The integrative metabolomic-transcriptomic landscape of glioblastome multiforme

Dieter Henrik Heiland; Jakob Wörner; Jan Gerrit Haaker; Daniel Delev; Nils Pompe; Bianca Mercas; Pamela Franco; Annette Gäbelein; Sabrina Heynckes; Dietmar Pfeifer; Stefan Weber; Irina Mader; Oliver Schnell

doi:10.18632/oncotarget.16544

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Research Papers:

The integrative metabolomic-transcriptomic landscape of glioblastome multiforme

Dieter Henrik Heiland _, Jakob Wörner, Jan Gerrit Haaker, Daniel Delev, Nils Pompe, Bianca Mercas, Pamela Franco, Annette Gäbelein, Sabrina Heynckes, Dietmar Pfeifer, Stefan Weber, Irina Mader and Oliver Schnell

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Oncotarget. 2017; 8:49178-49190. https://doi.org/10.18632/oncotarget.16544

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Abstract

Dieter Henrik Heiland1,5, Jakob Wörner2, Jan Gerrit Haaker1,5, Daniel Delev1,5, Nils Pompe2, Bianca Mercas1,5, Pamela Franco1,5, Annette Gäbelein1,5, Sabrina Heynckes1,5, Dietmar Pfeifer3,5, Stefan Weber2, Irina Mader4,5 and Oliver Schnell1,5

1Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany

2Institute of Physical Chemistry, Faculty of Chemistry and Pharmacy, University of Freiburg, Freiburg, Germany

3Department of Hematology, Oncology and Stem Cell Transplantation, Medical Center, University of Freiburg, Freiburg, Germany

4Department of Neuroradiology, Medical Center, University of Freiburg, Freiburg, Germany

5Faculty of Medicine, University of Freiburg, Freiburg, Germany

Correspondence to:

Dieter Henrik Heiland, email: [email protected]

Keywords: metabolomics, transcriptomics, network analysis, glioblastoma multiforme, WGCNA

Received: January 02, 2017 Accepted: February 23, 2017 Published: March 24, 2017

ABSTRACT

The purpose of this study was to map the landscape of metabolic-transcriptional alterations in glioblastoma multiforme. Omic-datasets were acquired by metabolic profiling (1D-NMR spectroscopy n=33 Patient) and transcriptomic profiling (n=48 Patients). Both datasets were analyzed by integrative network modeling. The computed model concluded in four different metabolic-transcriptomic signatures containing: oligodendrocytic differentiation, cell-cycle functions, immune response and hypoxia. These clusters were found being distinguished by individual metabolism and distinct transcriptional programs. The study highlighted the association between metabolism and hallmarks of oncogenic signaling such as cell-cycle alterations, immune escape mechanism and other cancer pathway alterations. In conclusion, this study showed the strong influence of metabolic alterations in the wide scope of oncogenic transcriptional alterations.

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Research Papers:

The integrative metabolomic-transcriptomic landscape of glioblastome multiforme

Abstract