Yin Wen, Tang Guihua, Zhou Quanwei, Cao Yudong, Li Haixia, Fu Xianyong, Wu Zhaoping, Jiang Xingjun
Department of Neurosurgery, Xiangya Hospital of Central South University, Changsha, China.
Department of Clinical Laboratory, Hunan Provincial People's Hospital (First Affiliated Hospital of Hunan Normal University), Changsha, China.
Front Genet. 2019 May 3;10:419. doi: 10.3389/fgene.2019.00419. eCollection 2019.
Glioblastoma multiforme (GBM) is the most aggressive primary central nervous system malignant tumor. The median survival of GBM patients is 12-15 months, and the 5 years survival rate is less than 5%. More novel molecular biomarkers are still urgently required to elucidate the mechanisms or improve the prognosis of GBM. This study aimed to explore novel biomarkers for GBM prognosis prediction. The gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets of GBM were downloaded. A total of 2241 overlapping differentially expressed genes (DEGs) were identified from TCGA and GSE7696 datasets. By univariate COX regression survival analysis, 292 survival-related genes were found among these DEGs ( < 0.05). Functional enrichment analysis was performed based on these survival-related genes. A five-gene signature (PTPRN, RGS14, G6PC3, IGFBP2, and TIMP4) was further selected by multivariable Cox regression analysis and a prognostic model of this five-gene signature was constructed. Based on this risk score system, patients in the high-risk group had significantly poorer survival results than those in the low-risk group. Moreover, with the assistance of GEPIA http://gepia.cancer-pku.cn/, all five genes were found to be differentially expressed in GBM tissues compared with normal brain tissues. Furthermore, the co-expression network of the five genes was constructed based on weighted gene co-expression network analysis (WGCNA). Finally, this five-gene signature was further validated in other datasets. In conclusion, our study identified five novel biomarkers that have potential in the prognosis prediction of GBM.
多形性胶质母细胞瘤（GBM）是最具侵袭性的原发性中枢神经系统恶性肿瘤。GBM患者的中位生存期为12 - 15个月，5年生存率低于5%。仍迫切需要更多新的分子生物标志物来阐明GBM的发病机制或改善其预后。本研究旨在探索用于GBM预后预测的新生物标志物。下载了来自癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）中GBM的基因表达谱。从TCGA和GSE7696数据集中共鉴定出2241个重叠的差异表达基因（DEG）。通过单变量COX回归生存分析，在这些DEG中发现了292个与生存相关的基因（<0.05）。基于这些与生存相关的基因进行了功能富集分析。通过多变量Cox回归分析进一步筛选出一个五基因特征（PTPRN、RGS14、G6PC3、IGFBP2和TIMP4），并构建了该五基因特征的预后模型。基于这个风险评分系统，高风险组患者的生存结果明显比低风险组患者差。此外，借助GEPIA（http://gepia.cancer-pku.cn/）发现，与正常脑组织相比，所有这五个基因在GBM组织中均有差异表达。此外，基于加权基因共表达网络分析（WGCNA）构建了这五个基因的共表达网络。最后，在其他数据集中进一步验证了这个五基因特征。总之，我们的研究鉴定出了五个在GBM预后预测中具有潜力的新生物标志物。
