An Experimentally Defined Hypoxia Gene Signature in Glioblastoma and Its Modulation by Metformin.
Marta Calvo TardónEliana MarinariDenis MiglioriniViviane BesStoyan TankovEmily CharrierThomas A McKeeValérie DutoitPierre-Yves DietrichErika CossetPaul R WalkerPublished in: Biology (2020)
Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumor, characterized by a high degree of intertumoral heterogeneity. However, a common feature of the GBM microenvironment is hypoxia, which can promote radio- and chemotherapy resistance, immunosuppression, angiogenesis, and stemness. We experimentally defined common GBM adaptations to physiologically relevant oxygen gradients, and we assessed their modulation by the metabolic drug metformin. We directly exposed human GBM cell lines to hypoxia (1% O2) and to physioxia (5% O2). We then performed transcriptional profiling and compared our in vitro findings to predicted hypoxic areas in vivo using in silico analyses. We observed a heterogenous hypoxia response, but also a common gene signature that was induced by a physiologically relevant change in oxygenation from 5% O2 to 1% O2. In silico analyses showed that this hypoxia signature was highly correlated with a perinecrotic localization in GBM tumors, expression of certain glycolytic and immune-related genes, and poor prognosis of GBM patients. Metformin treatment of GBM cell lines under hypoxia and physioxia reduced viable cell number, oxygen consumption rate, and partially reversed the hypoxia gene signature, supporting further exploration of targeting tumor metabolism as a treatment component for hypoxic GBM.
Keyphrases
- endothelial cells
- poor prognosis
- long non coding rna
- stem cells
- end stage renal disease
- genome wide
- gene expression
- copy number
- ejection fraction
- squamous cell carcinoma
- molecular docking
- chronic kidney disease
- vascular endothelial growth factor
- emergency department
- transcription factor
- peritoneal dialysis
- genome wide identification
- bone marrow
- drug delivery
- deep learning
- smoking cessation
- electronic health record
- replacement therapy