Survival analysis and its applications in identifying genes, signatures, and pathways in human cancers

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buir.advisorKarakayalı, Özlen Konu
dc.contributor.authorÖzhan, Ayşe
dc.date.accessioned2021-11-10T12:09:50Z
dc.date.available2021-11-10T12:09:50Z
dc.date.copyright2021-09
dc.date.issued2021-09
dc.date.submitted2021-11-09
dc.departmentGraduate Program in Materials Science and Nanotechnologyen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Ph.D.): Bilkent University, Department of Materials Science and Nanotechnology, İhsan Doğramacı Bilkent University, 2021.en_US
dc.descriptionIncludes bibliographical references (leaves 89-97).en_US
dc.description.abstractCancer literature makes use of survival analyses focused on gene expression based on univariable or multivariable regression. However, there is still a need to understand whether a) incorporating exon or isoform information on expression would improve estimation of survival in cancer patients; and b) applying multivariable regression to gene sets would allow to obtain cancer-specific independent gene signatures in cancer. Differential usage of individual exons, as well as transcripts, are phenomena common to cancerous tissue when compared to normal tissue. The glioblastoma, GBM; liver cancer LIHC; stomach adenocarcinoma, STAD; and breast carcinoma, BRCA datasets from The Cancer Genome Atlas (TCGA) were investigated to identify individual exons and transcripts with transcriptome-wide impact and significance on survival. Aggregation analyses of exons revealed the important genes for survival in each dataset, including GNA12 in STAD, AKAP13 in LIHC and RBMXL1 and CARS1 in BRCA. GSEA was applied on gene sets formed from the exon-based analysis, revealing distinct enrichment profiles for each dataset as well as overlaps for certain GO terms and KEGG pathways. In the second focus of this thesis, multivariable analyses on gene sets whose expressions were obtained from UCSC Xena were used to create two Shiny applications: one for dataset-specific analyses and one for analyses across TCGA-PANCAN. The dataset specific SmulTCan application incorporates Cox regression analyses with expressions of input genes of the user’s choice. The SmulTCan application contains additional model validation, best subset selection and prognostic analyses. The ClusterHR application performs clustering analyses with Cox regression results, while it can also be used for bicluster identification and comparison. The axon-guidance ligand-receptor gene sets Slit-Robo, netrins-receptors and Semas-receptors were used for demonstrating the apps. Several hazard ratio signatures and best subsets that can differentiate between prognostic outcomes have been identified from the input gene sets, as well as ligand-receptor pairs with prognostic significance.en_US
dc.description.degreePh.D.en_US
dc.description.statementofresponsibilityby Ayşe Özhanen_US
dc.embargo.release2022-03-15
dc.format.extentxii, 99 leaves : illustrations, charts (colors) ; 30 cm.en_US
dc.identifier.itemidB133519
dc.identifier.urihttp://hdl.handle.net/11693/76675
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRNA-Sequencingen_US
dc.subjectExonen_US
dc.subjectTranscriptomicsen_US
dc.subjectCanceren_US
dc.subjectTCGA-PANCANen_US
dc.subjectSurvivalen_US
dc.subjectCox regressionen_US
dc.subjectHierarchical clusteringen_US
dc.subjectGene-set enrichmenten_US
dc.subjectShinyen_US
dc.subjectPrognosisen_US
dc.subjectMachine learningen_US
dc.subjectAxon guidanceen_US
dc.subjectNoncoding RNAen_US
dc.titleSurvival analysis and its applications in identifying genes, signatures, and pathways in human cancersen_US
dc.title.alternativeGen, im ve yolak saptanmasında sağkalım analizi ve uygulamalarıen_US
dc.typeThesisen_US

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Graduate Program in Materials Science and Nanotechnology - Ph.D. / Sc.D.