CNRS UMR 6061 Genetic and Development, University of Rennes 1, Rennes, France.
Clin Cancer Res. 2011 Jan 15;17(2):317-27. doi: 10.1158/1078-0432.CCR-10-1126. Epub 2011 Jan 11.
Gene expression studies provide molecular insights improving the classification of patients with high-grade gliomas. We have developed a risk estimation strategy based on a combined analysis of gene expression data to search for robust biomarkers associated with outcome in these tumors.
We performed a meta-analysis using 3 publicly available malignant gliomas microarray data sets (267 patients) to define the genes related to both glioma malignancy and patient outcome. These biomarkers were used to construct a risk-score equation based on a Cox proportional hazards model on a subset of 144 patients. External validations were performed on microarray data (59 patients) and on RT-qPCR data (194 patients). The risk-score model performances (discrimination and calibration) were evaluated and compared with that of clinical risk factors, MGMT promoter methylation status, and IDH1 mutational status.
This interstudy cross-validation approach allowed the identification of a 4-gene signature highly correlated to survival (CHAF1B, PDLIM4, EDNRB, and HJURP), from which an optimal survival model was built (P < 0.001 in training and validation sets). Multivariate analysis showed that the 4-gene risk score was strongly and independently associated with survival (hazard ratio = 0.46; 95% CI, 0.26-0.81; P = 0.007). Performance estimations indicated that this score added beyond standard clinical parameters and beyond both the MGMT methylation status and the IDH1 mutational status in terms of discrimination (C statistics, 0.827 versus 0.835; P < 0.001).
The 4-gene signature provides an independent risk score strongly associated with outcome of patients with high-grade gliomas.
基因表达研究提供了分子见解，有助于改善高级别脑胶质瘤患者的分类。我们开发了一种风险估计策略，该策略基于基因表达数据的综合分析，以寻找与这些肿瘤预后相关的稳健生物标志物。
我们使用 3 个公开的恶性脑胶质瘤微阵列数据集（267 例患者）进行荟萃分析，以确定与胶质瘤恶性程度和患者预后均相关的基因。这些生物标志物用于构建一个基于 Cox 比例风险模型的风险评分方程，该方程在 144 例患者的子集中进行构建。在微阵列数据（59 例患者）和 RT-qPCR 数据（194 例患者）上进行了外部验证。评估了风险评分模型的性能（区分度和校准度），并与临床危险因素、MGMT 启动子甲基化状态和 IDH1 突变状态进行了比较。
这种跨研究的交叉验证方法允许从高度与生存相关的 4 个基因特征（CHAF1B、PDLIM4、EDNRB 和 HJURP）中鉴定出一个生存模型（在训练和验证集中均 P < 0.001）。多变量分析表明，4 个基因风险评分与生存强烈且独立相关（风险比=0.46；95%CI，0.26-0.81；P=0.007）。性能评估表明，该评分在区分度方面优于标准临床参数，并且优于 MGMT 甲基化状态和 IDH1 突变状态（C 统计量，0.827 与 0.835；P < 0.001）。
该 4 个基因特征提供了一个与高级别脑胶质瘤患者预后密切相关的独立风险评分。
