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Machine learning analysis identifies genes differentiating triple negative breast cancers

Triple negative breast cancer (TNBC) is one of the most aggressive form of breast cancer (BC) with the highest mortality due to high rate of relapse, resistance, and lack of an effective treatment. Various molecular approaches have been used to target TNBC but with little success. Here, using machin...

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Autores principales: Kothari, Charu, Osseni, Mazid Abiodoun, Agbo, Lynda, Ouellette, Geneviève, Déraspe, Maxime, Laviolette, François, Corbeil, Jacques, Lambert, Jean-Philippe, Diorio, Caroline, Durocher, Francine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320018/
https://www.ncbi.nlm.nih.gov/pubmed/32591639
http://dx.doi.org/10.1038/s41598-020-67525-1
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author Kothari, Charu
Osseni, Mazid Abiodoun
Agbo, Lynda
Ouellette, Geneviève
Déraspe, Maxime
Laviolette, François
Corbeil, Jacques
Lambert, Jean-Philippe
Diorio, Caroline
Durocher, Francine
author_facet Kothari, Charu
Osseni, Mazid Abiodoun
Agbo, Lynda
Ouellette, Geneviève
Déraspe, Maxime
Laviolette, François
Corbeil, Jacques
Lambert, Jean-Philippe
Diorio, Caroline
Durocher, Francine
author_sort Kothari, Charu
collection PubMed
description Triple negative breast cancer (TNBC) is one of the most aggressive form of breast cancer (BC) with the highest mortality due to high rate of relapse, resistance, and lack of an effective treatment. Various molecular approaches have been used to target TNBC but with little success. Here, using machine learning algorithms, we analyzed the available BC data from the Cancer Genome Atlas Network (TCGA) and have identified two potential genes, TBC1D9 (TBC1 domain family member 9) and MFGE8 (Milk Fat Globule-EGF Factor 8 Protein), that could successfully differentiate TNBC from non-TNBC, irrespective of their heterogeneity. TBC1D9 is under-expressed in TNBC as compared to non-TNBC patients, while MFGE8 is over-expressed. Overexpression of TBC1D9 has a better prognosis whereas overexpression of MFGE8 correlates with a poor prognosis. Protein–protein interaction analysis by affinity purification mass spectrometry (AP-MS) and proximity biotinylation (BioID) experiments identified a role for TBC1D9 in maintaining cellular integrity, whereas MFGE8 would be involved in various tumor survival processes. These promising genes could serve as biomarkers for TNBC and deserve further investigation as they have the potential to be developed as therapeutic targets for TNBC.
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spelling pubmed-73200182020-06-30 Machine learning analysis identifies genes differentiating triple negative breast cancers Kothari, Charu Osseni, Mazid Abiodoun Agbo, Lynda Ouellette, Geneviève Déraspe, Maxime Laviolette, François Corbeil, Jacques Lambert, Jean-Philippe Diorio, Caroline Durocher, Francine Sci Rep Article Triple negative breast cancer (TNBC) is one of the most aggressive form of breast cancer (BC) with the highest mortality due to high rate of relapse, resistance, and lack of an effective treatment. Various molecular approaches have been used to target TNBC but with little success. Here, using machine learning algorithms, we analyzed the available BC data from the Cancer Genome Atlas Network (TCGA) and have identified two potential genes, TBC1D9 (TBC1 domain family member 9) and MFGE8 (Milk Fat Globule-EGF Factor 8 Protein), that could successfully differentiate TNBC from non-TNBC, irrespective of their heterogeneity. TBC1D9 is under-expressed in TNBC as compared to non-TNBC patients, while MFGE8 is over-expressed. Overexpression of TBC1D9 has a better prognosis whereas overexpression of MFGE8 correlates with a poor prognosis. Protein–protein interaction analysis by affinity purification mass spectrometry (AP-MS) and proximity biotinylation (BioID) experiments identified a role for TBC1D9 in maintaining cellular integrity, whereas MFGE8 would be involved in various tumor survival processes. These promising genes could serve as biomarkers for TNBC and deserve further investigation as they have the potential to be developed as therapeutic targets for TNBC. Nature Publishing Group UK 2020-06-26 /pmc/articles/PMC7320018/ /pubmed/32591639 http://dx.doi.org/10.1038/s41598-020-67525-1 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Kothari, Charu
Osseni, Mazid Abiodoun
Agbo, Lynda
Ouellette, Geneviève
Déraspe, Maxime
Laviolette, François
Corbeil, Jacques
Lambert, Jean-Philippe
Diorio, Caroline
Durocher, Francine
Machine learning analysis identifies genes differentiating triple negative breast cancers
title Machine learning analysis identifies genes differentiating triple negative breast cancers
title_full Machine learning analysis identifies genes differentiating triple negative breast cancers
title_fullStr Machine learning analysis identifies genes differentiating triple negative breast cancers
title_full_unstemmed Machine learning analysis identifies genes differentiating triple negative breast cancers
title_short Machine learning analysis identifies genes differentiating triple negative breast cancers
title_sort machine learning analysis identifies genes differentiating triple negative breast cancers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7320018/
https://www.ncbi.nlm.nih.gov/pubmed/32591639
http://dx.doi.org/10.1038/s41598-020-67525-1
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