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Bayesian networks established functional differences between breast cancer subtypes

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dc.contributor.authorTrilla Fuertes, Lucía
dc.contributor.authorGámez Pozo, Ángelo
dc.contributor.authorLópez Vacas, Rocío
dc.contributor.authorLópez Camacho, Elena
dc.contributor.authorPrado Vázquez, Guillermo
dc.contributor.authorZapater Moros, Andrea
dc.contributor.authorDíaz Almirón, Mariana
dc.contributor.authorFerrer Gómez, María
dc.contributor.authorNanni, Paolo
dc.contributor.authorZamora Auñón, Pilar
dc.contributor.authorEspinosa, Enrique
dc.contributor.authorMaín, Paloma
dc.contributor.authorFresno Vara, Juan Ángel
dc.contributor.authorMartín Arevalillo, Jorge
dc.contributor.authorNavarro Veguillas, Hilario
dc.date.accessioned2024-05-20T11:24:45Z
dc.date.available2024-05-20T11:24:45Z
dc.date.issued2020-06-11
dc.description.abstractBreast cancer is a heterogeneous disease. In clinical practice, tumors are classified as hormonal receptor positive, Her2 positive and triple negative tumors. In previous works, our group defined a new hormonal receptor positive subgroup, the TN-like subtype, which had a prognosis and a molecular profile more similar to triple negative tumors. In this study, proteomics and Bayesian networks were used to characterize protein relationships in 96 breast tumor samples. Components obtained by these methods had a clear functional structure. The analysis of these components suggested differences in processes such as mitochondrial function or extracellular matrix between breast cancer subtypes, including our new defined subtype TN-like. In addition, one of the components, mainly related with extracellular matrix processes, had prognostic value in this cohort. Functional approaches allow to build hypotheses about regulatory mechanisms and to establish new relationships among proteins in the breast cancer context.en
dc.description.versionversión publicada
dc.identifier.doihttp://doi.org/10.1371/journal.pone.0234752
dc.identifier.urihttps://hdl.handle.net/20.500.14468/11925
dc.language.isoen
dc.publisherPLOS
dc.relation.centerFacultad de Ciencias
dc.relation.departmentEstadística, Investigación Operativa y Cálculo Numérico
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.titleBayesian networks established functional differences between breast cancer subtypeses
dc.typejournal articleen
dc.typeartículoes
dspace.entity.typePublication
relation.isAuthorOfPublicationea1c092a-eceb-49c8-abb7-d4d52f53930a
relation.isAuthorOfPublicationb4ef2ce4-140a-4dfd-b87f-363a7a9136e5
relation.isAuthorOfPublication.latestForDiscoveryea1c092a-eceb-49c8-abb7-d4d52f53930a
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