Plasma amino acids indicate glioblastoma with ATRX loss

From Biomarker Discovery to Clinical Applications of Metabolomics in Glioblastoma

BACKGROUND/OBJECTIVES

In recent years, interest in studying changes in cancer metabolites has resulted in significant advances in the metabolomics field. Glioblastoma remains the most aggressive and lethal brain malignancy, which presents with notable metabolic reprogramming.

METHODS

We performed literature research from the PubMed database and considered research articles focused on the key metabolic pathways altered in glioblastoma (e.g., glycolysis, lipid metabolism, TCA cycle), the role of oncometabolites and metabolic plasticity, and the differential expression of metabolites in glioblastoma. Currently used metabolomics approaches can be either targeted, focusing on specific metabolites and pathways, or untargeted, which involves data-driven exploration of the metabolome and also results in the identification of new metabolites. Data processing and analysis is of great importance and can be improved with the integration of machine learning approaches for metabolite identification.

RESULTS

Changes in α/β-glucose, lactate, choline, and 2-hydroxyglutarate were detected in glioblastoma compared with non-tumor tissues. Different metabolites such as fumarate, tyrosine, and leucine, as well as citric acid, isocitric acid, shikimate, and GABA were detected in blood and CSF, respectively.

CONCLUSIONS

Although promising new technological and bioinformatic approaches help us understand glioblastoma better, challenges associated with biomarker availability, tumor heterogeneity, interpatient variability, standardization, and reproducibility still remain. Metabolomics research, either alone or combined with genomics or proteomics (i.e., multiomics) in glioblastoma, can lead to biomarker identification, tracking of metabolic therapy response, discovery of novel metabolites and pathways, and identification of potential therapeutic targets.

Metabolomic profiling of plasma from glioma and meningioma patients based on two complementary mass spectrometry techniques

INTRODUCTION

Extracranial and intracranial tumors are a diverse group of malignant and benign neoplasms, influenced by multiple factors. Given the complex nature of these tumors and usually late or accidental diagnosis, minimally invasive, rapid, early, and accurate diagnostic methods are urgently required. Metabolomics offers promising insights into central nervous system tumors by uncovering distinctive metabolic changes linked to tumor development.

OBJECTIVES

This study aimed to elucidate the role of altered metabolites and the associated biological pathways implicated in the development of gliomas and meningiomas.

METHODS

The study was conducted on 95 patients with gliomas, 68 patients with meningiomas, and 71 subjects as a control group. The metabolic profiling of gliomas and meningiomas achieved by integrating untargeted metabolomic analysis based on GC-MS and targeted analysis performed using LC-MS/MS represents the first comprehensive study. Three comparisons (gliomas or meningiomas vs. controls as well as gliomas vs. meningiomas) were performed to reveal statistically significant metabolites.

RESULTS

Comparative analysis revealed 97, 56, and 27 significant metabolites for gliomas vs. controls, meningiomas vs. controls and gliomas vs. meningiomas comparison, respectively. Moreover, among above mentioned comparisons unique metabolites involved in arginine biosynthesis and metabolism, the Krebs cycle, and lysine degradation pathways were found. Notably, 2-aminoadipic acid has been identified as a metabolite that can be used in distinguishing two tumor types.

CONCLUSIONS

Our results provide a deeper understanding of the metabolic changes associated with brain tumor development and progression.

Mass Spectrometry Advances in Analysis of Glioblastoma

Some cancers such as glioblastoma (GBM), show minimal response to medical interventions, often only capable of mitigating tumor growth or alleviating symptoms. High metabolic activity in the tumor microenvironment marked by immune responses and hypoxia, is a crucial factor driving tumor progression. The many developments in mass spectrometry (MS) over the last decades have provided a pivotal tool for studying proteins, along with their posttranslational modifications. It is known that the proteomic landscape of GBM comprises a wide range of proteins involved in cell proliferation, survival, migration, and immune evasion. Combination of MS imaging and microscopy has potential to reveal the spatial and molecular characteristics of pathological tissue sections. Moreover, integration of MS in the surgical process in form of techniques such as DESI-MS or rapid evaporative ionization MS has been shown as an effective tool for rapid measurement of metabolite profiles, providing detailed information within seconds. In immunotherapy-related research, MS plays an indispensable role in detection and targeting of cancer antigens which serve as a base for antigen-specific therapies. In this review, we aim to provide detailed information on molecular profile in GBM and to discuss recent MS advances and their clinical benefits for targeting this aggressive disease.

