Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang 310009, P.R. China.
Int J Mol Med. 2018 Apr;41(4):2070-2078. doi: 10.3892/ijmm.2018.3422. Epub 2018 Jan 25.
The present study aimed to explore possible prognostic marker genes in glioblastoma (GBM). Differentially expressed genes (DEGs) were screened by comparing microarray data of tumor and normal tissue samples from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) dataset GSE22866. Subsequently, the prognosis‑associated DEGs were screened via Cox regression analysis, followed by construction of gene/protein/pathway interaction networks of these DEGs by calculating the correlation coefficient between the DEGs. Next, a prognostic prediction system was constructed using Bayes discriminant analysis, which was validated by the microarray data of samples from patients with good and bad prognosis from the TCGA and Chinese Glioma Genome Atlas (CGGA), as well as the GEO dataset. Finally, a co‑expression network of the signature genes in the prediction system was constructed in combination with the significant pathways. A total of 288 overlapping DEGs (false discovery rate <0.5 and |log2 of fold change|>1) were screened, 123 of which were identified to be associated with the prognosis of GBM patients. The co‑expression network of these prognosis‑associated DEGs included 1405 interactions and 112 DEGs, and 6 functional modules were identified in the network. The prognostic prediction system was comprised of 63 signature genes with a specificity value of 0.929 and a sensitivity value of 0.948. GBM samples with good and bad prognosis in the TCGA, CGGA and GEO datasets were distinguishable by these signature genes (P=1.33x10‑6, 1.63x10‑4 and 0.00534, respectively). The co‑expression network of signature genes with significant pathways was comprised of 56 genes and 361 interactions. Protein kinase Cγ (PRKCG), protein kinase Cβ (PRKCB) and calcium/calmodulin‑dependent protein kinase IIα (CAMK2A) were important genes in the network, and based on the expression of these genes, it was possible to distinguish between samples with significantly different survival risks. In the present study, an effective prognostic prediction system for GBM patients was constructed and validated. PRKCG, PRKCB and CAMK2A may be potential prognostic factors for GBM.
本研究旨在探索胶质母细胞瘤（GBM）中可能的预后标志物基因。通过比较来自癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）数据集 GSE22866 的肿瘤和正常组织样本的微阵列数据，筛选差异表达基因（DEGs）。随后，通过 Cox 回归分析筛选与预后相关的 DEGs，然后计算 DEGs 之间的相关系数，构建这些 DEGs 的基因/蛋白/通路相互作用网络。接下来，使用贝叶斯判别分析构建预后预测系统，该系统通过 TCGA 和中国脑胶质瘤基因组图谱（CGGA）以及 GEO 数据集的预后良好和预后不良患者的样本微阵列数据进行验证。最后，结合显著通路构建预测系统中特征基因的共表达网络。共筛选到 288 个重叠 DEGs（假发现率<0.5 和 |log2 倍数变化|>1），其中 123 个与 GBM 患者的预后相关。这些与预后相关的 DEGs 的共表达网络包括 1405 个相互作用和 112 个 DEGs，网络中鉴定出 6 个功能模块。预后预测系统由 63 个特征基因组成，特异性值为 0.929，灵敏度值为 0.948。TCGA、CGGA 和 GEO 数据集的预后良好和预后不良的 GBM 样本可通过这些特征基因区分（P=1.33x10-6、1.63x10-4 和 0.00534，分别）。具有显著通路的特征基因的共表达网络包括 56 个基因和 361 个相互作用。蛋白激酶 Cγ（PRKCG）、蛋白激酶 Cβ（PRKCB）和钙/钙调蛋白依赖性蛋白激酶 IIα（CAMK2A）是网络中的重要基因，基于这些基因的表达，可以区分生存风险显著不同的样本。本研究构建并验证了一种有效的 GBM 患者预后预测系统。PRKCG、PRKCB 和 CAMK2A 可能是 GBM 的潜在预后因素。
