Application of cDNA microarrays to generate a molecular taxonomy capable of distinguishing between colon cancer and normal colon

Tong Tong Zou, Florin M. Selaru, Yan Xu, Valentina Shustova, Jing Yin, Yuriko Mori, David Shibata, Fumiaki Sato, Suma Wang, Andreea Olaru, Elena Deacu, Thomas C. Liu, John M. Abraham, Stephen J. Meltzer

Research output: Contribution to journalArticlepeer-review

129 Scopus citations


In order to discover global gene expression patterns characterizing subgroups of colon cancer, microarrays were hybridized to labeled RNAs obtained from seventeen colonic specimens (nine carcinomas and eight normal samples). Using a hierarchical agglomerative method, the samples grouped naturally into two major clusters, in perfect concordance with pathological reports (colon cancer versus normal colon). Using a variant of the unpaired t-test, selected genes were ordered according to an index of importance. In order to confirm microarray data, we performed quantitative, real-time reverse transcriptase - polymerase chain reaction (TaqMan RT-PCR) on RNAs from 13 colorectal tumors and 13 normal tissues (seven of which were matched normal-tumor pairs). RT-PCR was performed on the gro1, B-factor, adlican, and endothelin converting enzyme-1 genes and confirmed microarray findings. Two hundred and fifty genes were identified, some of which were previously reported as being involved in colon cancer. We conclude that cDNA microarraying, combined with bioinformatics tools, can accurately classify colon specimens according to current histopathological taxonomy. Moreover, this technology holds promise of providing invaluable insight into specific gene roles in the development and progression of colon cancer. Our data suggests that a large-scale approach may be undertaken with the purpose of identifying biomarkers relevant to cancer progression.

Original languageEnglish (US)
Pages (from-to)4855-4862
Number of pages8
Issue number31
StatePublished - 2002
Externally publishedYes


  • Cluster analysis
  • Colon cancer
  • Gene filtering
  • cDNA microarrays

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Cancer Research


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