Glioblastoma (GBM) is one of the deadliest human cancers. Because of the extremely unfavorable prognosis of GBM, it is important to develop more effective diagnostic and therapeutic strategies based on biologically and clinically relevant subclassification systems. Analyzing a collection of seventeen patient-derived glioblastoma stem-like cells (GSCs) by gene expression profiling, NMR spectroscopy and signal transduction pathway activation, we identified two GSC clusters, one characterized by a pro-neural-like phenotype and the other showing a mesenchymal-like phenotype. Evaluating the levels of proteins differentially expressed by the two GSC clusters in the TCGA GBM sample collection, we found that SRC activation is associated with a GBM subgroup showing better prognosis whereas activation of RPS6, an effector of mTOR pathway, identifies a subgroup with a worse prognosis. The two clusters are also differentiated by NMR spectroscopy profiles suggesting a potential prognostic stratification based on metabolic evaluation. Our data show that the metabolic/proteomic profile of GSCs is informative of the genomic/proteomic GBM landscape, which differs among tumor subtypes and is associated with clinical outcome.

This Southern European study confirms the importance of demoralization in cancer patients as a different condition with respect to depression and its relationship with poor QoL and suicidal ideation.

The metabolic profiles of glioblastoma stem-like cells (GSCs) growing in neurospheres were examined by (1)H NMR spectroscopy. Spectra of two GSC lines, labelled 1 and 83, from tumours close to the subventricular zone of the temporal lobe were studied in detail and compared with those of neural stem/progenitor cells from the adult olfactory bulb (OB-NPCs) and of the T98G glioblastoma cell line. In both GSCs, signals from myoinositol (Myo-I), UDP-hexosamines (UDP-Hex) and glycine indicated an astrocyte/glioma metabolism. For line 1, the presence of signals from N-acetyl aspartate, GABA and creatine pointed to a neuronal fingerprint. These metabolites were almost absent from line 83 spectra, whereas lipid signals, absent from normal neural lineages, were intense in line 83 spectra and remained low in those of line 1, irrespective of apoptotic fate. Spectra of OB-NPC cells displayed strong similarities with those from line 1, with low lipid signals and clearly detectable neuronal signals. In contrast, the spectral profile of line 83 was more similar to that of T98G, displaying high lipids and nearly complete absence of the neuronal markers. A mixed neural-astrocyte metabolic phenotype with a strong neuronal fingerprint was therefore found in line 1, while an astrocytic/glioma-like metabolism prevailed in line 83. We found a signal assigned to the amide proton of N-acetyl galactosamine in GSC lines and in OB-NPC spectra, whereas it was absent from those of T98G cells. This signal may be related to a stem-cell-specific protein glycosylation pattern and is therefore suggested as a marker of cell multipotency. Other GSC lines from patients with different clinical outcomes were then examined. Unsupervised analysis of spectral data from 13 lines yielded two clusters, with six lines resembling spectral features of line 1 and seven resembling those of line 83, suggesting that distinct metabolic phenotypes may be present in GSC lines.

Glioblastoma multiforme (GBM) is the most common and malignant primary brain tumor in adults, characterized by aggressive growth, limited response to therapy, and inexorable recurrence. Because of the extremely unfavorable prognosis of GBM, it is important to develop more effective diagnostic and therapeutic strategies based on biologically and clinically relevant patient stratification systems. Analyzing a collection of patient‐derived GBM stem‐like cells (GSCs) by gene expression profiling, nuclear magnetic resonance spectroscopy, and signal transduction pathway activation, we identified two GSC clusters characterized by different clinical features. Due to the widely documented role played by microRNAs (miRNAs) in the tumorigenesis process, in this study we explored whether these two GBM patient subtypes could also be discriminated by different miRNA signatures. Global miRNA expression pattern was analyzed by oblique principal component analysis and principal component analysis. By a combined inferential strategy on PCA results, we identified a reduced set of three miRNAs – miR‐23a, miR‐27a, and miR‐9* (miR‐9‐3p) – able to discriminate the proneural‐ and mesenchymal‐like GSC phenotypes as well as mesenchymal and proneural subtypes of primary GBM included in The Cancer Genome Atlas (TCGA) data set. Kaplan–Meier analysis showed a significant correlation between the selected miRNAs and overall survival in 429 GBM specimens from TCGA‐identifying patients who had an unfavorable outcome. The survival prognostic capability of the three‐miRNA signatures could have important implications for the understanding of the biology of GBM subtypes and could be useful in patient stratification to facilitate interpretation of results from clinical trials.