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Advancing ovarian cancer research through subtype specific models, molecular stratification, and a patient guided approach to identify long non-coding RNA biomarkers
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Date
2025
Authors
McCabe, Aideen
Journal Title
Journal ISSN
Volume Title
Publisher
University College Cork
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Abstract
Ovarian cancer is the most fatal gynaecological malignancy, responsible for over 200,000 deaths worldwide each year. Low ovarian cancer survival rates are primarily driven by a lack of effective early detection methods, poor understanding of subtype specific heterogeneity and non-specific symptoms. Therefore, there is a striking need to both improve our understanding of the molecular differences underlying ovarian cancer subtypes and to identify more effective early-detection biomarkers. This research should also be informed by the priorities of those affected by the disease in order to ensure relevance and increase the translation of results into the clinic.
This thesis aims to address these gaps in ovarian cancer research via three main aims: improving pre-clinical model selection, creating accessible molecular subtyping tools, and identifying lncRNA biomarker candidates. Chapter 2 evaluates the transcriptomic and genomic features of a panel of 56 ovarian cancer cell lines to identify those that best match the gene expression profiles of various ovarian cancer subtypes. This chapter also shows that A2780, one of the most commonly used cell lines, is not representative of any subtype of ovarian cancer. Chapter 3 describes the development of classifieRov, a user-friendly Shiny application that implements consensus molecular subtyping for high-grade serous ovarian cancer (HGSOC) tumours, the most common and aggressive ovarian cancer subtype. This tool facilitates stratification of HGSOC tumours into four clinically relevant molecular subtypes, while also allowing researchers without bioinformatics expertise to apply a number of sample annotation tools to their data, making exploring these subtypes more accessible to the broader ovarian cancer research community. Finally, Chapter 4 details a patient and public involvement (PPI) guided approach to investigate long non-coding RNAs as potential blood-based biomarkers for ovarian cancer detection. In reponse to PPI input, single-cell RNA sequencing of peripheral blood mononuclear cells and analysis of tumor-educated platelet and primary tumour RNA profiles identified highly specific lncRNAs with potential for non-invasive diagnosis. Collectively, this work advances ovarian cancer research by establishing a panel of subtype-specific cell line models, creating accessible bioinformatics tools and implementing a patient-informed biomarker discovery pipeline.
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Keywords
Ovarian cancer , Bioinformatics , Patient and public involvement , Biomarker , Long non-coding RNA
Citation
McCabe, A. 2025. Advancing ovarian cancer research through subtype specific models, molecular stratification, and a patient guided approach to identify long non-coding RNA biomarkers. PhD Thesis, University College Cork.