A Synopsis of Biomarkers in Glioblastoma: Past and Present

Accounting for 48% of malignant brain tumors in adults, glioblastoma has been of great interest in the last decades, especially in the biomolecular and neurosurgical fields, due to its incurable nature and notable neurological morbidity. The major advancements in neurosurgical technologies have positively influenced the extent of safe tumoral resection, while the latest progress in the biomolecular field of GBM has uncovered new potential therapeutical targets. Although GBM currently has no curative therapy, recent progress has been made in the management of this disease, both from surgical and molecular perspectives. The main current therapeutic approach is multimodal and consists of neurosurgical intervention, radiotherapy, and chemotherapy, mostly with temozolomide. Although most patients will develop treatment resistance and tumor recurrence after surgical removal, biomolecular advancements regarding GBM have contributed to a better understanding of this pathology and its therapeutic management. Over the past few decades, specific biomarkers have been discovered that have helped predict prognosis and treatment responses and contributed to improvements in survival rates.

Potential Diagnostic and Clinical Significance of Selected Genetic Alterations in Glioblastoma

Glioblastoma is currently considered the most common and, unfortunately, also the most aggressive primary brain tumor, with the highest morbidity and mortality rates. The average survival of patients diagnosed with glioblastoma is 14 months, and only 2% of patients survive 3 years after surgery. Based on our clinical experience and knowledge from extensive clinical studies, survival is mainly related to the molecular biological properties of glioblastoma, which are of interest to the general medical community. Our study examined a total of 71 retrospective studies published from 2016 through 2022 and available on PubMed that deal with mutations of selected genes in the pathophysiology of GBM. In conclusion, we can find other mutations within a given gene group that have different effects on the prognosis and quality of survival of a patient with glioblastoma. These mutations, together with the associated mutations of other genes, as well as intratumoral heterogeneity itself, offer enormous potential for further clinical research and possible application in therapeutic practice.

D-Loop Mutations as Prognostic Markers in Glioblastoma-A Pilot Study

Glioblastoma, a highly aggressive brain tumor, poses significant treatment challenges. A deeper investigation into its molecular complexity is essential for the identification of novel prognostic biomarkers and therapeutic strategies, potentially improving patient outcomes in terms of survival and quality of life. While nuclear DNA mutations have been extensively studied, the role of mitochondrial DNA (mtDNA) mutations, specifically in the D-loop region, remains poorly understood. This prospective case-control study aimed to assess the prognostic significance of the mtDNA D-loop m.16126T>C variant in glioblastoma patients. Immunohistochemistry and droplet digital PCR (ddPCR) were employed for mutation analysis, complemented by statistical analyses and a literature review. The study cohort comprised 22 glioblastoma patients (mean age 59.36 ± 14.17, 12 (54.55%) females), and 25 controls (59.48 ± 13.22, 12 (80%) females). The D-loop m.16126T>C variant was observed in four (18%) of the glioblastoma samples and was associated with shorter median survival (9.5 vs. 18 months; p = 0.016, log-rank test). This study underscores the importance of investigating mtDNA, especially D-loop variants, in glioblastoma, suggesting its potential as a prognostic biomarker and, therefore, its possible therapeutic targets, warranting further exploration.

