基于细胞周期相关基因的胶质瘤患者预后模型构建与验证

Development and validation of a prognostic model for patients with glioma based on cell cycle-related genes

摘要:

目的 探讨细胞周期相关基因在胶质瘤患者中的表达及预后价值。方法 利用CGGA数据库筛选与胶质瘤患者预后相关的细胞周期基因，并基于CGGA与TCGA中胶质瘤患者的临床数据，通过LASSO回归分析，构建预测患者生存情况的预后模型。根据计算公式，区分高低风险组患者，组间进行GSEA富集分析与ssGSEA免疫微环境分析。结果 筛选到10个与患者预后密切相关的细胞周期基因，LASSO回归分析纳入4个基因[细胞周期蛋白依赖性激酶抑制剂2C（CDKN2C）、姐妹染色单体分离的PTTG1调控因子（PTTG1）、细胞周期蛋白依赖性激酶2（CDK2）、WEE1 G2检查点激酶（WEE1）]构建预后模型，计算公式为：风险值（risk socre）=（0.008）×CDKN2C表达量+（0.022）×PTTG1表达量+（0.031）×CDK2表达量+（0.127）×WEE1表达量。生存分析显示，高风险组患者生存率低于低风险组，ROC曲线表明，模型在CGGA与TCGA队列中，均具有较好的预测能力。GSEA富集分析显示，高风险组富集到多个细胞周期进程相关的信号通路，提示可能参与胶质瘤的恶性进程。免疫微环境分析表明，高风险组患者的免疫细胞浸润与免疫反应激活程度均高于低风险组。结论 基于细胞周期相关基因的预后模型可较好地应用于胶质瘤患者的预后预测，纳入的关键基因可能是胶质瘤治疗的可靠靶点。 [国际神经病学神经外科学杂志, 2023, 50(4): 15-24]

Abstract:

Objective To investigate the expression and prognostic value of cell cycle-related genes in patients with glioma.Methods The CGGA database was used to screen for the cell cycle-genes related to the prognosis of glioma patients， and then based on the clinical data of glioma patients in CGGA and TCGA cohorts， the LASSO regression analysis was used to establish a prognostic model for predicting the survival of patients. The patients were divided into high- and low-risk groups based on the calculation formula， and the GSEA enrichment analysis and the ssGSEA immune microenvironment analysis were performed between two groups.Results Ten cell cycle-related genes that were closely associated with patient prognosis were obtained， among which four genes （CDKN2C， PTTG1， CDK2， and WEE1） were included in the LASSO regression analysis to establish a prognostic model， and the calculation formula was risk score = （0.008）×CDKN2C expression + （0.022）×PTTG1 expression + （0.031）×CDK2 expression + （0.127）×WEE1 expression. The survival analysis showed that the high-risk group had a significantly lower survival rate than the low-risk group， and the ROC curve analysis showed that the model had a good predictive ability in both CGGA and TCGA cohorts. The GSEA enrichment analysis showed that multiple signaling pathways associated with cell cycle progression were enriched in the high-risk group， suggesting that it might be involved in the malignant progression of glioma. The immune microenvironment analysis showed that the high-risk group had significantly higher degrees of immune cell infiltration and immune response activation than the low-risk group.Conclusions The prognostic model based on cell cycle-related genes can be used to predict the prognosis of patients with glioma， and the key genes included in this model may be reliable targets for glioma treatment. ［Journal of International Neurology and Neurosurgery, 2023, 50(4): 15-24］