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Medusa-structure of gene regulatory network: Dominance of transcription factors in cancer subtypes
Date:5/6/2011

Over the past decades, researchers seeking to understand molecular mechanisms underlying various diseases, notably cancer, have taken advantage of DNA microarrays to interrogate tissues specimen of patients for the expression status of thousands of genes at once. Jointly, such gene expression status of each gene in the genome, measured as the level of their transcripts, constitutes the gene expression profile. Since each of the tens of thousands of genes can be switched on or off, a gene expression profile contains complex information, akin to a huge bar code with tens of thousands of digits for every sample. While microarray data was initially used by gene hunters to identify novel genes, such as those which are only active in samples of particular cancer tissues, researchers have later learned to employ sophisticated computational tools to classify these bar codes into subgroups and to find subgroup specific signatures. In cancer research such statistical analysis of gene expression patterns can serve to identify new cancer subtypes and help classify patients more accurately. However, there is only so much that such brute force computational pattern recognition can offer. Biologists also would like to understand: Where the particular gene expression pattern comes from? How does the cell know how to "write" the long bar code, defining the expression level of gene after gene, across tens of thousands of genes, in such a reliable manner to encode cell types and cancer cell phenotypes? Although not often asked by computational biologists using statistical analysis to extract information, this is a central and basic biological question.

A team of researchers at Harvard Medical School's Children's Hospital led by Sui Huang (who is now at the University of Calgary) have analyzed gene expression profiles with precisely this question in mind. In the work published in the May 2011 issue of Experimental Biology and Medicine, Dr. Huang and his students,
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Contact: Dr. Sui Huang
sui.huang@ucalgary.ca
403-210-7858
Society for Experimental Biology and Medicine
Source:Eurekalert

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