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dc.contributor.advisorJeffrey A. Bailey
dc.contributor.authorMovassagh, Mercedeh J.
dc.date2022-08-11T08:08:38.000
dc.date.accessioned2022-08-23T16:01:58Z
dc.date.available2022-08-23T16:01:58Z
dc.date.issued2019-04-30
dc.date.submitted2019-05-10
dc.identifier.doi10.13028/nfs6-2431
dc.identifier.urihttp://hdl.handle.net/20.500.14038/31241
dc.description.abstractViruses are known to be associated with 20% of human cancers. Epstein Barr virus (EBV) in particular is the first virus associated with human cancers. Here, we computationally detect EBV and explore the effects of this virus across cancers by taking advantage of the fact that EBV microRNAs (miRNAs) and Epstein Barr virus small RNAs (EBERs) are expressed at all viral latencies. We identify and characterize two sub-populations of EBV positive tumors: those with high levels of EBV miRNA and EBERS expression and those with medium levels of expression. Based on principal component analysis (PCA) and hierarchical clustering of viral miRNAs across all samples we observe a pattern of expression for these EBV miRNAs which is correlated with both the tumor cell type (B cell versus epithelial cell) and with the overall levels of expression of these miRNAs. We further investigated the effect of the levels of EBV miRNAs with the overall survival of patients across cancers. Through Kaplan Meier survival analysis we observe a significant correlation with levels of EBV miRNAs and lower survival in adult AML patients. We also designed a machine learning model for risk assessment of EBV in association with adult AML and other clinical factors. Our next aim was to identify targets of EBV miRNAs, hence, we used a combination of previously known methodologies for miRNA target detection in addition to a multivariable regression approach to identify targets of these viral miRNAs in stomach cancer. Finally, we investigate the variations across EBV subtype specific EBNA3C gene which interacts with the host immune system. Preliminary data suggests potential regional variations plus higher pathogenicity of subtype 1 in comparison to subtype 2 EBV. Overall, these studies further our understanding of how EBV manipulates the tumor microenvironment across cancer subtypes.
dc.language.isoen_US
dc.rightsLicensed under a Creative Commons license
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectEBV
dc.subjectCancer
dc.subjectmiRNAs
dc.subjectEBERs
dc.subjectEpidemiology
dc.subjectAcute Myeloid Leukemia (AML)
dc.subjectStomach Cancer
dc.subjectEndemic Burkitt Lymphoma (eBL)
dc.subjectComputational Biology
dc.subjectDisease Modeling
dc.subjectGenetics and Genomics
dc.subjectGenomics
dc.subjectImmune System Diseases
dc.subjectTranslational Medical Research
dc.subjectVirus Diseases
dc.titleComprehensive Computational Assessment And Evaluation of Epstein Barr virus (EBV) Variations, miRNAs, And EBERs in eBL, AML And Across Cancers
dc.typeDoctoral Dissertation
dc.identifier.legacyfulltexthttps://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=2032&context=gsbs_diss&unstamped=1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/gsbs_diss/1022
dc.legacy.embargo2020-06-01T00:00:00-07:00
dc.identifier.contextkey14471172
refterms.dateFOA2022-08-27T04:41:04Z
html.description.abstract<p>Viruses are known to be associated with 20% of human cancers. Epstein Barr virus (EBV) in particular is the first virus associated with human cancers. Here, we computationally detect EBV and explore the effects of this virus across cancers by taking advantage of the fact that EBV microRNAs (miRNAs) and Epstein Barr virus small RNAs (EBERs) are expressed at all viral latencies. We identify and characterize two sub-populations of EBV positive tumors: those with high levels of EBV miRNA and EBERS expression and those with medium levels of expression.</p> <p>Based on principal component analysis (PCA) and hierarchical clustering of viral miRNAs across all samples we observe a pattern of expression for these EBV miRNAs which is correlated with both the tumor cell type (B cell versus epithelial cell) and with the overall levels of expression of these miRNAs.</p> <p>We further investigated the effect of the levels of EBV miRNAs with the overall survival of patients across cancers. Through Kaplan Meier survival analysis we observe a significant correlation with levels of EBV miRNAs and lower survival in adult AML patients. We also designed a machine learning model for risk assessment of EBV in association with adult AML and other clinical factors.</p> <p>Our next aim was to identify targets of EBV miRNAs, hence, we used a combination of previously known methodologies for miRNA target detection in addition to a multivariable regression approach to identify targets of these viral miRNAs in stomach cancer.</p> <p>Finally, we investigate the variations across EBV subtype specific EBNA3C gene which interacts with the host immune system. Preliminary data suggests potential regional variations plus higher pathogenicity of subtype 1 in comparison to subtype 2 EBV.</p> <p>Overall, these studies further our understanding of how EBV manipulates the tumor microenvironment across cancer subtypes.</p>
dc.identifier.submissionpathgsbs_diss/1022
dc.contributor.departmentProgram in Bioinformatics and Integrative Biology
dc.description.thesisprogramBioinformatics and Computational Biology
dc.identifier.orcid0000-0001-7690-0230


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