Transcript variants of CELF2 gene as unique prognostic indicators in breast cancer

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buir.advisorGüre, Ali Osmay
dc.contributor.authorAzizolli, Shila
dc.date.accessioned2022-09-14T06:06:02Z
dc.date.available2022-09-14T06:06:02Z
dc.date.copyright2022-08
dc.date.issued2022-08
dc.date.submitted2022-09-13
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Master's): Bilkent University, Department of Molecular Biology and Genetics, İhsan Doğramacı Bilkent University, 2022.en_US
dc.descriptionIncludes bibliographical references (leaves 55-61).en_US
dc.description.abstractBreast cancer (BC) is the most common malignant tumor in women around the world. Aside from finding a cure for this disease, it is also critical to identify prognostic biomarkers that can help clinicians intervene with the appropriate treatment and prevent BC progression. Current biomarker identification methods rely primarily on multi-gene prognostic signature models. However, due to the tumors' high heterogeneity, the accuracy of these multi-gene signatures is questionable. As a result, our main objective was to conduct a comprehensive analysis to identify a reliable prognostic biomarker in BC. Previously, a group of eight carnitine metabolites and SAH were linked to a poor prognosis in BC (Dr. Waqas Akbar, Unpublished Data). We discovered the genes associated with these metabolites using correlation analysis, and then we identified CELF2 as a good prognostic biomarker in BC. We validated CELF2's prognostic role in RNA-Seq and Microarray datasets in-silico. We demonstrate that the CELF2 1554569_a_at probeset is more consistent in its association with a favorable prognosis direction than the 202157_s_at probeset. When compared to the other probeset, CELF2 – 202157_s_at is expressed at higher levels and in a broader range of tissues. We were unable to find a clear significant association between CELF2 expression and prognosis during the in-vitro immunohistochemistry validation experiments. We hypothesized that this could be because our polyclonal anti-CELF2 antibody also recognized the less consistent 202157_s_at CELF2 probeset. We discovered that there are probeset-specific CELF2 transcript variants that are associated with different prognosis while testing this hypothesis. We created a Risk Score model by combining the expression levels of good and less-favorable CELF2 prognostic transcripts to improve prognosis prediction accuracy. The model successfully stratified the patients and predicted a higher overall survival in the Low-Risk group versus the High-Risk group. Overall, our findings suggest that each unique transcript variant of a gene can be associated with different prognosis directions. Therefore, we propose that studying prognostic associations at a gene transcript level could be a rich resource for the development of more robust biomarkers and therapeutics in cancer in the future.en_US
dc.description.provenanceSubmitted by Betül Özen (ozen@bilkent.edu.tr) on 2022-09-14T06:06:02Z No. of bitstreams: 1 B161286.pdf: 3363598 bytes, checksum: 1298074c2b720c5602f55a3e02ea4114 (MD5)en
dc.description.provenanceMade available in DSpace on 2022-09-14T06:06:02Z (GMT). No. of bitstreams: 1 B161286.pdf: 3363598 bytes, checksum: 1298074c2b720c5602f55a3e02ea4114 (MD5) Previous issue date: 2022-08en
dc.description.statementofresponsibilityby Shila Azizollien_US
dc.embargo.release2023-03-12
dc.format.extentxiv, 67 leaves : charts (some color) ; 30 cm.en_US
dc.identifier.itemidB161286
dc.identifier.urihttp://hdl.handle.net/11693/110504
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBreast canceren_US
dc.subjectPrognosisen_US
dc.subjectBiomarkeren_US
dc.subjectTranscript variantsen_US
dc.titleTranscript variants of CELF2 gene as unique prognostic indicators in breast canceren_US
dc.title.alternativeMeme kanserinde CELF2 geninin özel prognostik göstergeler olarak transkrip çeşitlerien_US
dc.typeThesisen_US
thesis.degree.disciplineMolecular Biology and Genetics
thesis.degree.grantorBilkent University
thesis.degree.levelMaster's
thesis.degree.nameMS (Master of Science)

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