Dataset Information


Transcriptome profiling-based identification of prognostic subtypes and multi-omics signatures of glioblastoma.

ABSTRACT: Glioblastoma (GBM) is a lethal tumor, but few biomarkers and molecular subtypes predicting prognosis are available. This study was aimed to identify prognostic subtypes and multi-omics signatures for GBM. Using oncopression and TCGA-GBM datasets, we identified 80 genes most associated with GBM prognosis using correlations between gene expression levels and overall survival of patients. The prognostic score of each sample was calculated using these genes, followed by assigning three prognostic subtypes. This classification was validated in two independent datasets (REMBRANDT and Severance). Functional annotation revealed that invasion- and cell cycle-related gene sets were enriched in poor and favorable group, respectively. The three GBM subtypes were therefore named invasive (poor), mitotic (favorable), and intermediate. Interestingly, invasive subtype showed increased invasiveness, and MGMT methylation was enriched in mitotic subtype, indicating need for different therapeutic strategies according to prognostic subtypes. For clinical convenience, we also identified genes that best distinguished the invasive and mitotic subtypes. Immunohistochemical staining showed that markedly higher expression of PDPN in invasive subtype and of TMEM100 in mitotic subtype (P?


PROVIDER: S-EPMC6646357 | BioStudies | 2019-01-01

REPOSITORIES: biostudies

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