基于衰老相关基因的胶质瘤预后模型  

Construction of Glioma Prognosis Model Based on Aging-Related Genes

作　　者：周宇能 彭肖 周婧 朱颖 李婧 陈孝平 ZHOU Yuneng;PENG Xiao;ZHOU Jing;ZHU Ying;LI Jing;CHEN Xiaoping(School of Environmental Ecology and Biological Engineering,Wuhan Institute of Technology,Wuhan 430205,China)

机构地区：[1]武汉工程大学环境生态与生物工程学院,湖北武汉430205

出　　处：《武汉工程大学学报》2023年第3期305-311,共7页Journal of Wuhan Institute of Technology

基　　金：国家自然科学基金(81172178)。

摘　　要：利用生物信息学方法筛选与胶质瘤预后相关的衰老基因,并进行相关预后风险模型的构建与验证。首先,从中国脑胶质瘤基因组图谱数据库中获取胶质瘤患者的表达谱和临床信息,通过单因素回归、拉索回归、多因素回归的方法构建相关预后模型;其次使用外部数据集进行验证;再使用箱线图表明各临床亚型的风险评分差异;最后使用富集分析(GSEA)探讨可能涉及的机制。结果筛选出4个用于预后的基因。训练集和验证集的生存曲线显示,高风险组的总生存率显著低于低风险组(p<0.001)。受试者工作特征曲线分析结果显示,训练集在1、3和5 a的曲线下面积分别为0.91、0.95和0.95,验证集在1、3和5 a的曲线下面积分别为0.73、0.80和0.80,说明该模型对患者的预后预测具有很强的预测效能。临床亚型箱线图显示多种临床亚型分组的风险得分具有显著差异。GSEA分析富集的通路主要涉及JAK-STAT信号路径、P53信号通路、泛素介导的蛋白水解、长期电位富集等通路。以上结果表明这4个衰老相关基因可能为胶质瘤预后潜在的生物标志物。Aging genes associated with glioma prognosis were screened by bioinformatics methods,and prognostic risk models were constructed and validated.Firstly,the expression profiles and clinical information of glioma patients were obtained from the Chinese glioma genome Atlas database,and the relevant prognostic models were constructed by univariate Cox regression analysis,lasso regression and multifactor Cox regression analysis.Secondly,validation was performed using an external data set.Boxplot plots were then used to show differences in risk scores among clinical subtypes.Finally,gene set enrichment analysis(GSEA)was used to explore the possible mechanisms involved.Results of four prognostic genes were selected.Survival curves in the training and validation sets showed that overall survival in the high-risk group is significantly lower than that in the low-risk group(p<0.001).The analysis results of the subjects'working characteristic curves showed that the areas under the 1-year,3-year and 5-year curves of the training set are 0.91,0.95 and 0.95,respectively,and the areas under the 1-year,3-year and 5-year curves of the verification set are 0.73,0.80 and 0.80,respectively,indicating that the model has a strong predictive power on patient prognosis.Clinical subtype boxplot showed significant differences in risk scores among different clinical subtypes.GSEA enrichment pathways mainly involve JAK-STAT signaling pathway,P53 signaling pathway,ubiquitin-mediated proteolysis,long-term potential enrichment and other pathways.These results suggest that these four aging genes may be potential biomarkers for glioma prognosis.

关 键 词：胶质瘤 预后模型 衰老基因 生物信息学 

分 类 号：R730.7[医药卫生—肿瘤]