Molecular classification and survival prediction in human gliomas based on proteome analysis
Molecular classification and survival prediction in human gliomas based on proteome analysis
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DOI：
10.1158/0008-5472.can-03-1254
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发表时间：
2004-04-01
期刊：
影响因子：
11.2
通讯作者：
Yamaura, A
中科院分区：
文献类型：
--
作者：
Iwadate, Y;Sakaida, T;Yamaura, A
胶质瘤的生物学特征是高度异质性的生物学侵袭性，即使在同一组织学类别中也是如此，全球基因表达数据将在蛋白质水平上准确描述胶质瘤的生物学特征。我们研究了基于双向凝胶电泳和基质辅助激光解吸/电离飞行时间质谱仪的蛋白质组分析是否可以识别高级别和低级别胶质瘤组织中蛋白质表达的差异。比较了85个组织样本的蛋白质组获利模式：52例多形性胶质母细胞瘤、13例间变性星形细胞瘤、10例间变性星形细胞瘤、10例萎缩性细胞瘤和10例正常脑组织。基于蛋白质组图谱的聚类分析可以完全区分正常脑组织和脑胶质瘤组织。基于蛋白质组的聚集性与患者的存活率显著相关，我们可以确定具有侵袭性的星形细胞瘤的生物学亚型。判别分析提取了一组根据组织学分级差异表达的37个蛋白质。其中，许多在高级别胶质瘤中升高的蛋白质被归类为信号转导蛋白，包括小G蛋白。免疫组织化学分析证实已鉴定的蛋白质在胶质瘤组织中表达。本研究表明，蛋白质组分析有助于开发一种新的系统来预测胶质瘤的生物学侵袭性。这些蛋白质可能是预测生存的新生物标记物和抗胶质瘤治疗的合理靶点。
The biological features of gliomas, which are characterized by highly heterogeneous biological aggressiveness even in the same histological category, would be precisely described by global gene expression data at the protein level. We investigated whether proteome analysis based on two-dimensional gel electrophoresis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry can identify differences in protein expression between high- and low-grade glioma tissues. Proteome profiting patterns were compared in 85 tissue samples: 52 glioblastoma multiforme, 13 anaplastic astrocytomas, 10 atrocytomas, and 10 normal brain tissues. We could completely distinguish the normal brain tissues from glioma tissues by cluster analysis based on the proteome profiling patterns. Proteome-based clustering significantly correlated with the patient survival, and we could identify a biologically distinct subset of astrocytomas with aggressive nature. Discriminant analysis extracted a set of 37 proteins differentially expressed based on histological grading. Among them, many of the proteins that were increased in high-grade gliomas were categorized as signal transduction proteins, including small G-proteins. Immunohistochemical analysis confirmed the expression of identified proteins in glioma tissues. The present study shows that proteome analysis is useful to develop a novel system for the prediction of biological aggressiveness of gliomas. The proteins identified here could be novel biomarkers for survival prediction and rational targets for antiglioma therapy.