EXPLORATION OF METHYLATION-DRIVEN MECHANISMS IN CANCER
Molecular Biology, Genetics & Bioengineering, PhD Dissertation, 2016
Prof. Dr. Ismail Cakmak (Thesis Advisor), Prof.Dr. Ugur Sezerman, Prof.Dr. Yucel Saygin,
MD Ph.D. Devrim Gozuacik, Assoc. Prof. Oguzhan Kulekci
Date & Time: 4th of April, 2016 – 2.40 PM
Keywords : Transcriptomics, Epigenetics, Data analysis, Data integration, Cancer Biology, Protein-protein interaction network, Functional Enrichment Analysis
DNA methylation is an important epigenetic phenomenon that plays a key role in the regulation of expression. For this reason, there have been many studies on the topic of methylation’s role in cancer mechanisms. These studies include analyses based on differential methylation, with the integration of expression information as supporting evidence. In the present study, we firstly focused on defining an optimal analysis strategy when both expression and methylation information are available. We investigated the methylation and expression changes on the genes themselves to have a deeper knowledge of thyroid cancer etiology. Moreover, we investigated more important genomic regions considering methylation information and identified common and distinct genes and pathways among different cancer types. In addition, we defined a novel graph-based analysis strategy for identifying methylation-driven potential cancer-causing gene patterns. We applied our method to variety of cancers using the Illumina HumanMethylation450k methylation chip and RNA sequencing data. To extract the significantly altered methylation-driven patterns within a STRING protein-protein interaction network, we first defined a methylation change threshold for “large methylation changes”. Subsequently, in addition to focusing on the interplay between methylation and expression, we carefully considered the individual relationships between different genes to ensure a deeper understanding of the methylome and transcriptome. Furthermore, we investigated the presence of shared and distinct features among the different types of cancers using hierarchical clustering analysis. Overall, our study not only defined a novel approach for the identification of significantly altered methylation-driven pathways but also contributed to improving our knowledge of the etiologies of different cancers and the common and distinct features among them.