A molecular map of mesenchymal tumors.
A molecular map of mesenchymal tumors.
复制标题
间充质肿瘤的分子图。
DOI：
10.1186/gb-2005-6-9-r76
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发表时间：
2005
期刊：
影响因子：
12.3
通讯作者：
Boshoff, Chris
中科院分区：
文献类型：
--
作者：
Henderson, Stephen R;Guiliano, David;Presneau, Nadege;McLean, Sean;Frow, Richard;Vujovic, Sonja;Anderson, John;Sebire, Neil;Whelan, Jeremy;Athanasou, Nick;Flanagan, Adrienne M;Boshoff, Chris
一项对96例间叶肿瘤基因表达谱的综合研究确定了该组大多数肿瘤的分子指纹。骨和软组织肿瘤代表了一组不同的肿瘤，被认为是来自间充质或神经嵴的细胞。由于许多肿瘤的分化差或异质性，组织学诊断具有挑战性，导致预后和适当治疗的不确定性。我们对96个肿瘤的基因表达谱进行了广泛而全面的研究，这些肿瘤代表了所有间质组织，包括几个问题诊断组。使用适用于这个问题的机器学习方法，我们确定了大多数肿瘤的分子指纹，这些指纹具有特异性（决定性）和生物学揭示性。我们展示了基因表达谱和机器学习对复杂临床问题的实用性，并确定了某些间叶肿瘤的假定起源。
A comprehensive study of the gene expression profile of 96 mesenchymal tumors identifies molecular fingerprints for most tumors in this group. Bone and soft tissue tumors represent a diverse group of neoplasms thought to derive from cells of the mesenchyme or neural crest. Histological diagnosis is challenging due to the poor or heterogenous differentiation of many tumors, resulting in uncertainty over prognosis and appropriate therapy. We have undertaken a broad and comprehensive study of the gene expression profile of 96 tumors with representatives of all mesenchymal tissues, including several problem diagnostic groups. Using machine learning methods adapted to this problem we identify molecular fingerprints for most tumors, which are pathognomonic (decisive) and biologically revealing. We demonstrate the utility of gene expression profiles and machine learning for a complex clinical problem, and identify putative origins for certain mesenchymal tumors.