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首页> 外文期刊>Genomics >Genomic expression patterns distinguish long-term from short-term glioblastoma survivors: a preliminary feasibility study.
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Genomic expression patterns distinguish long-term from short-term glioblastoma survivors: a preliminary feasibility study.

机译：基因组表达模式将长期和短期胶质母细胞瘤幸存者区分开来：初步的可行性研究。

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We used microarray analysis to investigate associations between genotypic expression profiles and survival phenotypes in patients with primary glioblastoma (GBM). Tumor samples from 7 long-term glioblastoma survivors (>24 months) and 13 short-term survivors (<9 months) were analyzed to detect differential patterns of gene expression between these groups and to identify genotypic subclasses of glioblastomas that correlate with survival phenotypes. Five unsupervised and three supervised clustering algorithms consistently and accurately grouped the tumors into genotypic subgroups corresponding to the two clinical survival phenotypes. Three unique prospective mathematical classification algorithms were subsequently trained to use expression data to stratify unknown glioblastomas between survival groups and performed this task with 100% accuracy in validation studies. A set of 1478 genes with significant differential expression (p<0.01) between long-term and short-term survivors was identified, and additional mathematical filtering was used to isolate a 43-gene "fingerprint" that distinguished survival phenotypes. Differential regulation of a subset of these genes was confirmed using RT-PCR. Gene ontology analysis of the fingerprint demonstrated pathophysiologic functions for the gene products that are consistent with current models of tumor biology, suggesting that differential expression of these genes may contribute etiologically to the observed differences in survival. These results demonstrate that unique expression profiles characterize genotypic subsets of primary GBMs associated with differential survival phenotypes, and these profiles can be used in a prospective fashion to assign unknown tumors to survival groups. Future efforts will focus on building more robust classifiers and identifying additional subclasses of gliomas with phenotypic significance.
机译：我们使用微阵列分析来调查原发性胶质母细胞瘤（GBM）患者的基因型表达谱与生存表型之间的关联。分析了来自7个长期胶质母细胞瘤幸存者（> 24个月）和13个短期胶质母细胞瘤（<9个月）的肿瘤样本，以检测这些组之间基因表达的差异模式，并鉴定与存活表型相关的基因胶质母细胞瘤的亚型。五种无监督和三种有监督的聚类算法将肿瘤一致且准确地分为与两种临床生存表型相对应的基因型亚组。随后对三种独特的前瞻性数学分类算法进行了训练，以使用表达数据对存活组之间的未知胶质母细胞瘤进行分层，并在验证研究中以100％的准确性执行此任务。鉴定出一组1478个在长期和短期存活者之间具有显着差异表达（p <0.01）的基因，并使用附加的数学过滤方法来分离可区分存活表型的43个基因“指纹”。使用RT-PCR证实了这些基因的一个子集的差异调节。指纹的基因本体论分析证明了与当前肿瘤生物学模型相一致的基因产物的病理生理功能，表明这些基因的差异表达可能在病因上有助于观察到的生存差异。这些结果表明，独特的表达谱表征了与差异生存表型相关的原发性GBM的基因型亚群，并且这些谱可以以前瞻性的方式用于将未知肿瘤分配给生存组。未来的工作将集中在建立更强大的分类器和识别具有表型意义的神经胶质瘤的其他亚类。

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