Perez-Diez Ainhoa, Morgun Andrey, Shulzhenko Natalia
Ghost Lab, Laboratory of Cellular and Molecular Immunology, NIAID, NIH, USA.
Adv Exp Med Biol. 2007;593:74-85. doi: 10.1007/978-0-387-39978-2_8.
Microarray analysis has yet to be widely accepted for diagnosis and classification of human cancers, despite the exponential increase in microarray studies reported in the literature. Among several methods available, a few refined approaches have evolved for the analysis of microarray data for cancer diagnosis. These include class comparison, class prediction and class discovery. Using as examples some of the major experimental contributions recently provided in the field of both hematological and solid tumors, we discuss the steps required to utilize microarray data to obtain general and reliable gene profiles that could be universally used in clinical laboratories. As we show, microarray technology is not only a new tool for the clinical lab but it can also improve the accuracy of the classical diagnostic techniques by suggesting novel tumor-specific markers. We then highlight the importance of publicly available microarray data and the development of their integrated analysis that may fulfill the promise that this new technology holds for cancer diagnosis and classification.
尽管文献报道的微阵列研究呈指数级增长,但微阵列分析尚未被广泛用于人类癌症的诊断和分类。在现有的几种方法中,已经出现了一些用于癌症诊断微阵列数据分析的精细方法。这些方法包括类别比较、类别预测和类别发现。我们以血液学和实体瘤领域最近的一些主要实验贡献为例,讨论利用微阵列数据获得可在临床实验室普遍使用的通用且可靠的基因图谱所需的步骤。正如我们所展示的,微阵列技术不仅是临床实验室的一种新工具,还可以通过提出新的肿瘤特异性标志物来提高传统诊断技术的准确性。然后,我们强调了公开可用的微阵列数据的重要性以及其综合分析的发展,这可能实现这项新技术在癌症诊断和分类方面的前景。