Date of Award
12-2022
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
School of Computing
Committee Chair/Advisor
Dr. Terri F. Bruce
Committee Member
Dr. Brian C. Dean
Committee Member
Dr. Bethany Wolf
Committee Member
Dr. William Bridges
Committee Member
Dr. William Richardson
Abstract
Ovarian cancer (OC) is an aggressive gynecological cancer and is currently the 5th leading cause of deaths due to cancer in women. High mortality rates are attributable to the vague pathogenesis and asymptomatic nature of the early stages. The development of a liquid biopsy for routine OC screening could help identify the disease at an earlier stage, making treatments more likely to be effective thereby increasing survival rates. Exosomes, small (~100nm) extracellular vesicles present in body fluids, have been shown to contain cancer-progression, onset, and related factors, making them good candidates for use in liquid biopsies. However, to date, only limited exosomal data is available for OC patients.
This study first examines the exosomal small RNAs (miRs) derived from cervical mucus samples for early-stage predictive potential in patients. This was done using several multivariate and univariate approaches including a filter-based feature selection method based in information theory – minimum redundancy and maximum relevance (mRMR) sparsely applied to RNA-seq data. We externally validate this using two independent datasets and functionally validate it using bioinformatics approaches.
Second, we construct a primary miR integrated network using published literature comprising of microRNAs (miRs), genes, pathway information and disease for functional association of miR biomarkers with the disease that followed propagation of influence through the network using random walk theory. We introduce the minimum redundance and maximum relevance (mRMR) into this network to account for redundant features.
Finally, we extend the mRMR filter criterion in a novel score aggregation method, called the ‘miROUNDTABLE’, integrating information from the multiple independent ‘agents’ to work towards a consensus biomarker panel. The ‘agents’ represent full ranked lists from different sources (different studies, and/or different analysis methods). This method aims at mitigating inconsistencies of identified candidate biomarkers across different studies to some extent while improving their reproducibility in new cohorts most of which have small sample sizes. We inspect this protocol’s stability using a stability measure.
This multi-agent score aggregation method is a disease-agnostic approach and can be easily extended to other diseases while being a step toward small data integration in ovarian cancer studies. This research aims to bridge the gap between miRNA related research findings, be the groundwork for further research in role of exosomes in early-stage cancer progression and facilitate the transition from the bench to a clinical setting for early ovarian cancer prediction and diagnostics.
Recommended Citation
Mandal, Paritra, "Data-Driven Biomarker Panel Discovery in Ovarian Cancer Using Heterogenous Data Fusion on Exosomal and Non-Exosomal Microrna Expression Data" (2022). All Dissertations. 3176.
https://tigerprints.clemson.edu/all_dissertations/3176
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