Shyamsundar Radha, Kim Young H, Higgins John P, Montgomery Kelli, Jorden Michelle, Sethuraman Anand, van de Rijn Matt, Botstein David, Brown Patrick O, Pollack Jonathan R
Department of Pathology, Stanford University School of Medicine, 269 Campus Drive, CCSR 3245A, Stanford, CA 94305-5176, USA.
Genome Biol. 2005;6(3):R22. doi: 10.1186/gb-2005-6-3-r22. Epub 2005 Feb 14.
Numerous studies have used DNA microarrays to survey gene expression in cancer and other disease states. Comparatively little is known about the genes expressed across the gamut of normal human tissues. Systematic studies of global gene-expression patterns, by linking variation in the expression of specific genes to phenotypic variation in the cells or tissues in which they are expressed, provide clues to the molecular organization of diverse cells and to the potential roles of the genes.
Here we describe a systematic survey of gene expression in 115 human tissue samples representing 35 different tissue types, using cDNA microarrays representing approximately 26,000 different human genes. Unsupervised hierarchical cluster analysis of the gene-expression patterns in these tissues identified clusters of genes with related biological functions and grouped the tissue specimens in a pattern that reflected their anatomic locations, cellular compositions or physiologic functions. In unsupervised and supervised analyses, tissue-specific patterns of gene expression were readily discernable. By comparative hybridization to normal genomic DNA, we were also able to estimate transcript abundances for expressed genes.
Our dataset provides a baseline for comparison to diseased tissues, and will aid in the identification of tissue-specific functions. In addition, our analysis identifies potential molecular markers for detection of injury to specific organs and tissues, and provides a foundation for selection of potential targets for selective anticancer therapy.
众多研究已使用DNA微阵列来检测癌症及其他疾病状态下的基因表达。而对于正常人体组织中广泛表达的基因,我们了解得相对较少。通过将特定基因表达的变化与它们所表达的细胞或组织中的表型变化联系起来,对全球基因表达模式进行系统研究,可为不同细胞的分子组织以及基因的潜在作用提供线索。
在此,我们描述了一项对代表35种不同组织类型的115个人体组织样本中的基因表达进行的系统检测,使用的是代表约26,000种不同人类基因的cDNA微阵列。对这些组织中的基因表达模式进行无监督层次聚类分析,确定了具有相关生物学功能的基因簇,并将组织样本按反映其解剖位置、细胞组成或生理功能的模式进行了分组。在无监督和有监督分析中,基因表达的组织特异性模式很容易辨别。通过与正常基因组DNA进行比较杂交,我们还能够估计已表达基因的转录本丰度。
我们的数据集为与患病组织进行比较提供了一个基线,并将有助于确定组织特异性功能。此外,我们的分析确定了用于检测特定器官和组织损伤的潜在分子标记,并为选择选择性抗癌治疗的潜在靶点提供了基础。