基于单细胞测序构建胶质母细胞瘤预后模型

Establishing prognostic model of glioblastoma based on single cell sequencing

[目的]基于单细胞测序筛选胶质母细胞瘤特征基因并构建预后模型。[方法]分析GEO数据库单细胞RNA测序数据集GSE84465,筛选出GBM细胞分化相关的差异基因。下载TCGA数据库GBM的基因表达谱和临床数据,采用Lasso回归、Cox回归分析筛选出特征基因构建预后模型,根据独立预后因素构建列线图,GSE83300作为外部验证集。基于风险评分中位数将患者分组,比较两组生存差异。[结果]通过scRNA-seq得到492个分化差异基因,经过回归分析得到基于6个基因(PLAUR、RARRES2、G0S2、MDK、SERPINE2、CD81)的预后模型。其1、3、5年ROC曲线下面积均大于0.7;KM分析显示高低风险组预后存在差异(P<0.001),GSE83300验证结果与TCGA一致。多因素Cox回归分析表明年龄和风险评分可以作为独立影响因素(P<0.01);C-Index(0.679)、校准图显示列线图预测模型有良好的拟合度。GSEA分析示高低风险组差异基因集参与细胞因子受体相互作用、抗原处理与提呈等通路。[结论]由PLAUR、RARRES2、G0S2、MDK、SERPINE2、CD81构建的模型能够预测GBM患者预后。

[Objective]To screen feature genes of glioblastoma and construct a prognostic model based on single-cell sequencing.[Method]ScRNA-seq data GSE84465 from GEO database was analysed to identify the differential genes related to GBM cells differentiation.The gene expression and clinical data of GBM were downloaded in TCGA database, then Lasso regression, Cox regression analysis were used to obtain the feature genes to constructe prognostic model, then according to independently prognostic factors nomogram was constructed, GSE83300 as external validation set.The patients were grouped based to the median risk score with comparing survival difference.[Result]400 differential genes of differentiation were screened by scRNA-seq, and after regression analysis the prognostic model about 6 genes(PLAUR,RARRES2,G0S2,MDK,SERPINE2,CD81)was obtained.The areas under ROC curve in 1,3,5 years were greater than 0.7;KM analysis showed the prognosis was different between high risk group and low risk group(P<0.001);the result about validation of GSE83300 was consistent with TCGA.Multivariate Cox analysis found age and risk score could be used as independently prognostic factors(P<0.01).C-index(0.679),calibration plot showed the nomogram about prediction of the prognosis had good fitting.GSEA showed that the differential gene sets between two group were related to cytokine receptor interaction, antigen treatment and presentation and others.[Conclusion] The model about six genes(PLAUR,RARRES2,G0S2,MDK,SERPINE2,CD81) can predict effectively the prognosis of GBM.