MicroRNAs (miRNAs) are negative gene regulators acting at the 3'UTR level, modulating the translation of cancer-related genes. Single-nucleotide polymorphisms (SNPs) within the 3'UTRs could impact the miRNA-dependent gene regulation either by weakening or by reinforcing the binding sites. Thus, the alteration of the normal regulation of a given gene could affect the individual's risk of cancer. Therefore, it is helpful to develop a tool enabling the researchers to predict which of the many SNPs could really impact the regulation of a target gene. At present, there are several available databases and algorithms able to predict potential binding sites in the 3'UTR of genes. However, each algorithm gives different predictions and none of them gives, for each polymorphism, a direct measurement of the biological impact. We propose an approach allowing the assignment to each polymorphism a ranking of its biological impact. The method is based on a simple elaboration of predictions from preexisting well-established algorithms. As an example, we show the application of this approach to 140 genes candidate for colorectal cancer (CRC). These genes were identified following a genome-wide sequencing of 20,857 transcripts from 18,191 genes in 11 CRC specimens and were found somatically mutated and thought to be crucial for the development of cancer.

Prediction of the biological effect of polymorphisms within microRNA binding sites.

BARALE, ROBERTO;GEMIGNANI, FEDERICA;LANDI, STEFANO
2011

Abstract

MicroRNAs (miRNAs) are negative gene regulators acting at the 3'UTR level, modulating the translation of cancer-related genes. Single-nucleotide polymorphisms (SNPs) within the 3'UTRs could impact the miRNA-dependent gene regulation either by weakening or by reinforcing the binding sites. Thus, the alteration of the normal regulation of a given gene could affect the individual's risk of cancer. Therefore, it is helpful to develop a tool enabling the researchers to predict which of the many SNPs could really impact the regulation of a target gene. At present, there are several available databases and algorithms able to predict potential binding sites in the 3'UTR of genes. However, each algorithm gives different predictions and none of them gives, for each polymorphism, a direct measurement of the biological impact. We propose an approach allowing the assignment to each polymorphism a ranking of its biological impact. The method is based on a simple elaboration of predictions from preexisting well-established algorithms. As an example, we show the application of this approach to 140 genes candidate for colorectal cancer (CRC). These genes were identified following a genome-wide sequencing of 20,857 transcripts from 18,191 genes in 11 CRC specimens and were found somatically mutated and thought to be crucial for the development of cancer.
Landi, D; Barale, Roberto; Gemignani, Federica; Landi, Stefano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/201349
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