Identification and validation of a gene expression signature that predicts outcome in malignant glioma patients
Identification and validation of a gene expression signature that predicts outcome in malignant glioma patients
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DOI：
10.3892/ijo.2011.1240
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发表时间：
2012-03-01
影响因子：
5.2
通讯作者：
Yamanaka, Ryuya
中科院分区：
文献类型：
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作者：
Kawaguchi, Atsushi;Yajima, Naoki;Yamanaka, Ryuya
更好地了解恶性胶质瘤的潜在生物学对于早期检测策略和新疗法的发展至关重要。这项研究旨在确定与生存相关的基因。我们研究了使用随机生存森林模型选择的基因是否可以用于客观地定义胶质瘤亚组。使用GeneChip Human Genome U133 Plus 2.0 Expression阵列分析来自50个未治疗的神经胶质瘤的RNA。我们确定了82个基因，其表达与患者生存率密切相关。出于实用目的，还选择了15个基因组。完整的82个基因签名和15个基因集亚组都表明它们在4个独立外部数据集中的3个中具有显著的预测性。我们的方法是有效的客观分类胶质瘤，并提供了一个更准确的预后预测。我们通过使用随机生存森林模型评估了基因表达与生存时间之间的关系，与微阵列的显著性分析相比，这种性能是更好的分类器。
Better understanding of the underlying biology of malignant gliomas is critical for the development of early detection strategies and new therapeutics. This study aimed to define genes associated with survival. We investigated whether genes selected using random survival forests model could be used to define subgroups of gliomas objectively. RNAs from 50 non-treated gliomas were analyzed using the GeneChip Human Genome U133 Plus 2.0 Expression array. We identified 82 genes whose expression was strongly and consistently related to patient survival. For practical purposes, a 15-gene set was also selected. Both the complete 82 gene signature and the 15 gene set subgroup indicated their significant predictivity in the 3 out of 4 independent external dataset. Our method was effective for objectively classifying gliomas, and provided a more accurate predictor of prognosis. We assessed the relationship between gene expressions and survival time by using the random survival forests model and this performance was a better classifier compared to significance analysis of microarrays.