Exploring the gut microbiota and its potential as a biomarker in gliomas

Gut microbiome alterations are associated with various cancers including brain tumours such as glioma and glioblastoma. The gut communicates with the brain via a bidirectional pathway known as the gut-brain axis (GBA) which is essential for maintaining homeostasis. The gut microbiota produces many metabolites including short chain fatty acids (SCFAs) and essential amino acids such as glutamate, glutamine, arginine and tryptophan. Through the modulation of these metabolites the gut microbiome is able to regulate several functions of brain cells, immune cells and tumour cells including DNA methylation, mitochondrial function, the aryl hydrocarbon receptor (AhR), T-cell proliferation, autophagy and even apoptosis. Here, we summarise current findings on gut microbiome with respect to brain cancers, an area of research that is widely overlooked. Several studies investigated the relationship between gut microbiota and brain tumours. However, it remains unclear whether the gut microbiome variation is a cause or product of cancer. Subsequently, a biomarker panel was constructed for use as a predictive, prognostic and diagnostic tool with respect to multiple cancers including glioma and glioblastoma multiforme (GBM). This review further presents the intratumoural microbiome, a fascinating microenvironment within the tumour as a possible treatment target that can be manipulated to maximise effectiveness of treatment via personalised therapy. Studies utilising the microbiome as a biomarker and therapeutic strategy are necessary to accurately assess the effectiveness of the gut microbiome as a clinical tool with respect to brain cancers.

A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors

Metabolomics combined with machine learning methods (MLMs), is a powerful tool for searching novel diagnostic panels. This study was intended to use targeted plasma metabolomics and advanced MLMs to develop strategies for diagnosing brain tumors. Measurement of 188 metabolites was performed on plasma samples collected from 95 patients with gliomas (grade I-IV), 70 with meningioma, and 71 healthy individuals as a control group. Four predictive models to diagnose glioma were prepared using 10 MLMs and a conventional approach. Based on the cross-validation results of the created models, the F1-scores were calculated, then obtained values were compared. Subsequently, the best algorithm was applied to perform five comparisons involving gliomas, meningiomas, and controls. The best results were obtained using the newly developed hybrid evolutionary heterogeneous decision tree (EvoHDTree) algorithm, which was validated using Leave-One-Out Cross-Validation, resulting in an F1-score for all comparisons in the range of 0.476-0.948 and the area under the ROC curves ranging from 0.660 to 0.873. Brain tumor diagnostic panels were constructed with unique metabolites, which reduces the likelihood of misdiagnosis. This study proposes a novel interdisciplinary method for brain tumor diagnosis based on metabolomics and EvoHDTree, exhibiting significant predictive coefficients.

Tissue and plasma free amino acid detection by LC-MS/MS method in high grade glioma patients

PURPOSE

The changes in serum amino acid profiles are evaluated in different types of cancers and screening tests were developed for estimating the risk of cancer by rapid analysis of plasma free amino acid (PFAA) levels. There is scarce evidence about the metabolomics analysis of PFAA in malignant gliomas. The aim of the present study was to identify the most promising diagnostic amino acid biomarkers that could be objectively measured for high-grade glioma and to compare their level with the tissue counterpart.

METHODS

In this prospective study, we collected serum samples from 22 patients with the pathological diagnosis of high-grade diffuse glioma according to WHO 2016 classification and 22 healthy subjects, and brain tissue from 22 controls. Plasma and tissue amino acid concentrations were analyzed applying liquid chromatography-tandem mass spectrometry (LC-MS/MS) method.

RESULTS

Serum alanine, alpha-aminobutyric acid (AABA), lysine (Lys) and cysteine concentrations were significantly higher in high-grade glioma patients despite low levels of alanine and Lys in the tumor tissue. Aspartic acid, histidine and taurine were significantly decreased in both serum and tumors of glioma patients. A positive correlation was detected between tumor volumes and serum levels of latter three amino acids.

CONCLUSION

This study demonstrated potential amino acids which may have diagnostic value for high-grade glioma patients by utilizing LC-MS/MS method. Our results are preliminary to compare serum and tissue levels of amino acids in patients with malignant gliomas. The data presented here may provide feature ideas about the metabolic pathways in the pathogenesis of gliomas.

Bioenergetic Profiling in Glioblastoma Multiforme Patients with Different Clinical Outcomes

The accumulation of cell biomass is associated with dramatically increased bioenergetic and biosynthetic demand. Metabolic reprogramming, once thought as an epiphenomenon, currently relates to disease progression, also in response to extracellular fate-decisive signals. Glioblastoma multiforme patients often suffer misdiagnosis, short survival time, low quality of life, and poor disease management options. Today, tumor genetic testing and histological analysis guide diagnosis and treatment. We and others appreciate that metabolites complement translational biomarkers and molecular signatures in disease profiling and phenotyping. Herein, we coupled a mixed-methods content analysis to a mass spectrometry-based untargeted metabolomic analysis on plasma samples from glioblastoma multiforme patients to delineate the role of metabolic remodeling in biological plasticity and, hence, disease severity. Following data processing and analysis, we established a bioenergetic profile coordinated by the mitochondrial function and redox state, lipids, and energy substrates. Our findings show that epigenetic modulators are key players in glioblastoma multiforme cell metabolism, in particular when microRNAs are considered. We propose that biological plasticity in glioblastoma multiforme is a mechanism of adaptation and resistance to treatment which is eloquently revealed by bioenergetics.

