Gene expression profiling of human cancers
Gene expression profiling of human cancers
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DOI：
10.1196/annals.1322.003
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发表时间：
2004-01-01
期刊：
影响因子：
--
通讯作者：
Smith, CP
中科院分区：
文献类型：
--
作者：
Bucca, G;Carruba, G;Smith, CP
DNA微阵列使我们能够同时可视化一个细胞群体或组织样本中潜在的所有基因的表达--揭示“转录组”。这类数据的分析通常被称为基因表达谱(GEP)，因为它提供了特定生物样本中基因表达模式的全面图景。由于这个原因，微阵列正在给生命科学研究带来革命性的变化，并导致研究癌症生物学、癌症分类和预测癌症临床结果的新的和强大的方法的发展。最近几个备受瞩目的报告揭示了GEP数据的集群如何能够清楚地识别临床上(和预后上)重要的癌症亚型，这些患者根据既定的临床病理标准被认为患有类似的肿瘤。准确的“预后信号”可以从GEP数据中获得，这些数据代表相对较少的基因数量。这些信号在指导适当的治疗和预测临床结果方面可能很有价值，根据临床和组织学标准，它们通常优于其他系统。本文将介绍DNA微阵列技术的基本原理和现有的不同类型的微阵列平台，并通过回顾最近一些关于选定癌症的GEP研究，包括我们巴勒莫实验室对肝细胞癌的初步分析，来说明该技术的威力。GEP很可能在未来被用作临床领域的关键决策工具。然而，在该技术能够在这方面得到广泛采用之前，需要解决与数据分析、重复性、交叉可比性、验证和成本有关的几个问题。
DNA microarrays allow us to visualize simultaneously the expression of potentially all genes within a cell population or tissue sample-revealing the "transcriptome." The analysis of this type of data is commonly called "gene expression profiling" (GEP) because it provides a comprehensive picture of the pattern of gene expression in a particular biological sample. For this reason microarrays are revolutionizing life sciences research and are leading to the development of novel and powerful methods for investigating cancer biology, classifying cancers, and predicting clinical outcome of cancers. Several recent high-profile reports have revealed how clustering of GEP data can clearly identify clinically (and prognostically) important subtypes of cancer among patients considered by established clinicopathological criteria to have similar tumors. Accurate "prognostic signatures" can be obtained from GEP data, which represent relatively small numbers of genes. These signatures can be valuable in directing appropriate treatment and in predicting clinical outcome, and they generally outperform other systems based on clinical and histological criteria. In this paper the basic principles of DNA microarray technology and the different types of microarray platforms available will be introduced, and the power of the technique will be illustrated by reviewing some recent GEP studies on selected cancers, including a preliminary analysis of hepatocellular carcinoma from our Palermo laboratory. GEP is likely to be adopted in the future as a key decision-making tool in the clinical arena. However, several issues relating to data analysis, reproducibility, cross-comparability, validation, and cost need to be resolved before the technology can be adopted broadly in this context.