Chai Ruichao, Zhang Kenan, Wang Kuanyu, Li Guanzhang, Huang Ruoyu, Zhao Zheng, Liu Yanwei, Chen Jing
Department of Molecular Neuropathology, Beijing Neurosurgical Institute, Capital Medical University, No. 6 Tiantan Xili, Dongcheng District, Beijing, 100050, China.
Chinese Glioma Cooperative Group (CGCG), Beijing, China.
J Cancer Res Clin Oncol. 2018 Mar;144(3):439-447. doi: 10.1007/s00432-017-2572-6. Epub 2018 Jan 3.
Primary glioblastoma (pGBM) is the most common and lethal type of neoplasms in the central nervous system, while the existing biomarkers, lacking consideration on the stemness changes of GBM cells, are not specific enough to predict the complex prognosis respectively. We aimed to build a high-efficiency prediction gene signature related to GBM cell stemness and investigate its prognostic value in primary glioblastoma.
Differentially expressed genes were screened in GSE23806 database. The selected genes were then verified by univariate Cox regression in 591 patients from four enormous independent databases, including the Chinese Glioma Genome Atlas (CGGA), TCGA, REMBRANDT and GSE16011. Finally, the intersected genes were included to build the gene signature. GO analysis and GSEA were carried out to explore the bioinformatic implication.
The novel five-gene signature was used to identify high- and low-risk groups in the four databases, and the high-risk group showed notably poorer prognosis (P < 0.05). Gene ontology (GO) terms including "immune response", "apoptotic process", and "angiogenesis" were picked out by GO analysis and GSEA, which revealed that the gene signature was highly possibly related to the stemness of GSCs and predicting the prognosis of GBM effectively.
We built a gene signature with five glioblastoma stem-like cell (GSC) relevant genes, and predicted the survival in four independent databases effectively, which is possibly related to the stemness of GSCs in pGBM. Several GO terms were investigated to be correlated to the signature. The signature can predict the prognosis of glioblastoma efficiently.
原发性胶质母细胞瘤（pGBM）是中枢神经系统中最常见且致命的肿瘤类型，而现有的生物标志物未考虑胶质母细胞瘤细胞干性的变化，分别预测复杂预后的特异性不足。我们旨在构建一个与胶质母细胞瘤细胞干性相关的高效预测基因特征，并研究其在原发性胶质母细胞瘤中的预后价值。
在GSE23806数据库中筛选差异表达基因。然后，从包括中国胶质瘤基因组图谱（CGGA）、TCGA、REMBRANDT和GSE16011在内的四个大型独立数据库中的591例患者中，通过单变量Cox回归验证所选基因。最后，纳入交集基因构建基因特征。进行基因本体论（GO）分析和基因集富集分析（GSEA）以探索生物信息学意义。
使用新的五基因特征在四个数据库中识别高风险和低风险组，高风险组的预后明显较差（P < 0.05）。通过GO分析和GSEA筛选出包括“免疫反应”、“凋亡过程”和“血管生成”等基因本体论（GO）术语，这表明该基因特征极有可能与胶质母细胞瘤干细胞（GSCs）的干性相关，并有效预测胶质母细胞瘤的预后。
我们构建了一个包含五个与胶质母细胞瘤干细胞样细胞（GSC）相关基因的基因特征，并在四个独立数据库中有效预测了生存率，这可能与pGBM中GSCs的干性有关。研究发现几个GO术语与该特征相关。该特征可以有效预测胶质母细胞瘤的预后。
