000166013 001__ 166013 000166013 005__ 20260116163027.0 000166013 0247_ $$2doi$$a10.1371/journal.pone.0326442 000166013 0248_ $$2sideral$$a147444 000166013 037__ $$aART-2025-147444 000166013 041__ $$aeng 000166013 100__ $$aLópez-Díaz, Gloria 000166013 245__ $$aLongitudinal biomarker progression and validation for predicting operational tolerance in a prospective multicenter liver transplantation immunosuppression withdrawal trial 000166013 260__ $$c2025 000166013 5060_ $$aAccess copy available to the general public$$fUnrestricted 000166013 5203_ $$aLiver transplantation (LT) is a life-saving treatment for end-stage liver disease, but long-term immunosuppression (IS) is associated with significant side effects. Achieving operational tolerance (OT), where the graft is accepted without IS, remains a critical goal. Biomarkers play a pivotal role in understanding the complex mechanisms of OT, enabling personalized treatment strategies and improving patient outcomes. Additionally, machine learning techniques offer powerful tools for identifying predictive biomarkers and optimizing IS withdrawal protocols. This multicenter trial aimed to investigate the longitudinal evolution of genetic biomarkers during IS withdrawal and validate their predictive value for OT in LT recipients. A prospective, multicenter IS withdrawal trial was conducted with 91 LT patients. Tolerant (TOL) and non-tolerant (non-TOL) patients were compared, and longitudinal blood and liver samples were collected to analyze biomarkers. Generalized Additive Mixed Models (GAMMs) and logistic algorithms were employed to assess biomarker associations and predict OT. Of the 45 patients who completed the trial, 17 (37.8%) achieved OT. Molecular biomarker analysis revealed significant differences between TOL and non-TOL groups. Non-TOL patients exhibited higher baseline methylation of the FOXP3 regulatory T cell-specific demethylated region (TSDR) in whole blood. Longitudinal analysis showed distinct patterns in FOXP3, SENP6, miR31, and miR95 expression between groups. Notably, FOXP3 expression followed a U-shaped trajectory in TOL patients, decreasing during IS withdrawal and increasing post-withdrawal. Machine learning identified several key predictive biomarkers for OT. This study confirms the association between FOXP3 TSDR methylation and OT in LT patients and identifies FEM1C, miR31 and TFRC as promising predictive biomarkers. These findings highlight the potential for personalized IS withdrawal strategies, though further validation in larger cohorts is needed before clinical application. 000166013 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es 000166013 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000166013 700__ $$aSánchez-Lorencio, María Isabel 000166013 700__ $$aIñarrairaegui, Mercedes 000166013 700__ $$aGonzález-Diéguez, María Luisa 000166013 700__ $$aCadahía, Valle 000166013 700__ $$aOtero-Ferreiro, Alejandra 000166013 700__ $$aVázquez-Millán, María Ángeles 000166013 700__ $$aRomero-Cristóbal, Mario 000166013 700__ $$aSalcedo, Magdalena 000166013 700__ $$0(orcid)0000-0003-4672-8083$$aLorente-Pérez, Sara$$uUniversidad de Zaragoza 000166013 700__ $$aSánchez-Antolín, Gloria 000166013 700__ $$ade la Peña, Jesús 000166013 700__ $$aRamírez, Pablo 000166013 700__ $$aPata, María P. 000166013 700__ $$aBaroja-Mazo, Alberto 000166013 700__ $$aHerrero, José I. 000166013 700__ $$aPons, José Antonio 000166013 7102_ $$11007$$2610$$aUniversidad de Zaragoza$$bDpto. Medicina, Psiqu. y Derm.$$cArea Medicina 000166013 773__ $$g20, 12 (2025), e0326442 [19 pp.]$$pPLoS One$$tPLoS ONE$$x1932-6203 000166013 8564_ $$s1661921$$uhttps://zaguan.unizar.es/record/166013/files/texto_completo.pdf$$yVersión publicada 000166013 8564_ $$s2674245$$uhttps://zaguan.unizar.es/record/166013/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000166013 909CO $$ooai:zaguan.unizar.es:166013$$particulos$$pdriver 000166013 951__ $$a2026-01-16-14:54:53 000166013 980__ $$aARTICLE