Identification of prognostic gene signatures of glioblastoma: a study based on TCGA data analysis
Identification of prognostic gene signatures of glioblastoma: a study based on TCGA data analysis
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DOI：
10.1093/neuonc/not024
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发表时间：
2013-07-01
期刊：
影响因子：
15.9
通讯作者：
Yung, W. K. Alfred
中科院分区：
文献类型：
--
作者：
Kim, Yong-Wan;Koul, Dimpy;Yung, W. K. Alfred
背景癌症基因组图谱（TCGA）项目是一个大规模的努力，其目标是确定胶质母细胞瘤（GBM）中的新型分子畸变。方法.在这里，我们描述了基因表达数据和拷贝数畸变（CNA）数据的深入分析，将GBM分类为预后组，以确定可能具有生物学意义的亚型的相关性。结果为了确定预测生存模型，我们在173例患者中搜索了TCGA，并确定了42个探针集（P = .0005），可用于将肿瘤样本分为3组，并显示出显着（P = .0006）改善的总生存期。Kaplan-Meier曲线显示，第3组的中位生存期（127周）明显长于第1组和第2组（分别为47周和52周）。然后，我们验证了42个探针集，以根据其他公共GBM基因表达数据集（例如，GSE 4290数据集）中的生存率对患者进行分层。使用多变量考克斯回归模型对基因表达和拷贝数畸变的总体分析显示，42个探针组具有独立于其他变量的显著（P <0.018）预后价值。结论.通过整合来自TCGA的多维基因组数据，我们在GBM的一个新的预后组中确定了一个特定的生存模型，并建议将GBM患者分子分层为同质亚组可能为开发新的治疗方式提供机会。
Background. The Cancer Genome Atlas (TCGA) project is a large-scale effort with the goal of identifying novel molecular aberrations in glioblastoma (GBM). Methods. Here, we describe an in-depth analysis of gene expression data and copy number aberration (CNA) data to classify GBMs into prognostic groups to determine correlates of subtypes that may be biologically significant. Results. To identify predictive survival models, we searched TCGA in 173 patients and identified 42 probe sets (P = .0005) that could be used to divide the tumor samples into 3 groups and showed a significantly (P = .0006) improved overall survival. Kaplan-Meier plots showed that the median survival of group 3 was markedly longer (127 weeks) than that of groups 1 and 2 (47 and 52 weeks, respectively). We then validated the 42 probe sets to stratify the patients according to survival in other public GBM gene expression datasets (eg, GSE4290 dataset). An overall analysis of the gene expression and copy number aberration using a multivariate Cox regression model showed that the 42 probe sets had a significant (P < .018) prognostic value independent of other variables. Conclusions. By integrating multidimensional genomic data from TCGA, we identified a specific survival model in a new prognostic group of GBM and suggest that molecular stratification of patients with GBM into homogeneous subgroups may provide opportunities for the development of new treatment modalities.