1. In silico Approach for Validating and Unveiling New Applications for Prognostic Biomarkers of Endometrial Cancer
- Author
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Petr V. Nazarov, Silvia Cabrera, Eva Coll-de la Rubia, Antonio Gil-Moreno, Eva Colas, Gunnar Dittmar, Elena Martinez-Garcia, Vicente Bebia, Institut Català de la Salut, [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, and Vall d'Hebron Barcelona Hospital Campus
- Subjects
Oncology ,Cancer Research ,medicine.medical_specialty ,CPTAC ,In silico ,Otros calificadores::/diagnóstico [Otros calificadores] ,Article ,uterine cancer ,Molecular classification ,Uterine cancer ,Information Science::Computing Methodologies::Computer Simulation [INFORMATION SCIENCE] ,high-risk ,Internal medicine ,Ciencias de la información::metodologías computacionales::simulación por ordenador [CIENCIA DE LA INFORMACIÓN] ,medicine ,Other subheadings::/diagnosis [Other subheadings] ,Stage (cooking) ,Diagnosis::Prognosis [ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT] ,prognostic biomarker ,diagnóstico::pronóstico [TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS] ,RC254-282 ,Endometri - Càncer - Prognosi ,business.industry ,Endometrial cancer ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,Neoplasms::Neoplasms by Site::Urogenital Neoplasms::Genital Neoplasms, Female::Uterine Neoplasms::Endometrial Neoplasms [DISEASES] ,bioinformatics ,TCGA ,medicine.disease ,MSH6 ,Expression data ,MSH2 ,endometrial cancer ,Simulació (Medicina) ,business ,neoplasias::neoplasias por localización::neoplasias urogenitales::neoplasias de los genitales femeninos::neoplasias uterinas::neoplasias endometriales [ENFERMEDADES] - Abstract
Simple Summary 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. Abstract 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.
- Published
- 2021