spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape.
Bioinformatics
; 39(5)2023 05 04.
Article
en En
| MEDLINE
| ID: mdl-37084275
ABSTRACT
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.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias de la Mama
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MicroARNs
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ARN Largo no Codificante
Tipo de estudio:
Prognostic_studies
Límite:
Female
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Humans
Idioma:
En
Año:
2023
Tipo del documento:
Article