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Profiling and Identification of Small Non-coding RNAs as Prognostic Markers for Breast Cancer Open Access


Other title
Breast Cancer
Small non-coding RNAs
Piwi-interacting RNAs
Transfer RNAs
Small nucleolar RNAs
Next Generation Sequencing
Prognostic Markers
Overall Survival
Recurrence Free Survival
Gene expression
The Cancer Genome Atlas Project
PIWI genes
Risk Score
Reduction mammoplasty
Type of item
Degree grantor
University of Alberta
Author or creator
Krishnan, Preethi
Supervisor and department
Damaraju, Sambasivarao (Laboratory Medicine and Pathology)
Examining committee member and department
Mackey, John R (Oncology)
Ghosh, Sunita (Oncology)
Kovalchuk, Olga (Biological Sciences, University of Lethbridge)
Medical Sciences-Laboratory Medicine and Pathology

Date accepted
Graduation date
2016-06:Fall 2016
Doctor of Philosophy
Degree level
Breast cancer (BC) continues to be one of the leading causes of cancer related death among women. Despite continuous progress in screening, diagnosis and treatment of BC, a subset of patients experience recurrence and/or death. Optimal management of BC has remained a challenge due to these inter-individual variations in response to treatment. Although the reasons for inter-individual variations are elusive at this point of time, the challenge now lies in identifying patients who are at higher risk for recurrence and/or death. This in turn may aid in altering treatment modalities according to individual’s needs, enhancing the quality of life and survival period. So far, prognostication of BC has relied largely upon clinical staging combined with traditional biomarkers such as Estrogen receptor, Progesterone receptor and human epidermal growth factor receptor but these have remained imperfect estimators of risk for recurrence. Messenger RNA molecules from microarray profiling studies that have so far been in several clinical trials for BC prognostication have also seen limited success in routine clinical use, highlighting the need for more robust biomarkers. In this thesis, I have considered small non-coding RNAs (sncRNAs) as potential biomarkers for BC. sncRNAs (< 200 nt in length) are a group of RNAs that are transcribed, yet not translated, but perform an array of functions. Specifically, I have focused on four sncRNAs – miRNAs, piRNAs, tRNAs and snoRNAs. Although the canonical functions of each of these RNAs are different, these four RNAs appear to share some gene regulatory functions predominantly at the post-transcriptional level, though there may be exceptions for gene regulation even at a transcriptional level. miRNAs and piRNAs are classified as master regulators of gene expression; whereas, tRNAs and snoRNAs are currently being explored for gene regulatory functions. A possible mechanism by which these molecules may exert regulatory roles is by generating distinct gene regulatory molecules (e.g., miRNAs and piRNAs). The clinical relevance of miRNAs in the context of BC has been well addressed. However, the contribution of the piRNAs, snoRNAs and tRNAs is beginning to emerge for BC etiology but their role in prognosis in BC are at best rudimentary, if not, unknown. The main objective of this thesis was to identify miRNAs, piRNAs, tRNAs and snoRNAs associated with BC prognosis, with outcomes of interest being overall survival (OS) and recurrence free survival (RFS). sncRNAs were profiled from 11 normal (reduction mammoplasty) and 104 breast tumor tissues using next generation sequencing, which enables a genome-wide capture of sncRNAs. Two statistical paradigms were adopted to identify prognostic markers from every class of sncRNAs – case-control (CC) and case-only (CO). While the former approach considered only differentially expressed sncRNAs for survival analysis and may miss on a subset of expressed sncRNAs, the latter approach included all the sncRNAs profiled for a comprehensive analysis. Individual classes of sncRNAs from CC and CO were subjected to Univariate Cox proportional hazards regression modeling. Risk scores were constructed using a panel of significant sncRNAs (which varied from 4-14 for each class of sncRNAs). Based on cut-off point estimated using receiver operating characteristics curve, patients were classified into low and high-risk groups. Further, risk scores were investigated to identify their potential as independent prognostic factors using multivariate Cox proportional hazards regression model. Signatures from miRNAs, piRNAs, snoRNAs and tRNAs independently showed association with both OS and RFS – (i) risk scores were identified as potential independent prognostic factors and (ii) patients belonging to high-risk group were associated with poor prognosis. sncRNAs associated with OS were independently validated using TCGA dataset, strengthening the study findings. To further gain biological insights of the prognostic sncRNAs, putative gene (mRNA) targets regulated by miRNAs and piRNAs were identified from an in-house gene expression dataset; these studies served as a proxy for functional validation. Also, other sncRNAs (along with their corresponding targets) embedded within snoRNAs were identified. The identified targets were involved in key cellular pathways such as apoptosis, cell cycle, cell migration and proliferation. Overall, my work has identified novel sncRNA molecules as potential biomarkers for BC prognostication. This work on genome-wide profiling of sncRNAs using modern sequencing platforms significantly augments the limited previous literature, and the data provided in this study therefore extends the comprehensive search for BC biomarkers.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
Citation for previous publication
Krishnan P, Ghosh S, Wang B, Li D, Narasimhan A, Berendt R, Graham K, Mackey J, Kovalchuk O, Damaraju S. Next generation sequencing profiling identifies miR-574-3p and miR-660-5p as potential novel prognostic markers for breast cancer. BMC Genomics 2015;16:735-735. (doi: 10.1186/s12864-015-1899-0)Krishnan P, Ghosh S, Graham K, Mackey J, Kovalchuk O, Damaraju S. Piwi-interacting RNAs and PIWI genes as novel prognostic markers for breast cancer. Oncotarget 2016. (doi: 10.18632/oncotarget.9272)Krishnan P, Ghosh S, Wang B, Li D, Berendt R, Mackey J, Kovalchuk O, Damaraju S. Small nucleolar RNAs – New players in breast cancer prognosis. [abstract]. In: Proceedings of the AACR Special Conference on Noncoding RNAs and Cancer: Mechanisms to Medicines ; 2015 Dec 4-7; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2016;76(6 Suppl):Abstract nr B37

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