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A bespoke target selection tool to guide biomarker discovery in tubo-ovarian cancer
INTRODUCTION: Cancers presenting at advanced stages inherently have poor prognosis. High grade serous carcinoma (HGSC) is the most common and aggressive form of tubo-ovarian cancer. Clinical tests to accurately diagnose and monitor this condition are lacking. Hence, development of disease-specific t...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9260242/ https://www.ncbi.nlm.nih.gov/pubmed/35832628 http://dx.doi.org/10.1016/j.csbj.2022.06.016 |
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author | Beirne, James P. Gilmore, Alan McInerney, Caitríona E. Roddy, Aideen Glenn McCluggage, W. Harley, Ian J.G. Abdullah Alvi, M. Prise, Kevin M. McArt, Darragh G. Mullan, Paul B. |
author_facet | Beirne, James P. Gilmore, Alan McInerney, Caitríona E. Roddy, Aideen Glenn McCluggage, W. Harley, Ian J.G. Abdullah Alvi, M. Prise, Kevin M. McArt, Darragh G. Mullan, Paul B. |
author_sort | Beirne, James P. |
collection | PubMed |
description | INTRODUCTION: Cancers presenting at advanced stages inherently have poor prognosis. High grade serous carcinoma (HGSC) is the most common and aggressive form of tubo-ovarian cancer. Clinical tests to accurately diagnose and monitor this condition are lacking. Hence, development of disease-specific tests are urgently required. METHODS: The molecular profile of HGSC during disease progression was investigated in a unique patient cohort. A bespoke data browser was developed to analyse gene expression and DNA methylation datasets for biomarker discovery. The Ovarian Cancer Data Browser (OCDB) is built in C# with a.NET framework using an integrated development environment of Microsoft Visual Studio and fast access files (.faf). The graphical user interface is easy to navigate between four analytical modes (gene expression; methylation; combined gene expression and methylation data; methylation clusters), with a rapid query response time. A user should first define a disease progression trend for prioritising results. Single or multiomics data are then mined to identify probes, genes and methylation clusters that exhibit the desired trend. A unique scoring system based on the percentage change in expression/methylation between disease stages is used. Results are filtered and ranked using weighting and penalties. RESULTS: The OCDB’s utility for biomarker discovery is demonstrated with the identified target OSR2. Trends in OSR2 repression and hypermethylation with HGSC disease progression were confirmed in the browser samples and an independent cohort using bioassays. The OSR2 methylation biomarker could discriminate HGSC with high specificity (95%) and sensitivity (93.18%). CONCLUSIONS: The OCDB has been refined and validated to be an integral part of a unique biomarker discovery pipeline. It may also be used independently to aid identification of novel targets. It carries the potential to identify further biomarker assays that can reduce type I and II errors within clinical diagnostics. |
format | Online Article Text |
id | pubmed-9260242 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-92602422022-07-12 A bespoke target selection tool to guide biomarker discovery in tubo-ovarian cancer Beirne, James P. Gilmore, Alan McInerney, Caitríona E. Roddy, Aideen Glenn McCluggage, W. Harley, Ian J.G. Abdullah Alvi, M. Prise, Kevin M. McArt, Darragh G. Mullan, Paul B. Comput Struct Biotechnol J Research Article INTRODUCTION: Cancers presenting at advanced stages inherently have poor prognosis. High grade serous carcinoma (HGSC) is the most common and aggressive form of tubo-ovarian cancer. Clinical tests to accurately diagnose and monitor this condition are lacking. Hence, development of disease-specific tests are urgently required. METHODS: The molecular profile of HGSC during disease progression was investigated in a unique patient cohort. A bespoke data browser was developed to analyse gene expression and DNA methylation datasets for biomarker discovery. The Ovarian Cancer Data Browser (OCDB) is built in C# with a.NET framework using an integrated development environment of Microsoft Visual Studio and fast access files (.faf). The graphical user interface is easy to navigate between four analytical modes (gene expression; methylation; combined gene expression and methylation data; methylation clusters), with a rapid query response time. A user should first define a disease progression trend for prioritising results. Single or multiomics data are then mined to identify probes, genes and methylation clusters that exhibit the desired trend. A unique scoring system based on the percentage change in expression/methylation between disease stages is used. Results are filtered and ranked using weighting and penalties. RESULTS: The OCDB’s utility for biomarker discovery is demonstrated with the identified target OSR2. Trends in OSR2 repression and hypermethylation with HGSC disease progression were confirmed in the browser samples and an independent cohort using bioassays. The OSR2 methylation biomarker could discriminate HGSC with high specificity (95%) and sensitivity (93.18%). CONCLUSIONS: The OCDB has been refined and validated to be an integral part of a unique biomarker discovery pipeline. It may also be used independently to aid identification of novel targets. It carries the potential to identify further biomarker assays that can reduce type I and II errors within clinical diagnostics. Research Network of Computational and Structural Biotechnology 2022-06-17 /pmc/articles/PMC9260242/ /pubmed/35832628 http://dx.doi.org/10.1016/j.csbj.2022.06.016 Text en Crown Copyright © 2022 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Beirne, James P. Gilmore, Alan McInerney, Caitríona E. Roddy, Aideen Glenn McCluggage, W. Harley, Ian J.G. Abdullah Alvi, M. Prise, Kevin M. McArt, Darragh G. Mullan, Paul B. A bespoke target selection tool to guide biomarker discovery in tubo-ovarian cancer |
title | A bespoke target selection tool to guide biomarker discovery in tubo-ovarian cancer |
title_full | A bespoke target selection tool to guide biomarker discovery in tubo-ovarian cancer |
title_fullStr | A bespoke target selection tool to guide biomarker discovery in tubo-ovarian cancer |
title_full_unstemmed | A bespoke target selection tool to guide biomarker discovery in tubo-ovarian cancer |
title_short | A bespoke target selection tool to guide biomarker discovery in tubo-ovarian cancer |
title_sort | bespoke target selection tool to guide biomarker discovery in tubo-ovarian cancer |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9260242/ https://www.ncbi.nlm.nih.gov/pubmed/35832628 http://dx.doi.org/10.1016/j.csbj.2022.06.016 |
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