Author ORCID Identifier

Date of Graduation


Document Type

Thesis (MS)

Program Affiliation

Biomedical Sciences

Degree Name

Masters of Science (MS)

Advisor/Committee Chair

Dr. Jeffrey T. Chang

Committee Member

Dr. Alemayehu A. Gorfe

Committee Member

Dr. Nicholas E. Navin

Committee Member

Dr. Sanjay S. Shete

Committee Member

Dr. Xiaobing Shi


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.


cancer, noncoding, rna motifs, mutation, rna processing, computational, bioinformatics



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.