Abstract
Background: A promising application of the huge amounts of genetic data currently available lies in developinga better understanding of complex diseases, such as cancer. Analysis of publicly available databases can helpidentify potential candidates for genes or mutations specifically related to the cancer phenotype. In spite of theirhuge potential to affect gene function, no systematic attention has been paid so far to the changes that occur inuntranslated regions of mRNA.Results: In this study, we used Expressed Sequence Tag (EST) databases as a source for cancer-related sequencepolymorphism discovery at the whole-genome level. Using a novel computational procedure, we focused on theidentification 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 andphenotypic variation, bioinformatic tools were used to predict the potential impact of cancer-associated UTRSNPson mRNA secondary structure and UTR regulatory elements. We provide a comprehensive and unbiaseddescription of cancer-associated UTR-SNPs that may be useful to define genotypic markers or to proposepolymorphisms that can act to alter gene expression levels. Our results suggest that a fraction of cancerassociatedUTR-SNPs may have functional consequences on mRNA stability and/or expression.Conclusion: We have undertaken a comprehensive effort to identify cancer-associated polymorphisms inuntranslated regions of mRNA and to characterize putative functional UTR-SNPs. Alteration of translationalcontrol can change the expression of genes in tumor cells, causing an increase or decrease in the concentrationof specific proteins. Through the description of testable candidates and the experimental validation of a numberof UTR-SNPs discovered on the secreted protein acidic and rich in cysteine (SPARC) gene, this report illustratesthe utility of a cross-talk between in silico transcriptomics and cancer genetics.