Despite the prevalence of mutations in the noncoding regions of the DNA, their effects on cancer development remain largely uninvestigated. This is especially evident when compared to coding mutations, which have been relatively well-studied and, in certain cases, been identified as driver mutations for cancer. Recent studies, however, have identified noncoding mutations that frequently appear in certain types of cancer, which may be evidence that those mutations are important to cancer development. Nonetheless, the role of noncoding mutations in cancer remains unclear. A potential vector for understanding this mechanism is through observing the relation between noncoding mutations and functional RNA motifs. The goals for this study, therefore, were to identify RNA motifs that were significantly associated with the presence of somatic, noncoding mutations and to predict the functional impact of noncoding variants. The analysis was conducted on mutations detected in whole genome sequencing profiles of breast cancer samples obtained from the TCGA database. I derived the significance of the number of noncoding mutations affecting a particular motif as well as the enrichment of noncoding mutations on each motif. I also created linear models to identify the motifs with mutations that had the greatest impact on cancer-related pathways. I found that a number of motifs are affected by significantly less mutations than we would expect at random. Additionally, I found that functional RNA motifs related to splicing are often significant in the linear models, suggesting that they play a role the relation between noncoding mutations and cancer. These findings will help improve understanding of the effects of noncoding mutations on RNA processing in the context of breast cancer.
","abstract_html":"<p>Despite the prevalence of mutations in the noncoding regions of the DNA, their effects on cancer development remain largely uninvestigated. This is especially evident when compared to coding mutations, which have been relatively well-studied and, in certain cases, been identified as driver mutations for cancer. Recent studies, however, have identified noncoding mutations that frequently appear in certain types of cancer, which may be evidence that those mutations are important to cancer development. Nonetheless, the role of noncoding mutations in cancer remains unclear. A potential vector for understanding this mechanism is through observing the relation between noncoding mutations and functional RNA motifs. The goals for this study, therefore, were to identify RNA motifs that were significantly associated with the presence of somatic, noncoding mutations and to predict the functional impact of noncoding variants. The analysis was conducted on mutations detected in whole genome sequencing profiles of breast cancer samples obtained from the TCGA database. I derived the significance of the number of noncoding mutations affecting a particular motif as well as the enrichment of noncoding mutations on each motif. I also created linear models to identify the motifs with mutations that had the greatest impact on cancer-related pathways. I found that a number of motifs are affected by significantly less mutations than we would expect at random. Additionally, I found that functional RNA motifs related to splicing are often significant in the linear models, suggesting that they play a role the relation between noncoding mutations and cancer. These findings will help improve understanding of the effects of noncoding mutations on RNA processing in the context of breast cancer.</p>","abstract_has_math":false,"creators":["Zhu, Kevin","orcid.org/0000-0003-2784-3190
"],"institution":null,"degree_name":"Masters of Science (MS)","degree_level":"Thesis (MS)","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Dr. Jeffrey T. Chang","Dr. Alemayehu A. Gorfe","Dr. Nicholas E. Navin"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-12-01T08:00:00Z","date_published":"2017-12-01T08:00:00Z","updated_at":"2026-07-24T05:50:09Z","subjects":["cancer","noncoding","rna motifs","mutation","rna processing","computational","bioinformatics","Medicine and Health Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.library.tmc.edu/utgsbs_dissertations/814","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Jeffrey T. Chang","Dr. Alemayehu A. Gorfe","Dr. Nicholas E. Navin"]},{"key":"dc:creator","label":"Author","values":["Zhu, Kevin","orcid.org/0000-0003-2784-3190
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"]},{"key":"dc:title","label":"Title","values":["Computational Identification of Noncoding Driver Mutations Based On Impact On Rna Processing"]}]}],"canonical_facts":{"dc:contributor":["Dr. Jeffrey T. Chang","Dr. Alemayehu A. Gorfe","Dr. Nicholas E. Navin"],"dc:creator":["Zhu, Kevin","orcid.org/0000-0003-2784-3190
"],"dc:date.available":["2017-10-31T07:00:00Z"],"dc:description.abstract":["Despite the prevalence of mutations in the noncoding regions of the DNA, their effects on cancer development remain largely uninvestigated. This is especially evident when compared to coding mutations, which have been relatively well-studied and, in certain cases, been identified as driver mutations for cancer. Recent studies, however, have identified noncoding mutations that frequently appear in certain types of cancer, which may be evidence that those mutations are important to cancer development. Nonetheless, the role of noncoding mutations in cancer remains unclear. A potential vector for understanding this mechanism is through observing the relation between noncoding mutations and functional RNA motifs. The goals for this study, therefore, were to identify RNA motifs that were significantly associated with the presence of somatic, noncoding mutations and to predict the functional impact of noncoding variants. The analysis was conducted on mutations detected in whole genome sequencing profiles of breast cancer samples obtained from the TCGA database. I derived the significance of the number of noncoding mutations affecting a particular motif as well as the enrichment of noncoding mutations on each motif. I also created linear models to identify the motifs with mutations that had the greatest impact on cancer-related pathways. I found that a number of motifs are affected by significantly less mutations than we would expect at random. Additionally, I found that functional RNA motifs related to splicing are often significant in the linear models, suggesting that they play a role the relation between noncoding mutations and cancer. These findings will help improve understanding of the effects of noncoding mutations on RNA processing in the context of breast cancer.
"],"dc:identifier":["https://digitalcommons.library.tmc.edu/utgsbs_dissertations/814"],"dc:subject":["cancer","noncoding","rna motifs","mutation","rna processing","computational","bioinformatics","Medicine and Health Sciences"],"dc:title":["Computational Identification of Noncoding Driver Mutations Based On Impact On Rna Processing"],"thesis:degree_level":["Thesis (MS)"],"thesis:degree_name":["Masters of Science (MS)"]},"updated_at":"2026-07-24T05:50:09Z"}