"limma"packets to identify differentially ex-pressed GRGs in gastric cancer tissues,and used univariate Cox regression analysis to screen for prognosis related GRGs.Then,LASSO regression analysis was used to construct a prognosis predic-tion model based on the GRGs.A nomogram was developed based on the independent prognostic risk factors determined by Cox regression analysis.Finally,the correlations between 22 types of infiltra-ting immune cells and the GRGs or risk scoring models were analyzed.Results Twenty-one differ-entially expressed GRGs were identified,which were mainly enriched in the alcohol metabolism and tyrosine metabolism pathways.Finally,5 prognostic related glycolytic genes(ADH1 B,ADH4,CLDN9,VCAN,and LHX90)were selected by LASSO and Cox models and were used to construct a gene signature for gastric cancer prognosis prediction.The overall survival of gastric cancer patients with low-risk scores is significantly better than that of gastric cancer patients with high-risk scores.The ROC curve analysis showed that the values of area under the curve(AUC)of the above model to predict the 1-year,3-year,and 5-year survival for the gastric cancer patients were 0.602,0.680,and 0.802,respectively.The effectiveness of this model to predict the survival of gastric cancer patients was better than that of using tumor staging and grading.Univariate and multivariate Cox analysis showed that the prognostic model,age,gender,tumor stage and tumor grade were inde-pendent prognostic factors for gastric cancer.Based on these prognostic factors,a Nomogram was con-structed to predict the survival of gastric cancer patients.Finally,we found that the proportion of CD8 T cells and helper follicular T cells was significantly reduced in the high-risk group,while the propor-tion of M0 macrophages and M2 macrophages was significantly higher in the high-risk group than in the low-risk group.The proportion of helper follicular T cells was negatively correlated with the expres-sion levels of ADH1B and VCAN and the risk scores.The proportion of M2 macrophages was positively correlated with the expression level of VCAN and the risk scores.Conclusion The 5 GRGs screened out in this study are related to the prognosis of gastric cancer,which can be used for the prognosis of gastric cancer patients and may be used as potential therapeutic targets for gastric cancer.
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