Huang Yen-Tsung, Hsu Thomas, Kelsey Karl T, Lin Chien-Ling
Department of Epidemiology, Brown University, Providence, Rhode Island, United States of America.
Genet Epidemiol. 2015 Feb;39(2):134-43. doi: 10.1002/gepi.21875. Epub 2014 Dec 23.
Glioblastoma multiforme (GBM), the most common type of malignant brain tumor, is highly fatal. Limited understanding of its rapid progression necessitates additional approaches that integrate what is known about the genomics of this cancer. Using a discovery set (n = 348) and a validation set (n = 174) of GBM patients, we performed genome-wide analyses that integrated mRNA and micro-RNA expression data from GBM as well as associated survival information, assessing coordinated variability in each as this reflects their known mechanistic functions. Cox proportional hazards models were used for the survival analyses, and nonparametric permutation tests were performed for the micro-RNAs to investigate the association between the number of associated genes and its prognostication. We also utilized mediation analyses for micro-RNA-gene pairs to identify their mediation effects. Genome-wide analyses revealed a novel pattern: micro-RNAs related to more gene expressions are more likely to be associated with GBM survival (P = 4.8 × 10(-5)). Genome-wide mediation analyses for the 32,660 micro-RNA-gene pairs with strong association (false discovery rate [FDR] < 0.01%) identified 51 validated pairs with significant mediation effect. Of the 51 pairs, miR-223 had 16 mediation genes. These 16 mediation genes of miR-223 were also highly associated with various other micro-RNAs and mediated their prognostic effects as well. We further constructed a gene signature using the 16 genes, which was highly associated with GBM survival in both the discovery and validation sets (P = 9.8 × 10(-6)). This comprehensive study discovered mediation effects of micro-RNA to gene expression and GBM survival and provided a new analytic framework for integrative genomics.
多形性胶质母细胞瘤(GBM)是最常见的恶性脑肿瘤类型,具有高度致命性。由于对其快速进展的了解有限,因此需要采用其他方法来整合关于这种癌症基因组学的已知信息。我们使用GBM患者的一个发现集(n = 348)和一个验证集(n = 174),进行了全基因组分析,整合了来自GBM的mRNA和微小RNA表达数据以及相关的生存信息,评估了每种数据中的协同变异性,因为这反映了它们已知的机制功能。使用Cox比例风险模型进行生存分析,并对微小RNA进行非参数置换检验,以研究相关基因数量与其预后之间的关联。我们还对微小RNA - 基因对进行了中介分析,以确定它们的中介作用。全基因组分析揭示了一种新模式:与更多基因表达相关的微小RNA更有可能与GBM生存相关(P = 4.8 × 10^(-5))。对32,660个具有强关联的微小RNA - 基因对(错误发现率[FDR] < 0.01%)进行全基因组中介分析,确定了51个具有显著中介作用的验证对。在这51对中,miR - 223有16个中介基因。miR - 223的这16个中介基因也与各种其他微小RNA高度相关,并介导了它们的预后作用。我们进一步使用这16个基因构建了一个基因特征,该特征在发现集和验证集中均与GBM生存高度相关(P = 9.8 × 10^(-6))。这项全面的研究发现了微小RNA对基因表达和GBM生存的中介作用,并为整合基因组学提供了一个新的分析框架。