دورية أكاديمية
Normal tissue content impact on the GBM molecular classification
| العنوان: | Normal tissue content impact on the GBM molecular classification |
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| المؤلفون: | Madurga, Rodrigo, García-Romero, Noemí, Jiménez, Beatriz, Collazo, Ana, Pérez-Rodríguez, Francisco, Hernández-Laín, Aurelio, Fernández-Carballal, Carlos, Prat-Acín, Ricardo, Zanin, Massimiliano, Menasalvas, Ernestina, Ayuso-Sacido, Angel |
| المساهمون: | Instituto de Salud Carlos III, Ministerio de Economía y Competitividad (España), Agencia Estatal de Investigación (España), BioBanco VIH HGM, Ministerio de Ciencia, Innovación y Universidades (España) |
| بيانات النشر: | Oxford University Press |
| سنة النشر: | 2021 |
| المجموعة: | Digital.CSIC (Consejo Superior de Investigaciones Científicas / Spanish National Research Council) |
| مصطلحات موضوعية: | Glioblastoma, Molecular classification, Gliomas |
| الوصف: | 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 The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal tissue.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. ; The Fondo de Investigaciones Sanitarias (FIS) (PI17-01489); the Miguel Servet Program (CP11/00147); del Instituto de Salud Carlos III (AAS) and the Ministerio de Economía y Competitividad–FEDERER (RTC-2016-4990-1); Convocatoria de ayudas para la contratación de investigadores predoctorales e investigadores postdoctorales cofinanciadas por Fondo Social Europeo a través del Programa Operativo de Empleo Juvenil y la ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 1467-5463 1477-4054 |
| العلاقة: | #PLACEHOLDER_PARENT_METADATA_VALUE#; info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/RTC-2016-4990-1; Postprint; https://doi.org/10.1093/bib/bbaa129Test; Sí; Briefings in Bioinformatics 22(3): bbaa129 (2020); http://hdl.handle.net/10261/218363Test; http://dx.doi.org/10.13039/501100011033Test; http://dx.doi.org/10.13039/501100003329Test; http://dx.doi.org/10.13039/501100004587Test |
| DOI: | 10.1093/bib/bbaa129 |
| DOI: | 10.13039/501100011033 |
| DOI: | 10.13039/501100003329 |
| DOI: | 10.13039/501100004587 |
| الإتاحة: | https://doi.org/10.1093/bib/bbaa129Test https://doi.org/10.13039/501100011033Test https://doi.org/10.13039/501100003329Test https://doi.org/10.13039/501100004587Test http://hdl.handle.net/10261/218363Test |
| حقوق: | open |
| رقم الانضمام: | edsbas.AB7154EF |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 14675463 14774054 |
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| DOI: | 10.1093/bib/bbaa129 |