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  • 标题:Comparative expressed sequence hybridization to chromosomes for tumor classification and identification of genomic regions of differential gene expression
  • 本地全文:下载
  • 作者:Yong-Jie Lu ; Daniel Williamson ; Jeremy Clark
  • 期刊名称:Proceedings of the National Academy of Sciences
  • 印刷版ISSN:0027-8424
  • 电子版ISSN:1091-6490
  • 出版年度:2001
  • 卷号:98
  • 期号:16
  • 页码:9197-9202
  • DOI:10.1073/pnas.161272798
  • 语种:English
  • 出版社:The National Academy of Sciences of the United States of America
  • 摘要:Altered expression of genes can have phenotypic consequences in cancer development and treatment, developmental abnormalities, and differentiation processes. Here we describe a rapid approach, termed comparative expressed sequence hybridization (CESH), which gives a genome-wide view of relative expression patterns within tissues according to chromosomal location. No prior knowledge of genes or cloning is required, and minimal amounts of tissue can be used. Expression profiles are achieved in a manner similar to the identification of chromosomal imbalances by comparative genomic hybridization analysis. The approach is demonstrated to indicate a chromosomal region that harbors overexpressed genes that may be associated with a drug-resistant phenotype. In addition, known and new regions of differential gene expression in both normal tissues and tumor samples from the soft tissue sarcoma group of rhabdomyosarcoma (RMS) are indicated. These regions included 2p24; overexpression of MYCN at 2p24 was confirmed by quantitative reverse transcription-PCR for all of the alveolar RMS cases and did not necessarily correspond to genomic amplification. Evidence including region specific microarray analysis indicated that overexpression of several genes from a region may be required for detection by CESH. This evidence is consistent with clusters of functionally related genes and mechanisms that affect the expression of a number of genes at a particular genomic location. The distinctive CESH profiles demonstrated in different subtypes of RMS show potential for tumor classification.
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