Rewired Metabolism of Amino Acids and Its Roles in Glioma Pathology

Amino acids (AAs) are indispensable building blocks of diverse bio-macromolecules as well as functional regulators for various metabolic processes. The fact that cancer cells live with a voracious appetite for specific AAs has been widely recognized. Glioma is one of the most lethal malignancies occurring in the central nervous system. The reprogrammed metabolism of AAs benefits glioma proliferation, signal transduction, epigenetic modification, and stress tolerance. Metabolic alteration of specific AAs also contributes to glioma immune escape and chemoresistance. For clinical consideration, fluctuations in the concentrations of AAs observed in specific body fluids provides opportunities to develop new diagnosis and prognosis markers. This review aimed at providing an extra dimension to understanding glioma pathology with respect to the rewired AA metabolism. A deep insight into the relevant fields will help to pave a new way for new therapeutic target identification and valuable biomarker development.

Current understanding of the human microbiome in glioma

There is mounting evidence that the human microbiome is highly associated with a wide variety of central nervous system diseases. However, the link between the human microbiome and glioma is rarely noticed. The exact mechanism of microbiota to affect glioma remains unclear. Recent studies have demonstrated that the microbiome may affect the development, progress, and therapy of gliomas, including the direct impacts of the intratumoral microbiome and its metabolites, and the indirect effects of the gut microbiome and its metabolites. Glioma-related microbiome (gut microbiome and intratumoral microbiome) is associated with both tumor microenvironment and tumor immune microenvironment, which ultimately influence tumorigenesis, progression, and responses to treatment. In this review, we briefly summarize current knowledge regarding the role of the glioma-related microbiome, focusing on its gut microbiome fraction and a brief description of the intratumoral microbiome, and put forward the prospects in which microbiome can be applied in the future and some challenges still need to be solved.

Plasma Amino Acids May Improve Prediction Accuracy of Cerebral Vasospasm after Aneurysmal Subarachnoid Haemorrhage

Aneurysmal subarachnoid haemorrhages (aSAH) account for 5% of strokes and continues to place a great burden on patients and their families. Cerebral vasospasm (CVS) is one of the main causes of death after aSAH, and is usually diagnosed between day 3 and 14 after bleeding. Its pathogenesis remains poorly understood. To verify whether plasma concentration of amino acids have prognostic value in predicting CVS, we analysed data from 35 patients after aSAH (median age 55 years, IQR 39-62; 20 females, 57.1%), and 37 healthy volunteers (median age 50 years, IQR 38-56; 19 females, 51.4%). Fasting peripheral blood samples were collected on postoperative day one and seven. High performance liquid chromatography-mass spectrometry (HPLC-MS) analysis was performed. The results showed that plasma from patients after aSAH featured a distinctive amino acids concentration which was presented in both principal component analysis and direct comparison. No significant differences were noted between postoperative day one and seven. A total of 18 patients from the study group (51.4%) developed CVS. Hydroxyproline (AUC = 0.7042, 95%CI 0.5259-0.8826, p = 0.0248) and phenylalanine (AUC = 0.6944, 95%CI 0.5119-0.877, p = 0.0368) presented significant CVS prediction potential. Combining the Hunt-Hess Scale and plasma levels of hydroxyproline and phenylalanine provided the model with the best predictive performance and the lowest leave-one-out cross-validation of performance error. Our results suggest that plasma amino acids may improve sensitivity and specificity of Hunt-Hess scale in predicting CVS.

Integrative Metabolomics Reveals Deep Tissue and Systemic Metabolic Remodeling in Glioblastoma

(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.