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dc.contributorVall d'Hebron Barcelona Hospital Campus
dc.contributor.authorColl De la Rubia, Eva
dc.contributor.authorMartinez-Garcia, Elena
dc.contributor.authorDittmar, Gunnar
dc.contributor.authorNazarov, Petr V.
dc.contributor.authorBebia Conesa, Vicente
dc.contributor.authorCabrera Diaz, Silvia
dc.contributor.authorGil Moreno, Antonio
dc.contributor.authorColas Ortega, Eva
dc.date.accessioned2022-05-06T12:31:53Z
dc.date.available2022-05-06T12:31:53Z
dc.date.issued2021-10
dc.identifier.citationColl-de la Rubia E, Martinez-Garcia E, Dittmar G, Nazarov PV, Bebia V, Cabrera S, et al. In silico Approach for Validating and Unveiling New Applications for Prognostic Biomarkers of Endometrial Cancer. Cancers. 2021 Oct;13(20):5052.
dc.identifier.issn2072-6694
dc.identifier.urihttp://hdl.handle.net/11351/7489
dc.descriptionBioinformatics; Endometrial cancer; Prognostic biomarker
dc.description.abstractEndometrial cancer (EC) mortality is directly associated with the presence of prognostic factors. Current stratification systems are not accurate enough to predict the outcome of patients. Therefore, identifying more accurate prognostic EC biomarkers is crucial. We aimed to validate 255 prognostic biomarkers identified in multiple studies and explore their prognostic application by analyzing them in TCGA and CPTAC datasets. We analyzed the mRNA and proteomic expression data to assess the statistical prognostic performance of the 255 proteins. Significant biomarkers related to overall survival (OS) and recurrence-free survival (RFS) were combined and signatures generated. A total of 30 biomarkers were associated either to one or more of the following prognostic factors: histological type (n = 15), histological grade (n = 6), FIGO stage (n = 1), molecular classification (n = 16), or they were associated to OS (n = 11), and RFS (n = 5). A prognostic signature composed of 11 proteins increased the accuracy to predict OS (AUC = 0.827). The study validates and identifies new potential applications of 30 proteins as prognostic biomarkers and suggests to further study under-studied biomarkers such as TPX2, and confirms already used biomarkers such as MSH6, MSH2, or L1CAM. These results are expected to advance the quest for biomarkers to accurately assess the risk of EC patients.
dc.language.isoeng
dc.publisherMDPI
dc.relation.ispartofseriesCancers;13(20)
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceScientia
dc.subjectSimulació (Medicina)
dc.subjectEndometri - Càncer - Prognosi
dc.subject.meshEndometrial Neoplasms
dc.subject.mesh/diagnosis
dc.subject.meshPrognosis
dc.subject.meshComputer Simulation
dc.titleIn silico Approach for Validating and Unveiling New Applications for Prognostic Biomarkers of Endometrial Cancer
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.3390/cancers13205052
dc.subject.decsneoplasias endometriales
dc.subject.decs/diagnóstico
dc.subject.decspronóstico
dc.subject.decssimulación por ordenador
dc.relation.publishversionhttps://doi.org/10.3390/cancers13205052
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.audienceProfessionals
dc.contributor.organismesInstitut Català de la Salut
dc.contributor.authoraffiliation[Coll-de la Rubia E, Colás E] Grup de Recerca Biomèdica en Ginecologia, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. Universitat Autònoma de Barcelona, Bellaterra, Spain. CIBERONC Barcelona, Spain. [Martinez-Garcia E, Dittmar G, Nazarov PV] Luxembourg Institute of Health, L-1445 Strassen, Luxembourg. [Bebia V] Servei de Ginecologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. CIBERONC, Barcelona, Spain. [Cabrera S, Gil-Moreno A] Grup de Recerca Biomèdica en Ginecologia, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. Universitat Autònoma de Barcelona, Bellaterra, Spain. CIBERONC, Barcelona, Spain. Servei de Ginecologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. CIBERONC, Barcelona, Spain
dc.identifier.pmid34680205
dc.identifier.wos000717165500001
dc.relation.projectidinfo:eu-repo/grantAgreement/ES/PE2013-2016/PI17%2F02155
dc.relation.projectidinfo:eu-repo/grantAgreement/ES/PE2017-2020/PI20%2F00644
dc.relation.projectidinfo:eu-repo/grantAgreement/ES/PE2017-2020/IFI19%2F00029
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess


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