A 63 signature genes prediction system is effective for glioblastoma prognosis.
A 63 signature genes prediction system is effective for glioblastoma prognosis.
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DOI：
10.3892/ijmm.2018.3422
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发表时间：
2018-04
影响因子：
5.4
通讯作者：
Zhu X
中科院分区：
文献类型：
--
作者：
Zhang Y;Xu J;Zhu X
本研究旨在探索胶质母细胞瘤（GBM）中可能的预后标记基因。通过比较来自癌症基因组图谱 (TCGA) 和基因表达综合 (GEO) 数据集 GSE22866 的肿瘤和正常组织样本的微阵列数据来筛选差异表达基因 (DEG)。随后，通过Cox回归分析筛选与预后相关的DEG，然后通过计算DEG之间的相关系数构建这些DEG的基因/蛋白质/通路相互作用网络。接下来，利用贝叶斯判别分析构建了预后预测系统，并通过TCGA和中国胶质瘤基因组图谱（CGGA）以及GEO数据集的预后良好和不良患者样本的微阵列数据进行了验证。最后结合显着通路构建了预测系统中特征基因的共表达网络。总共筛选了288个重叠DEG（错误发现率<0.5和|log2倍数变化|>1），其中123个被确定与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，并且在网络中确定了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，敏感性值为 0.948。 TCGA、CGGA 和 GEO 数据集中预后良好和不良的 GBM 样本可通过这些特征基因进行区分（P 分别为 1.33×10−6、1.63×10−4 和 0.00534）。具有重要途径的特征基因的共表达网络由 56 个基因和 361 个相互作用组成。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，根据这些基因的表达，可以区分具有显着不同生存风险的样本。在本研究中，构建并验证了 GBM 患者的有效预后预测系统。 PRKCG、PRKCB 和 CAMK2A 可能是 GBM 的潜在预后因素。
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=1.33×10−6, 1.63×10−4 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.
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DOI：
10.1158/1055-9965.epi-14-0275
发表时间：
2014-10
期刊：
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
影响因子：
--
作者：
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通讯作者：
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影响因子：
168.9
作者：
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影响因子：
5.5
作者：
Bao, Zhao-Shi;Li, Ming-Yang;Jiang, Tao
通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
Bononi A;Agnoletto C;De Marchi E;Marchi S;Patergnani S;Bonora M;Giorgi C;Missiroli S;Poletti F;Rimessi A;Pinton P
通讯作者：
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