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In silico whole-genome screening for cancer-related single-nucleotide polymorphisms located in human mRNA untranslated regions

Abdel Aouacheria14*, Vincent Navratil1, Ricardo López-Pérez2, Norma C Gutiérrez2, Alexander Churkin3, Danny Barash3, Dominique Mouchiroud1 and Christian Gautier1

Author Affiliations

1 Laboratory of Biometry and Evolutionary Biology, CNRS UMR 5558, Claude Bernard University Lyon 1, 69622 Villeurbanne, France

2 Servicio de Hematología, Hospital Universitario de Salamanca, Centro de Investigación del Cáncer, Universidad de Salamanca-CISC, Spain

3 Department of Computer Science, Ben-Gurion University, 84105 Beer Sheva, Israel

4 Apoptosis and Oncogenesis Laboratory, Institute of Biology and Chemistry of Proteins, IBCP UMR 5086 CNRS-UCBL, IFR 128 Biosciences Lyon-Gerland; 7 passage du vercors, 69367 Lyon Cedex 07, France

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BMC Genomics 2007, 8:2  doi:10.1186/1471-2164-8-2

Published: 3 January 2007



A promising application of the huge amounts of genetic data currently available lies in developing a better understanding of complex diseases, such as cancer. Analysis of publicly available databases can help identify potential candidates for genes or mutations specifically related to the cancer phenotype. In spite of their huge potential to affect gene function, no systematic attention has been paid so far to the changes that occur in untranslated regions of mRNA.


In this study, we used Expressed Sequence Tag (EST) databases as a source for cancer-related sequence polymorphism discovery at the whole-genome level. Using a novel computational procedure, we focused on the identification of untranslated region (UTR)-localized non-coding Single Nucleotide Polymorphisms (UTR-SNPs) significantly associated with the tumoral state. To explore possible relationships between genetic mutation and phenotypic variation, bioinformatic tools were used to predict the potential impact of cancer-associated UTR-SNPs on mRNA secondary structure and UTR regulatory elements. We provide a comprehensive and unbiased description of cancer-associated UTR-SNPs that may be useful to define genotypic markers or to propose polymorphisms that can act to alter gene expression levels. Our results suggest that a fraction of cancer-associated UTR-SNPs may have functional consequences on mRNA stability and/or expression.


We have undertaken a comprehensive effort to identify cancer-associated polymorphisms in untranslated regions of mRNA and to characterize putative functional UTR-SNPs. Alteration of translational control can change the expression of genes in tumor cells, causing an increase or decrease in the concentration of specific proteins. Through the description of testable candidates and the experimental validation of a number of UTR-SNPs discovered on the secreted protein acidic and rich in cysteine (SPARC) gene, this report illustrates the utility of a cross-talk between in silico transcriptomics and cancer genetics.