Abstract
BackgroundA 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.ResultsIn 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.ConclusionWe 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.
Highlights
A promising application of the huge amounts of genetic data currently available lies in developing a better understanding of complex diseases, such as cancer
Since cDNA libraries are generated from a wide range of cancerous and normal tissues, Expressed Sequence Tag (EST) can be used both for measuring relative levels of gene expression [1,2,3,4,5,6], and for detecting single nucleotide differences among sequences derived from a same gene [7,8]
We developed an EST-based pipeline to detect cancerassociated untranslated region (UTR)-single nucleotide polymorphisms (SNPs)
Summary
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 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. Transcript sequences represent a key source for the search of aberrantly expressed genes and for the identification of genes whose products are deregulated in malignant cells. Among these transcript sequences, Expressed Sequence Tags (ESTs) are partial single-pass sequences of cDNAs made of mRNA from a particular organ, tissue or cell line. In addition to the well-established role of the poly-(A) tail, which confers protection to the RNA molecule from degradation by exonucleases, resulting in enhancement of translation, there are a number of motif sequences within the 3'-UTR that regulate mRNA stability and translational efficiency, including the recently identified microRNA-binding sites [22,23]
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