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

Junseong Park, Jin Kyoung Shim, Seon Jin Yoon, SeHoon Kim, Jong Hee Chang, Seok-Gu Kang

Research output: Contribution to journalArticle

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 < 0.001). We expect that this transcriptome-based classification, with multi-omics signatures and biomarkers, can improve molecular understanding of GBM, ultimately leading to precise stratification of patients for therapeutic interventions.

Original languageEnglish
Article number10555
JournalScientific reports
Volume9
Issue number1
DOIs
Publication statusPublished - 2019 Dec 1

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Gene Expression Profiling
Glioblastoma
Genes
cdc Genes
Tumor Biomarkers
Transcriptome
Methylation
Biomarkers
Staining and Labeling
Gene Expression
Survival
Therapeutics

All Science Journal Classification (ASJC) codes

  • General

Cite this

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title = "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 < 0.001). We expect that this transcriptome-based classification, with multi-omics signatures and biomarkers, can improve molecular understanding of GBM, ultimately leading to precise stratification of patients for therapeutic interventions.",
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Transcriptome profiling-based identification of prognostic subtypes and multi-omics signatures of glioblastoma. / Park, Junseong; Shim, Jin Kyoung; Yoon, Seon Jin; Kim, SeHoon; Chang, Jong Hee; Kang, Seok-Gu.

In: Scientific reports, Vol. 9, No. 1, 10555, 01.12.2019.

Research output: Contribution to journalArticle

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