Department of Neurosurgery, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China.
Department of Biological Chemistry, Changzhi Medical College, Changzhi, Shanxi, China.
J Cell Biochem. 2019 Sep;120(9):16037-16043. doi: 10.1002/jcb.28883. Epub 2019 May 13.
To identify independently prognostic gene panel in patients with glioblastoma (GBM).
The Cancer Genome Atlas (TCGA)-GBM was used as a training set and a test set. GSE13041 was used as a validation set. Survival associated differentially expression genes (DEGs), derived between GBM and normal brain tissue, was obtained using univariate Cox proportional hazards regression model and then was included in a least absolute shrinkage and selection operator penalized Cox proportional hazards regression model. Thus, a 4-gene prognostic panel was developed based on the risk score for each patient in that model. The prognostic role of the 4-gene panel was validated using univariate and multivariable Cox proportional hazards regression model.
A total of 686 patients with GBM were included in our study; 724 DEGs was identified, 133 of which was significantly correlated with the overall survival (OS) of patients with GBM. A 4-gene panel including NMB, RTN1, GPC5, and epithelial membrane protein 3 (EMP3) was developed. Kaplan-Meier survival analysis suggested that patients in the 4-gene panel low risk group had significantly better OS than those in the 4-gene panel high risk group in the training set (hazard ratio [HR] = 0.3826; 95% confidence interval [CI]: 0.2751-0.532; P < 0.0001), test set (HR = 0.718; 95% CI: 0.5282-0.9759; P = 0.033) and the independent validation set (HR = 0.6898; 95% CI: 0.4872-0.9766; P = 0.035). Both univariate and multivariable Cox proportional hazards regression analysis suggested that the 4-gene panel was independent prognostic factor for GBM in the training set.
We developed and validated 4-gene panel that was independently correlated with the survival of patients with GBM.
旨在鉴定胶质母细胞瘤（GBM）患者独立预后基因模块。
利用癌症基因组图谱（TCGA）-GBM 作为训练集和测试集，采用 GSE13041 作为验证集。使用单变量 Cox 比例风险回归模型获得 GBM 与正常脑组织之间差异表达基因（DEGs），然后将其纳入最小绝对收缩和选择算子惩罚 Cox 比例风险回归模型。由此，基于该模型中每个患者的风险评分，开发了一个 4 基因预后模块。采用单变量和多变量 Cox 比例风险回归模型验证 4 基因模块的预后作用。
本研究共纳入 686 例 GBM 患者；鉴定出 724 个 DEGs，其中 133 个与 GBM 患者的总生存期（OS）显著相关。开发了一个包含 NMB、RTN1、GPC5 和上皮膜蛋白 3（EMP3）的 4 基因模块。Kaplan-Meier 生存分析表明，在训练集（风险比 [HR] = 0.3826；95%置信区间 [CI]：0.2751-0.532；P < 0.0001）、测试集（HR = 0.718；95% CI：0.5282-0.9759；P = 0.033）和独立验证集（HR = 0.6898；95% CI：0.4872-0.9766；P = 0.035）中，4 基因模块低风险组患者的 OS 显著优于 4 基因模块高风险组患者。单变量和多变量 Cox 比例风险回归分析均提示，4 基因模块是训练集中 GBM 的独立预后因素。
我们开发并验证了与 GBM 患者生存情况独立相关的 4 基因模块。
