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

Article Article scientifique Article Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks eng 2022-03-25 author Miglioli Cesare 0000-0002-1181-5016 University of Geneva, Geneva School of Economics and Management, Geneva, 1205, Switzerland. cesare.miglioli@unige.ch. 835414 author Bakalli Gaetan Auburn University, Department of Mathematics and Statistics, Auburn, AL, 36849, USA. 800347 author Orso Samuel 0000-0002-2064-3586 University of Geneva, Geneva School of Economics and Management, Geneva, 1205, Switzerland. 670246 Services communs du département des sciences de la terre (DETCO) author Karemera Mucyo Auburn University, Department of Mathematics and Statistics, Auburn, AL, 36849, USA. 555859 Services communs du département des sciences de la terre (DETCO) author Molinari Roberto Auburn University, Department of Mathematics and Statistics, Auburn, AL, 36849, USA. author Guerrier Stéphane University of Geneva, Geneva School of Economics and Management, Geneva, 1205, Switzerland.,University of Geneva, Faculty of Science, Geneva, 1211, Switzerland. 625357 Services communs du département des sciences de la terre (DETCO) author Mili Nabil University of Lausanne, Lausanne, 1015, Switzerland. nabil.mili@unil.ch. 63021 16 5166 10.1038/s41598-022-08737-5 35338170 2045-2322 PMC8956684 https://www.nature.com/articles/s41598-022-08737-5 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. Dewey 550 Dewey 310 Swiss National Science Foundation New Challenges for Statistical Methods in Large and Complex Data Settings: Analysis of Dependent Data and Model Selection 176843 CH Innosuisse - Schweizerische Agentur für Innovationsförderung 37308.1 IP-ENG 117 Research Center for Statistics S5 42 Département des sciences de la Terre 154 false Scientific reports 12 1 d1465254-9b51-43cf-81d3-7a4bd21e6ee8 Article (Published version) Public Article_scientifique_from_crossref.pdf 1838243 application/pdf CC BY-4.0 faa0c00aa66e613a42461252b0449673460a184f5f6ce0139eb3b95ee5083392 openAccess Faculté d'économie et de management / Research Center for Statistics Faculté des sciences / Section des sciences de la Terre et de l'Environnement / Département des sciences de la Terre

Title	Published in	Year	Views	Downloads

Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks	Scientific reports	2022	2	8
Domain-Tailored Approaches to Statistical Learning		2021	732	14

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