Research Information System
Thesis Type: Postgraduate
Institution Of The Thesis: Yildiz Technical University, Faculty Of Chemıcal And Metallurgıcal Engıneerıng, Department Of Bioengineering, Turkey
Approval Date: 2018
Thesis Language: Turkish
Student: Selcen ARI
Supervisor: Alper Yılmaz
Abstract:
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.