The Project
MicroRNAs (miRs) are small non-coding RNAs of about 22 nucleotides long. Deep-sequencing and computational approaches indicate that mammalian genomes harbor thousands of miRs, and each miR can potentially influence the expression of hundreds of genes, whereas almost every mRNA can be targeted by dozens and even hundreds of miRs. miRs are involved in a wide range of biological processes (cell differentiation, organ development, hormonal and neural regulation, immune response, oncogenesis, et al.) and currently recognized as the global regulators of gene expression. Genetic variations of miRs and their target sites among individuals are increasingly recognized as possible factors underlying various human disorders.
My research is aimed to explore the genetic polymorphisms of miRs target sites in human populations in relation to malignancies. We plan to analyze genetic variations within and near microRNA-binding sites in oncogenes and tumor suppressor genes. We hypothesize that SNP may disrupt or, in opposite, create new microRNA-binding sites, thus, resulting in higher probability of tumorigenesis.
Using the bioinformatics tools, we analyzed about 400 previously reported cancer-associated SNPs within the microRNA-binding sites and found that more than 90% of them are surrounded by verified single and multiple low-frequency SNPs. The latter are positioned within the projected seed-matching areas (1-5 nucleotides, 58% incidence), within the microRNA-matching regions (between 6 and 20 nucleotides, 71% incidence), and within the distance where they potentially can affect microRNA-mRNA interaction (between 21 and 30 nucleotides, 36% incidence). These rare SNP may influence microRNA-mediated regulation of their target genes. It suggests the necessity to explore the links between predisposition to cancers and the combinations of several SNP in the proximity of microRNA-binding sites. The experimental approach includes sequencing SNPs in human tumors and established human cancer cell lines and testing the functionality of polymorphic microRNA-binding sites. We also apply mathematical analysis to compare the probability of the certain combination of nucleotides within the polymorphic sites with the frequencies of the specific cancers in human population.
