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spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape

MOTIVATION: Cancer is one of the leading causes of death worldwide. Despite significant improvements in prevention and treatment, mortality remains high for many cancer types. Hence, innovative methods that use molecular data to stratify patients and identify biomarkers are needed. Promising biomark...

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Autores principales: Boniolo, Fabio, Hoffmann, Markus, Roggendorf, Norman, Tercan, Bahar, Baumbach, Jan, Castro, Mauro A A, Robertson, A Gordon, Saur, Dieter, List, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220456/
https://www.ncbi.nlm.nih.gov/pubmed/37084275
http://dx.doi.org/10.1093/bioinformatics/btad276
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author Boniolo, Fabio
Hoffmann, Markus
Roggendorf, Norman
Tercan, Bahar
Baumbach, Jan
Castro, Mauro A A
Robertson, A Gordon
Saur, Dieter
List, Markus
author_facet Boniolo, Fabio
Hoffmann, Markus
Roggendorf, Norman
Tercan, Bahar
Baumbach, Jan
Castro, Mauro A A
Robertson, A Gordon
Saur, Dieter
List, Markus
author_sort Boniolo, Fabio
collection PubMed
description MOTIVATION: Cancer is one of the leading causes of death worldwide. Despite significant improvements in prevention and treatment, mortality remains high for many cancer types. Hence, innovative methods that use molecular data to stratify patients and identify biomarkers are needed. Promising biomarkers can also be inferred from competing endogenous RNA (ceRNA) networks that capture the gene–miRNA gene regulatory landscape. Thus far, the role of these biomarkers could only be studied globally but not in a sample-specific manner. To mitigate this, we introduce spongEffects, a novel method that infers subnetworks (or modules) from ceRNA networks and calculates patient- or sample-specific scores related to their regulatory activity. RESULTS: We show how spongEffects can be used for downstream interpretation and machine learning tasks such as tumor classification and for identifying subtype-specific regulatory interactions. In a concrete example of breast cancer subtype classification, we prioritize modules impacting the biology of the different subtypes. In summary, spongEffects prioritizes ceRNA modules as biomarkers and offers insights into the miRNA regulatory landscape. Notably, these module scores can be inferred from gene expression data alone and can thus be applied to cohorts where miRNA expression information is lacking. AVAILABILITY AND IMPLEMENTATION: https://bioconductor.org/packages/devel/bioc/html/SPONGE.html.
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spelling pubmed-102204562023-05-28 spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape Boniolo, Fabio Hoffmann, Markus Roggendorf, Norman Tercan, Bahar Baumbach, Jan Castro, Mauro A A Robertson, A Gordon Saur, Dieter List, Markus Bioinformatics Original Paper MOTIVATION: Cancer is one of the leading causes of death worldwide. Despite significant improvements in prevention and treatment, mortality remains high for many cancer types. Hence, innovative methods that use molecular data to stratify patients and identify biomarkers are needed. Promising biomarkers can also be inferred from competing endogenous RNA (ceRNA) networks that capture the gene–miRNA gene regulatory landscape. Thus far, the role of these biomarkers could only be studied globally but not in a sample-specific manner. To mitigate this, we introduce spongEffects, a novel method that infers subnetworks (or modules) from ceRNA networks and calculates patient- or sample-specific scores related to their regulatory activity. RESULTS: We show how spongEffects can be used for downstream interpretation and machine learning tasks such as tumor classification and for identifying subtype-specific regulatory interactions. In a concrete example of breast cancer subtype classification, we prioritize modules impacting the biology of the different subtypes. In summary, spongEffects prioritizes ceRNA modules as biomarkers and offers insights into the miRNA regulatory landscape. Notably, these module scores can be inferred from gene expression data alone and can thus be applied to cohorts where miRNA expression information is lacking. AVAILABILITY AND IMPLEMENTATION: https://bioconductor.org/packages/devel/bioc/html/SPONGE.html. Oxford University Press 2023-04-21 /pmc/articles/PMC10220456/ /pubmed/37084275 http://dx.doi.org/10.1093/bioinformatics/btad276 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Paper
Boniolo, Fabio
Hoffmann, Markus
Roggendorf, Norman
Tercan, Bahar
Baumbach, Jan
Castro, Mauro A A
Robertson, A Gordon
Saur, Dieter
List, Markus
spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape
title spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape
title_full spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape
title_fullStr spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape
title_full_unstemmed spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape
title_short spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape
title_sort spongeffects: cerna modules offer patient-specific insights into the mirna regulatory landscape
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220456/
https://www.ncbi.nlm.nih.gov/pubmed/37084275
http://dx.doi.org/10.1093/bioinformatics/btad276
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