Gliomas are a common tumor type that affects the glial cells with common features of malignant tumors such as aggressive invasiveness, malignant transformation and vascular proliferation through the central nervous system. Currently, standard therapeutic strategies to treat malignant gliomas are not efficient having alow-rate survival (~12 months). Hence, there is a clear necessity for the identification of novel diagnostic/prognostic tools and therapeutic strategies to manage and treat these devastatingtumor pathologies. In this sense, a relevant relationship between metabolic/endocrine factors and tumor development/progression has been widely reported since defects in endocrine and metabolic homeostasis underlie many common human diseases, including tumor progression. In this context, we carried out an interaction omics-based approach to identify potential endocrine/metabolic biomarkers with diagnostic, prognostic and/or therapeutic potential in low- and high-grade gliomas. For that purpose, we analyzed the correlation among radiological data, IDH1/2 mutations, gene expression profiling of multiple endocrine/metabolic elements in 25 tumor biopsies samples from patients (Age 48 ± 10-years/72%-men) clinically diagnosed with glioma (WHO2016). Initially, we used high resolution 31P and 1H magnetic resonance spectroscopy (MRS) in order to obtainthe quantification of 19 metabolites with a LCModel, while gene expression profiling was performed using qPCR of 19 genes related to energy metabolism. Moreover, IDH1/2 common mutation (IDH1R132H/IDH2R172H) was verified by immunohistochemistry and Sanger sequencing. Sequentially, all data was integrated using the mixOmics (R-package) allowing to build correlation network plot graphs and correlation maps to identify the most significant interactions, that were analyzed thereafter. Our results shown that the most frequent clinical features were intracranial hypertension and focal deficit. Remarkably, we found no differences between the metabolic or gene expression profiles in grade III vs IV glioma samples. However, there was a statistical significance or near-threshold correlation between some endocrine/metabolic patterns and IDH-mutation, where Alanine (4.7 ± 1.3% IDHw vs 2.5 ± 0.7 IDHmut), Glycine (2.7 ± 0.5% vs 1.6 ± 0.4%), Glycerophosphorylcholine (3.9 ± 0.4% vs 6.4 ± 0.9%) and Myo-inositol (4.9 ± 1.0% vs 11.9 ± 2.1%) were the most important biomarkers. Over expression of Lactate Dehydrogenase subunit-B (LDHB, 19 ± 3% vs 31 ± 6%) and Aconitase-1 (ACO1, 0.5 ± 0.1% vs 1.2 ± 0.3%) had also a significant or near-threshold relationship with IDH-mutation. Altogether, these results indicate that the endocrine-metabolic interaction patterns analyzed by this novel interactomic approach could be a useful tool to improve our knowledge of glioma behavior as well as being a potential source to identify novel biomarkers and therapeutic targets in order to tackle this pathology.
05 Sep 2020 - 09 Sep 2020