The prognostic relevance of a gene expression signature in MRI-defined highly vascularized glioblastoma

[EN] Background The vascular heterogeneity of glioblastomas (GB) remains an important area of research, since tumor progression and patient prognosis are closely tied to this feature. With this study, we aim to identify gene expression profiles associated with MRI-defined tumor vascularity and to in...

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Detalles Bibliográficos
Autores: Montosa-Micó, Víctor, Gil-Terrón-Rodríguez, Francisco Javier|||0000-0002-3536-6462, Gómez-Mahiques, María, López-Mateu, Carlos, Garcia-Gomez, Juan M, Fuster García, Elíes|||0000-0002-0716-8960, Álvarez-Torres, María del Mar, Burgos-Panadero, Rebeca
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/213317
Acceso en línea:https://riunet.upv.es/handle/10251/213317
Access Level:acceso abierto
Palabra clave:High-grade glioma
Glioblastoma
Relative cerebral blood volume
Tumor vascularity
RNA-seq
ONCOhabitats
Biomarker
Magnetic resonance imaging (MRI)
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Descripción
Sumario:[EN] Background The vascular heterogeneity of glioblastomas (GB) remains an important area of research, since tumor progression and patient prognosis are closely tied to this feature. With this study, we aim to identify gene expression profiles associated with MRI-defined tumor vascularity and to investigate its relationship with patient prognosis. Methods The study employed MRI parameters calculated with DSC Perfusion Quantification of ONCOhabitats glioma analysis software and RNA-seq data from the TCGA-GBM project dataset. In our study, we had a total of 147 RNA-seq samples, which 15 of them also had MRI parameter information. We analyzed the gene expression profiles associated with MRI-defined tumor vascularity using differential gene expression analysis and performed Log-rank tests to assess the correlation between the identified genes and patient prognosis. Results The findings of our research reveal a set of 21 overexpressed genes associated with the high vascularity pattern. Notably, several of these overexpressed genes have been previously implicated in worse prognosis based on existing literature. Our log-rank test further validates that the collective upregulation of these genes is indeed correlated with an unfavorable prognosis. This set of genes includes a variety of molecules, such as cytokines, receptors, ligands, and other molecules with diverse functions. Conclusions Our findings suggest that the set of 21 overexpressed genes in the High Vascularity group could potentially serve as prognostic markers for GB patients. These results highlight the importance of further investigating the relationship between the molecules such as cytokines or receptors underlying the vascularity in GB and its observation through MRI and developing targeted therapies for this aggressive disease.