Integrative Metabolomics Reveals Deep Tissue and Systemic Metabolic Remodeling in Glioblastoma

Abstract

(1) Background: Glioblastoma is the most common malignant brain tumor in adults. Its etiology remains unknown in most cases. Glioblastoma pathogenesis consists of a progressive infiltration of the white matter by tumoral cells leading to progressive neurological deficit, epilepsy, and/or intracranial hypertension. The mean survival is between 15 to 17 months. Given this aggressive prognosis, there is an urgent need for a better understanding of the underlying mechanisms of glioblastoma to unveil new diagnostic strategies and therapeutic targets through a deeper understanding of its biology. (2) Methods: To systematically address this issue, we performed targeted and untargeted metabolomics-based investigations on both tissue and plasma samples from patients with glioblastoma. (3) Results: This study revealed 176 differentially expressed lipids and metabolites, 148 in plasma and 28 in tissue samples. Main biochemical classes include phospholipids, acylcarnitines, sphingomyelins, and triacylglycerols. Functional analyses revealed deep metabolic remodeling in glioblastoma lipids and energy substrates, which unveils the major role of lipids in tumor progression by modulating its own environment. (4) Conclusions: Overall, our study demonstrates in situ and systemic metabolic rewiring in glioblastoma that could shed light on its underlying biological plasticity and progression to inform diagnosis and/or therapeutic strategies.

Assessing Metabolic Markers in Glioblastoma Using Machine Learning: A Systematic Review

“…Thirty-one articles were excluded, leaving 14 final studies included in this review, ten of which were included in the analysis [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. Four studies were not included in the analysis because they did not perform an ML algorithm on GBM; however, they did discuss the topic [ 21 , 42 , 43 , 44 , 45 , 46 ].…”

“…The studies within this review aim to improve diagnostic and prognostic accuracy by utilizing novel ML algorithms. Although this field exhibits an extensive level of research, there is a paucity of literature pertaining to the ML algorithms used to identify markers underlying GBM metabolism [ 42 ].…”

“…Supervised ML is broadly used in a predictive scenario where a “ground truth” value can be determined (e.g., a diagnosis of GBM) and the user wishes to identify similar data sets with an unknown “ground truth.” The supervised ML algorithms used by these studies were SVM, random forest, ANN, deep neural networks (e.g., PASnet), DT, NB, partial least-squares discriminant analysis (PLS-DA), logistic regression models, and LASSO-penalized Cox regression analysis [ 32 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. A logistic regression model appears to outperform other ML algorithms in classification systems, in this case, the classification of the IDH mutation.…”

Lost in the Forest: Encoding Categorical Variables and the Absent Levels Problem

“…We used the package ‘’ (“RANdom forest GEneRator” (Wright and Ziegler, 2017)) for R (Team, 2021), a popular implementation of random forest models because it can handle high dimensional data, is simple to use, and is computationally efficient. has been used widely in medical research (Gilard et al, 2021; Siegbahn et al, 2021; Yang et al, 2021), environmental monitoring (Sothe et al, 2022), genomics (Muller et al, 2021), epidemiology (Nader et al, 2021; Hamlet et al, 2021; Marotz et al, 2021), and other fields.…”

Stress Reactivity, Susceptibility to Hypertension, and Differential Expression of Genes in Hypertensive Compared to Normotensive Patients

“…Pulmonary hypertension may start to develop in newborns [ 8 ]. Hypertension co-occurs with cancer [ 9 , 10 , 11 , 12 , 13 ], cirrhosis [ 14 ], and prostatitis [ 15 ]. Hypertension worsens both injury [ 16 ] and transplantation [ 17 ] of a kidney.…”