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dc.contributor.authorMosquera Orgueira, Adrián
dc.contributor.authorPeleteiro Raindo, Andrés
dc.contributor.authorDíaz Arias, José 
dc.contributor.authorAntelo Rodríguez, Beatriz
dc.contributor.authorLópez Riñón, M.
dc.contributor.authorCerchione, C.
dc.contributor.authorde la Fuente Burguera, A.
dc.contributor.authorGonzález Pérez, Marta Sonia 
dc.contributor.authorMartinelli, G.
dc.contributor.authorMontesinos Fernández, P.
dc.contributor.authorPérez Encinas, Manuel Mateo 
dc.date.accessioned2025-08-12T11:28:02Z
dc.date.available2025-08-12T11:28:02Z
dc.date.issued2022
dc.identifier.citationMosquera Orgueira A, Peleteiro Raíndo A, Díaz Arias JÁ, Antelo Rodríguez B, López Riñón M, Cerchione C, et al. Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia. Frontiers in Oncology. 2022;12.
dc.identifier.issn2234-943X
dc.identifier.otherhttps://sergas.portalcientifico.es//documentos/635da1f2f50cf01a7960fbb7
dc.identifier.urihttp://hdl.handle.net/20.500.11940/20380
dc.description.abstractRisk stratification in acute myeloid leukemia (AML) has been extensively improved thanks to the incorporation of recurrent cytogenomic alterations into risk stratification guidelines. However, mortality rates among fit patients assigned to low or intermediate risk groups are still high. Therefore, significant room exists for the improvement of AML prognostication. In a previous work, we presented the Stellae-123 gene expression signature, which achieved a high accuracy in the prognostication of adult patients with AML. Stellae-123 was particularly accurate to restratify patients bearing high-risk mutations, such as ASXL1, RUNX1 and TP53. The intention of the present work was to evaluate the prognostic performance of Stellae-123 in external cohorts using RNAseq technology. For this, we evaluated the signature in 3 different AML cohorts (2 adult and 1 pediatric). Our results indicate that the prognostic performance of the Stellae-123 signature is reproducible in the 3 cohorts of patients. Additionally, we evidenced that the signature was superior to the European LeukemiaNet 2017 and the pediatric clinical risk scores in the prediction of survival at most of the evaluated time points. Furthermore, integration with age substantially enhanced the accuracy of the model. In conclusion, Stellae-123 is a reproducible machine learning algorithm based on a gene expression signature with promising utility in the field of AML.en
dc.description.sponsorshipWe'd like to thank the Super computing Center of Galicia(CESGA) for their support. The content of this paper is part ofthe doctoral thesis of APR to obtain a PhD at the Department ofMedicine, University of Santiago de Compostela.
dc.language.isoeng
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleEvaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia
dc.typeArticle
dc.rights.licenseAtribución 4.0 Internacional*
dc.authorsophosMosquera Orgueira, A.
dc.authorsophosPeleteiro Raíndo, A.
dc.authorsophosDíaz Arias, J.Á.
dc.authorsophosAntelo Rodríguez, B.
dc.authorsophosLópez Riñón, M.
dc.authorsophosCerchione, C.
dc.authorsophosde la Fuente Burguera, A.
dc.authorsophosGonzález Pérez, M.S.
dc.authorsophosMartinelli, G.
dc.authorsophosMontesinos Fernández, P.
dc.authorsophosPérez Encinas, M.M.
dc.identifier.doi10.3389/FONC.2022.968340
dc.identifier.sophos635da1f2f50cf01a7960fbb7
dc.journal.titleFrontiers in Oncologyen
dc.relation.projectIDSuper computing Center of Galicia(CESGA)
dc.relation.publisherversionhttps://doi.org/10.3389/fonc.2022.968340
dc.rights.accessRightsopenAccess*
dc.subject.keywordAS Santiago AP
dc.subject.keywordCHUS
dc.subject.keywordIDIS
dc.typefidesArtículo Científico (incluye Original, Original breve, Revisión Sistemática y Meta-análisis)
dc.typesophosArtículo Original
dc.volume.number12


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