摘要
目的探讨长链非编码RNA(lncRNA)对胶质母细胞瘤(GBM)患者预后的影响。方法利用肿瘤基因组图谱数据库下载并提取lncRNA表达矩阵和临床资料,筛选差异表达lncRNA,采用单因素Cox、Lasso和多因素Cox回归筛选预后相关lncRNA,建立GBM患者预后相关的2种lncRNA风险评分模型进行比较,并对重要的lncRNA进行预后的验证。结果通过差异基因分析(logFC≥2或≤-2,P<0.01)得到1255个差异lncRNA。单因素Cox、Lasso回归分析筛选出23个lncRNA,多因素Cox回归分析得到5个lncRNA。分别用23个和5个lncRNA建立风险评测模型,2种模型3 a生存率曲线下面积(AUC)分别为0.955和0.890,5 a生存率AUC分别为0.961和0.849。对5种lncRNA预后进行验证,AC066612.2和胶质母细胞瘤预后相关性最强。结论2种风险预测模型均可有效预测胶质母细胞瘤患者的预后,可以用于指导临床治疗。
Objective To investigate the effect of long non-coding RNA(lncRNA)on the prognosis of glioblastoma patients.Methods LncRNA expression matrix and clinical data were downloaded and extracted from The Cancer Genome Atlas database to screen differentially expressed lncRNA.Univariate Cox,Lasso and multivariate Cox regression analysis were used to screen prognostic lncRNA,and two lncRNA risk score models related to prognosis of glioblastoma patients were established for comparison,and important lncRNA were verified for prognosis.Results The 1255 differentially expressed lncRNA were obtained by differential gene analysis(logFC≥2 or≤-2,P<0.01).Univariate Cox analysis and Lasso regression analysis screened out 23 kinds of lncRNA,while multivariate Cox regression analysis obtained 5 kinds of lncRNA.Risk evaluation models were established by using 23 and 5 kinds of lncRNA,respectively.The area under curve(AUC)of the 3-year survival rate curves of the two models were respectively 0.955 and 0.890,and those of the 5-year survival rates curves were respectively 0.961 and 0.849.The prognosis of 5 kinds of lncRNA was verified,and the correlation between AC066612.2 and prognosis of glioblastoma was the strongest.Conclusion Both risk prediction models can effectively predict the prognosis of GBM patients and can be used to guide clinical treatment.
作者
杜宝顺
王运刚
尚飞
张哲莹
DU Baoshun;WANG Yungang;SHANG Fei;ZHANG Zheying(Department of Neurosurgery,Xinxiang Central Hospital,Xinxiang 453000,China;Department of Pathology,Xinxiang Medical College,Xinxiang 453003,China)
出处
《肿瘤基础与临床》
2021年第4期281-285,共5页
journal of basic and clinical oncology
基金
国家自然科学基金资助项目(81802470)
河南省科技攻关项目(192102310362)。
