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Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks

Non-coding micro RNAs (miRNAs) dysregulation seems to play an important role in the pathways involved in breast cancer occurrence and progression. In different studies, opposite functions may be assigned to the same miRNA, either promoting the disease or protecting from it. Our research tackles the...

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Autores principales: Miglioli, Cesare, Bakalli, Gaetan, Orso, Samuel, Karemera, Mucyo, Molinari, Roberto, Guerrier, Stéphane, Mili, Nabil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956684/
https://www.ncbi.nlm.nih.gov/pubmed/35338170
http://dx.doi.org/10.1038/s41598-022-08737-5
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author Miglioli, Cesare
Bakalli, Gaetan
Orso, Samuel
Karemera, Mucyo
Molinari, Roberto
Guerrier, Stéphane
Mili, Nabil
author_facet Miglioli, Cesare
Bakalli, Gaetan
Orso, Samuel
Karemera, Mucyo
Molinari, Roberto
Guerrier, Stéphane
Mili, Nabil
author_sort Miglioli, Cesare
collection PubMed
description Non-coding micro RNAs (miRNAs) dysregulation seems to play an important role in the pathways involved in breast cancer occurrence and progression. In different studies, opposite functions may be assigned to the same miRNA, either promoting the disease or protecting from it. Our research tackles the following issues: (i) why aren’t there any concordant findings in many research studies regarding the role of miRNAs in the progression of breast cancer? (ii) could a miRNA have either an activating effect or an inhibiting one in cancer progression according to the other miRNAs with which it interacts? For this purpose, we analyse the AHUS dataset made available on the ArrayExpress platform by Haakensen et al. The breast tissue specimens were collected over 7 years between 2003 and 2009. miRNA-expression profiling was obtained for 55 invasive carcinomas and 70 normal breast tissue samples. Our statistical analysis is based on a recently developed model and feature selection technique which, instead of selecting a single model (i.e. a unique combination of miRNAs), delivers a set of models with equivalent predictive capabilities that allows to interpret and visualize the interaction of these features. As a result, we discover a set of 112 indistinguishable models (in a predictive sense) each with 4 or 5 miRNAs. Within this set, by comparing the model coefficients, we are able to identify three classes of miRNA: (i) oncogenic miRNAs; (ii) protective miRNAs; (iii) undefined miRNAs which can play both an oncogenic and a protective role according to the network with which they interact. These results shed new light on the biological action of miRNAs in breast cancer and may contribute to explain why, in some cases, different studies attribute opposite functions to the same miRNA.
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spelling pubmed-89566842022-03-28 Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks Miglioli, Cesare Bakalli, Gaetan Orso, Samuel Karemera, Mucyo Molinari, Roberto Guerrier, Stéphane Mili, Nabil Sci Rep Article Non-coding micro RNAs (miRNAs) dysregulation seems to play an important role in the pathways involved in breast cancer occurrence and progression. In different studies, opposite functions may be assigned to the same miRNA, either promoting the disease or protecting from it. Our research tackles the following issues: (i) why aren’t there any concordant findings in many research studies regarding the role of miRNAs in the progression of breast cancer? (ii) could a miRNA have either an activating effect or an inhibiting one in cancer progression according to the other miRNAs with which it interacts? For this purpose, we analyse the AHUS dataset made available on the ArrayExpress platform by Haakensen et al. The breast tissue specimens were collected over 7 years between 2003 and 2009. miRNA-expression profiling was obtained for 55 invasive carcinomas and 70 normal breast tissue samples. Our statistical analysis is based on a recently developed model and feature selection technique which, instead of selecting a single model (i.e. a unique combination of miRNAs), delivers a set of models with equivalent predictive capabilities that allows to interpret and visualize the interaction of these features. As a result, we discover a set of 112 indistinguishable models (in a predictive sense) each with 4 or 5 miRNAs. Within this set, by comparing the model coefficients, we are able to identify three classes of miRNA: (i) oncogenic miRNAs; (ii) protective miRNAs; (iii) undefined miRNAs which can play both an oncogenic and a protective role according to the network with which they interact. These results shed new light on the biological action of miRNAs in breast cancer and may contribute to explain why, in some cases, different studies attribute opposite functions to the same miRNA. Nature Publishing Group UK 2022-03-25 /pmc/articles/PMC8956684/ /pubmed/35338170 http://dx.doi.org/10.1038/s41598-022-08737-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Miglioli, Cesare
Bakalli, Gaetan
Orso, Samuel
Karemera, Mucyo
Molinari, Roberto
Guerrier, Stéphane
Mili, Nabil
Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks
title Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks
title_full Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks
title_fullStr Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks
title_full_unstemmed Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks
title_short Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks
title_sort evidence of antagonistic predictive effects of mirnas in breast cancer cohorts through data-driven networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956684/
https://www.ncbi.nlm.nih.gov/pubmed/35338170
http://dx.doi.org/10.1038/s41598-022-08737-5
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