Portal de investigación
Normal tissue content impact on the GBM molecular classification.
Fecha de publicación:
Fecha Ahead of Print:
Autores de IIS La Fe
Participantes ajenos a IIS La Fe
- Madurga, R
- Garcia-Romero, N
- Jimenez, B
- Collazo, A
- Perez-Rodriguez, F
- Hernandez-Lain, A
- Fernandez-Carballal, C
- Zanin, M
- Menasalvas, E
- Ayuso-Sacido, A
Grupos
Abstract
Molecular classification of glioblastoma has enabled a deeper understanding of the disease. The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal tissue. These samples can be misclassified preventing biological and clinical insights into the different tumor subtypes from coming to light. In this work, we present a model that tackles both the molecular classification of samples and discrimination of those with a high content of normal cells. We performed a transcriptomic in silico analysis on glioblastoma (GBM) samples (n = 810) and tested different criteria to optimize the number of genes needed for molecular classification. We used gene expression of normal brain samples (n = 555) to design an additional gene signature to detect samples with a high normal tissue content. Microdissection samples of different structures within GBM (n = 122) have been used to validate the final model. Finally, the model was tested in a cohort of 43 patients and confirmed by histology. Based on the expression of 20 genes, our model is able to discriminate samples with a high content of normal tissue and to classify the remaining ones. We have shown that taking into consideration normal cells can prevent errors in the classification and the subsequent misinterpretation of the results. Moreover, considering only samples with a low content of normal cells, we found an association between the complexity of the samples and survival for the three molecular subtypes.
Datos de la publicación
- ISSN/ISSNe:
- 1467-5463, 1477-4054
- Tipo:
- Article
- Páginas:
- -
- DOI:
- 10.1093/bib/bbaa129
- PubMed:
- 32632447
- Factor de Impacto:
- 3,032 SCImago ℠
- Cuartil:
- Q1 SCImago ℠
BRIEFINGS IN BIOINFORMATICS OXFORD UNIV PRESS
Citas Recibidas en Web of Science: 3
Documentos
- No hay documentos
Filiaciones
Keywords
- glioblastoma, gliomas, molecular classification
Financiación
Proyectos y Estudios Clínicos
EVALUACIÓN DEL ÍNDICE DE PROLIFERACIÓN KI-67 COMO PREDICTOR DE RECIDIVA EN MENINGIOMAS CEREBRALES GRADO I DE LA OMS. COMPARACIÓN CON LA ESCALA DE RESECCIÓN QUIRÚRGICA DE SIMPSON.
Investigador Principal: LUIS RICARDO PRAT ACÍN
2013_0041_CRC_PRAT . FUNDACION MUTUA MADRILEÑA . 2013
Cita
Madurga R,Garcia N,Jimenez B,Collazo A,Perez F,Hernandez A,Fernandez C,PRAT R,Zanin M,Menasalvas E,Ayuso A. Normal tissue content impact on the GBM molecular classification. Brief Bioinform. 2021. 22. (3):bbaa129. IF:13,994. (1).