Investigating the association between long non-coding RNAs and ductal carcinoma in situ

dc.check.chapterOfThesisChapters of thesis - 3,4,5,6
dc.check.date2024-01-22T11:26:34Z
dc.check.embargoformatApply the embargo to the e-thesis on CORA (If you have submitted an e-thesis and want to embargo it on CORA)en
dc.check.infoRestricted to everyone for three yearsen
dc.check.opt-outNot applicableen
dc.check.reasonThis thesis is due for publication or the author is actively seeking to publish this materialen
dc.contributor.advisorDean, Kellieen
dc.contributor.advisorO'Connor, Rosemaryen
dc.contributor.authorSamson, Julia
dc.date.accessioned2021-01-22T11:26:34Z
dc.date.issued2020
dc.date.submitted2020
dc.description.abstractBreast cancer is the most common cancer in women globally, with incidence rates increasing and survival rates varying widely depending on early detection and access to treatment. In Ireland, the number of diagnoses is increasing (National Cancer Registry, 2016), with 89% of new cases being invasive breast cancer. To reduce the number of individuals with invasive cancer, there is an urgent need for specific and sensitive diagnostic biomarkers for the earliest stages of breast cancer. Ductal carcinoma in situ (DCIS) is a non-obligate precursor to invasive ductal carcinoma. With an increasing number of studies linking long, non-coding RNAs (lncRNAs) to various cancers, we have specifically selected to examine lncRNAs as novel DCIS biomarkers and to characterise their biological roles. To do so, we aimed to perform a transcriptomic study focused on lncRNAs using two patient-derived DCIS cell lines (ETCC-006 and ETCC-010) and one cellular model of DCIS, MCF10DCIS.com. However, since ETCC-006 and ETCC-010 cells have been poorly studied in the literature, we started by conducting a detailed molecular characterization of those cells, comparing them to other breast cancer cell lines. Contrary to previously published results, we found that ETCC-006 and ETCC-010 cells in culture are more similar to triple negative breast cancer cell lines based on hormone receptor status. Next, we performed a lncRNA-capture RNA sequencing (RNAseq) on those characterized cells. From those experiments, we identified 92 common differentially expressed lncRNAs in those cells compared to MCF-10A cells. Using data from The Cancer Genome Atlas (TCGA), we reduced our targets to six lncRNAs whose altered expression is associated with adverse breast cancer patient outcomes. Next, we aimed to functionally characterize those targets by monitoring proliferation and migration after altering their expression levels. This led us to focus specifically on LOC729970, an uncharacterized lncRNA in the literature. To investigate its molecular function, we identified LOC729970 specific proteins interactors by mass spectrometry. Then, we chose to focus on the glycolytic enzyme GAPDH and the angiogenic factor CYR61 to confirm their interaction with LOC729970. Ultimately, our goal is to identify and characterise lncRNA biomarkers in DCIS with the hope of establishing a list of lncRNAs involved in the transition toward IDC. This could then lead to the development of diagnostic tools that could change how early breast cancer is detected and treated in patients worldwideen
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationSamson, J. 2020. Investigating the association between long non-coding RNAs and ductal carcinoma in situ. PhD Thesis, University College Cork.en
dc.identifier.endpage279en
dc.identifier.urihttps://hdl.handle.net/10468/10949
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2020, Julia Samson.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectLong non-coding RNAen
dc.subjectBreast canceren
dc.subjectDuctal carcinoma in situen
dc.subjectBiomarkeren
dc.thesis.opt-outfalse
dc.titleInvestigating the association between long non-coding RNAs and ductal carcinoma in situen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhDen
ucc.workflow.supervisork.dean@ucc.ie
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