In silico Approach for Validating and Unveiling New Applications for Prognostic Biomarkers of Endometrial Cancer
Coll de la Rubia, Eva (Hospital Universitari Vall d'Hebron. Institut de Recerca)
Martinez-Garcia, Elena (Luxembourg Institute of Health)
Dittmar, Gunnar (Luxembourg Institute of Health)
Nazarov, Petr V. (Luxembourg Institute of Health)
Bebia, Vicente (Hospital Universitari Vall d'Hebron)
Cabrera Díaz, Silvia (Hospital Universitari Vall d'Hebron)
Gil-Moreno, Antonio 1965- (Hospital Universitari Vall d'Hebron)
Colás Ortega, Eva (Hospital Universitari Vall d'Hebron. Institut de Recerca)
Universitat Autònoma de Barcelona

Date:	2021
Abstract:	Endometrial cancer (EC) mortality is directly associated with the presence of poor prognostic factors. Molecular prognostic factors have been identified, but none are used in clinical practice due to lack of validation studies. This study aims to validate a set of 255 prognostic biomarkers previously identified in an extensive literature review and explore new prognostic applications by analyzing them in The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases. A total of 30 biomarkers were validated and associated to a histological type (n = 15), histological grade (n = 6), FIGO stage (n = 1), molecular classification (n = 16), overall survival (n = 11), and recurrence-free survival (n = 5). Our results encourage further studies of understudied biomarkers such as TPX2, and validates already broadly studied biomarkers such as MSH6, MSH2, or L1CAM, among others. Finally, our results present a significant step to advance the quest for biomarkers to accurately assess the risk of EC patients. Endometrial 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.
Grants:	Instituto de Salud Carlos III PI17/02155 Instituto de Salud Carlos III PI20/00644 Instituto de Salud Carlos III IFI19/00029 Ministerio de Ciencia, Innovación y Universidades RTC-2017-6261-1 Ministerio de Economía y Competitividad CB16/12/00328 Generalitat de Catalunya 2017SGR1661
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Endometrial cancer ; Prognostic biomarker ; Uterine cancer ; High-risk ; Bioinformatics ; CPTAC ; TCGA
Published in:	Cancers, Vol. 13 (october 2021) , ISSN 2072-6694

DOI: 10.3390/cancers13205052
PMID: 34680205

15 p, 1.9 MB

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