Analysis of interplay between gene expression and miRNA expression in breast cancer next generation sequencing data

MiRNAs are one of the most important factors in the molecular regulation of cancer. Since the discovery of their potential for diagnosis and treatment, studies attemped to explain interaction of miRNAs with their target genes. However, the fact that miRNAs affect multiple targets poses a challenge in miRNA:target analysis. It is necessary to be understood of microRNA:target interactions and behaviors via network-based model system which includes microRNA binding and activity factors, for the identification of interactions between microRNAs and their targets. In this study, gene and miRNA expression data from breast cancer patients were retrieved from TCGA and these data were integrated via miRNA:target binding data retrieved from high throughput immunoprecipitation experiments available at several databases. A network-based model system has been developed taking into account factors that are important in miRNA:target interactions such as miRNA expression levels, gene expression level, seed type, binding energy and target site location on the gene. MiRNA:target interactions have been investigated by a network-based approach because single miRNA can have many targets and multiple miRNAs can exhibit repressive activity on one gene. In the case of a change in expression level of one the miRNA targets, recalculation of the expression levels of all the target genes interacting with that miRNA were analyzed on the network. As a result, a model system has been designed that simulates the regulation mechanisms and competitive behaviors of target genes through miRNA. When the network-based model system was applied to ABCC1 gene upregulation in the two breast cancer patient sample data sets, it was found that expression changes of 18.6% and 17.5% of the interacting genes on network could be predicted approximately. When the network-based model was applied to an in vitro study data set in which Septin9 gene was upregulated was performed, it was found that expression changes of 36% of the genes could be estimated approximately. In this context, it is considered that if regulatory element and the factors that are important in the model approach are known, the model can estimate the changes in the expression occurring in thousands of genes interacting after a change.The examination of competing behaviors of target genes against common targets of miRNAs and cooperative activities of miRNA on the regulation of common targets on network by the model developed in this thesis study will facilitate the understanding of complex miRNA: target interactions.