Open Access Highly Accessed Research article

A novel method of differential gene expression analysis using multiple cDNA libraries applied to the identification of tumour endothelial genes

John MJ Herbert1, Dov Stekel2, Sharon Sanderson3, Victoria L Heath1 and Roy Bicknell13*

Author Affiliations

1 Cancer Research UK Angiogenesis Group, Institute for Biomedical Research, University of Birmingham Medical School, Edgbaston, BIRMINGHAM, B15 2TT, UK

2 Centre for Systems Biology, School of Biosciences, University of Birmingham, Edgbaston, BIRMINGHAM, B15 2TT, UK

3 Cancer Research UK, Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, OXFORD, OX3 9DS, UK

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BMC Genomics 2008, 9:153  doi:10.1186/1471-2164-9-153

Published: 7 April 2008



In this study, differential gene expression analysis using complementary DNA (cDNA) libraries has been improved. Firstly by the introduction of an accurate method of assigning Expressed Sequence Tags (ESTs) to genes and secondly, by using a novel likelihood ratio statistical scoring of differential gene expression between two pools of cDNA libraries. These methods were applied to the latest available cell line and bulk tissue cDNA libraries in a two-step screen to predict novel tumour endothelial markers. Initially, endothelial cell lines were in silico subtracted from non-endothelial cell lines to identify endothelial genes. Subsequently, a second bulk tumour versus normal tissue subtraction was employed to predict tumour endothelial markers.


From an endothelial cDNA library analysis, 431 genes were significantly up regulated in endothelial cells with a False Discovery Rate adjusted q-value of 0.01 or less and 104 of these were expressed only in endothelial cells. Combining the cDNA library data with the latest Serial Analysis of Gene Expression (SAGE) library data derived a complete list of 459 genes preferentially expressed in endothelium. 27 genes were predicted tumour endothelial markers in multiple tissues based on the second bulk tissue screen.


This approach represents a significant advance on earlier work in its ability to accurately assign an EST to a gene, statistically measure differential expression between two pools of cDNA libraries and predict putative tumour endothelial markers before entering the laboratory. These methods are of value and available webcite to researchers that are interested in the analysis of transcriptomic data.