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    Combined network analysis and molecular dynamics simulations study for characterization of prevalent somatic mutations in breast cancer: Sf3b1 case study
    (İstanbul Medipol Üniversitesi Fen Bilimleri Enstitüsü, 2020) Mahmoud, A. S. M.; Özdemir, Mehmet Kemal
    Breast cancer has the highest incidence and mortality rates among women. The etiology of the disease has remained elusive because of complex interactions among various factors. The somatic mutations are one of such factors that lead to breast cancer development. Many somatic mutations have been identified in breast cancer. Unfortunately, in many cases, our knowledge about these mutations is limited to their allele frequencies and their relations to cancer deserve further investigation. In this thesis, in silico approach was defined to investigate the impact of somatic mutations in breast cancer by utilizing publicly available databases, bioinformatics, and computational biophysics tools. Firstly, a gene network of 67 genes participate in splicing mechanism was constructed. Analysis of this network reveals that splicing factor 3B subunit 1 (SF3B1) is the central node having higher network metrics and the highest mutation rate among other genes. Then, data and network analyses showed i) impact of aberrant splicing on other biological processes such as regulation of cell proliferation, apoptosis, and transcription and ii) relations among hematologic malignancies and breast cancer that may explain the transformation from one cancer to another. Lastly, the impact of K700E on dynamics and structure of SF3B1 was investigated by performing classical molecular dynamics simulation. Comparative analysis of wild type vs. mutant trajectories showed that the mutation i) decreases the stability of the components of the splicing machinery such as SF3B1, p14, and pre-mRNA, which consequently weakens the interaction formed between pre-mRNA and both K700E and p14$^{RRM}$and ii) distorts the communication among SF3B1 residues. These changes may lead to alternative branch point selection, aberrant splicing of pre-mRNA, and production of abnormal transcripts. This thesis provided i) insights into complex interactions among genes, pathways, and diseases that may improve the development of new prevention, prognostic and therapeutic approaches for cancer and ii) molecular details to understand the functional consequences of K700E on the spliceosomal machinery, proposing SF3B1 as a potential biomarker and therapeutic target for cancer. In light of these findings, the defined in silico process, which is based on bioinformatics, network analysis, and computational biophysics tools, can be improved and employed in the identification and characterization of highly mutated genes that lead to cancer development and/or prognosis. Consequently, developing new prevention and therapeutic approaches can be improved to combat cancer.