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Noncoding RNA-Involved Interactions for Cancer Prognosis: A Prostate Cancer Study

Wang, Leying
http://rave.ohiolink.edu/etdc/view?acc_num=osu1586651927830285

Abstract Details

2020, Master of Science, Ohio State University, Public Health.
Prostate cancer is a heterogeneous disease affecting millions of people worldwide. Noncoding RNAs (ncRNAs) have received attention in many prostate cancer studies. Accumulating evidence support that ncRNAs, such as pseudogenes and microRNAs (miRNAs), play essential regulatory roles in biological processes. The abnormal abundance of ncRNAs in cells can cause aberrant expression of coding genes triggering cancer-initiation and progression. Because of the intertwined relationship between different types of ncRNAs and coding genes, more complicated features such as ncRNA-gene interactions are worth to be accounted for when predicting survival and cancer progression. In this study, we used TCGA PRAD RNA-Seq expression profiles to develop multivariate Cox regression models for predicting prostate cancer progression and overall survival. We include pseudogene-coding gene interactions (PGGs) and miRNA-coding gene interactions (MTIs) when building these predictive models. In summary, 1 PGG and 9 MTIs were found significantly associated with prostate cancer progression-free survival. We also identified 10 coding genes, 15 pseudogenes, and 23 miRNAs that are associated with prostate cancer recurrence. For prostate cancer overall survival, no PGG interactions were identified significant. But 4 MTIs, 7 coding genes, and 8 miRNAs were found to be associated with overall survival. Overall, the expression patterns of these identified interactions and gene biomarkers can separate patients into high-risk and low-risk groups, therefore they are worth of further investigation as potential drug targets. However, we noticed that the predictive model for overall survival does not perform as good as the predictive model for progression-free survival due to a small number of events. In conclusion, this study proves the importance of ncRNAs and indicates that ncRNA-involved interactions should be considered when modeling prostate cancer survival. With these identified biomarkers and models, we will be able to predict patient response to treatments and provide potential drug targets for novel prostate cancer therapeutic development.
Lianbo Yu (Advisor)
Yan Zhang (Committee Member)
Chi Song (Committee Member)
92 p.
Bioinformatics

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Citations

  • Wang, L. (2020). Noncoding RNA-Involved Interactions for Cancer Prognosis: A Prostate Cancer Study [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586651927830285

    APA Style (7th edition)

  • Wang, Leying. Noncoding RNA-Involved Interactions for Cancer Prognosis: A Prostate Cancer Study. 2020. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1586651927830285.

    MLA Style (8th edition)

  • Wang, Leying. "Noncoding RNA-Involved Interactions for Cancer Prognosis: A Prostate Cancer Study." Master's thesis, Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1586651927830285

    Chicago Manual of Style (17th edition)

osu1586651927830285
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This open access ETD is published by The Ohio State University and OhioLINK.