A prospective study of serum metabolites and glioma risk

Comparative Analysis of Amino Acid Profiles in Patients with Glioblastoma and Meningioma Using Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometry (LC-ESI-MS/MS)

Brain tumors account for 1% of all cancers diagnosed de novo. Due to the specificity of the anatomical area in which they grow, they can cause significant neurological disorders and lead to poor functional status and disability. Regardless of the results of biochemical markers of intracranial neoplasms, they are currently of no diagnostic significance. The aim of the study was to use LC-ESI-MS/MS in conjunction with multivariate statistical analyses to examine changes in amino acid metabolic profiles between patients with glioblastoma, meningioma, and a group of patients treated for osteoarthritis of the spine as a control group. Comparative analysis of amino acids between patients with glioblastoma, meningioma, and the control group allowed for the identification of statistically significant differences in the amino acid profile, including both exogenous and endogenous amino acids. The amino acids that showed statistically significant differences (lysine, histidine, α-aminoadipic acid, phenylalanine) were evaluated for diagnostic usefulness based on the ROC curve. The best results were obtained for phenylalanine. Classification trees were used to build a model allowing for the correct classification of patients into the study group (patients with glioblastoma multiforme) and the control group, in which cysteine turned out to be the most important amino acid in the decision-making algorithm. Our results indicate amino acids that may prove valuable, used alone or in combination, toward improving the diagnosis of patients with glioma and meningioma. To better assess the potential utility of these markers, their performance requires further validation in a larger cohort of samples.

Pre-diagnostic blood biomarkers for adult glioma

Glioma is one of the most common malignant primary brain tumours in adults, of which, glioblastoma is the most prevalent and malignant entity. Glioma is often diagnosed at a later stage of disease progression, which means it is associated with significant mortality and morbidity. Therefore, there is a need for earlier diagnosis of these tumours, which would require sensitive and specific biomarkers. These biomarkers could better predict glioma onset to improve diagnosis and therapeutic options for patients. While liquid biopsies could provide a cheap and non-invasive test to improve the earlier detection of glioma, there is little known on pre-diagnostic biomarkers which predate disease detection. In this review, we examine the evidence in the literature for pre-diagnostic biomarkers in glioma, including metabolomics and proteomics. We also consider the limitations of these approaches and future research directions of pre-diagnostic biomarkers for glioma.

A pilot study on metabolomic characterization of human glioblastomas and patient plasma

Purpose

To determine whether recurrent GBMs are metabolically distinct from primary GBM, and whether patient plasma can be used as a liquid biopsy to reflect this difference.

Methods

In a single center cohort study, tissue and blood samples from 15 patients with glioblastoma (9 glioblastoma tissues at diagnosis, 3 pairs of tissue, and 6 pairs of plasma specimens at diagnosis and at recurrence) were analyzed.

Results

Several metabolites had significant alternations in both tumor and plasma specimens. In the tissue, the following representative metabolites had a significant increase in peak intensity at recurrence compared to diagnosis: N-alpha-methylhistamine (p = 0.037), glycerol-3-phosphate (p = 0.029), phosphocholine (p = 0.045), and succinic acid (p = 0.025). In patient plasma, metabolites that significantly increased at recurrence included: 2,4-difluorotoluene (p = 0.031), diatrizoic acid (p = 0.032), indole-3-acetate with (p = 0.029), urea (P = 0.025), pseudouridine (p = 0.042), and maltose (p = 0.035). Metabolites that significantly decreased in plasma at recurrence were: eicosenoic acid (p = 0.017), glucose-1-phosphate (p = 0.017), FA 18:2 (linoleic acid) (p = 0.017), arginine (p = 0.036), fatty acids 20:3 (homo-gamma-linolenic acid (p = 0.036), galactosamine (p = 0.007), and FA 18:3 (linolenic acid) (P = 0.012). Principal component analysis showed that the metabolomic profiles differ between tumor tissue and patient plasma.

Conclusions

Our data suggest that metabolomic profiles of human GBM tissue and patient plasma differ at diagnosis and at recurrence. Many metabolites involved in tumorigenesis and metabolomic flexibility were identified. A larger study using targeted metabolomic assay is warranted to measure the levels of these metabolites, which will help identify the metabolomic signatures in both GBM tissue and patient plasma for risk stratification, clinical outcome prediction, and development of new adjuvant metabolomic-targeting therapy.

Untargeted metabolomic profiling identifies serum metabolites associated with type 2 diabetes in a cross-sectional study of the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study

Type 2 diabetes (T2D) is a complex chronic disease with substantial phenotypic heterogeneity affecting millions of individuals. Yet, its relevant metabolites and etiological pathways are not fully understood. The aim of this study is to assess a broad spectrum of metabolites related to T2D in a large population-based cohort. We conducted a metabolomic analysis of 4,281 male participants within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. The serum metabolomic analysis was performed using an LC-MS/GC-MS platform. Associations between 1,413 metabolites and T2D were examined using linear regression, controlling for important baseline risk factors. Standardized β-coefficients and standard errors (SEs) were computed to estimate the difference in metabolite concentrations. We identified 74 metabolites that were significantly associated with T2D based on the Bonferroni-corrected threshold (P < 3.5 × 10-5). The strongest signals associated with T2D were of carbohydrates origin, including glucose, 1,5-anhydroglucitol (1,5-AG), and mannose (β = 0.34, -0.91, and 0.41, respectively; all P < 10-75). We found several chemical class pathways that were significantly associated with T2D, including carbohydrates (P = 1.3 × 10-11), amino acids (P = 2.7 × 10-6), energy (P = 1.5 × 10-4), and xenobiotics (P = 1.2 × 10-3). The strongest subpathway associations were seen for fructose-mannose-galactose metabolism, glycolysis-gluconeogenesis-pyruvate metabolism, fatty acid metabolism (acyl choline), and leucine-isoleucine-valine metabolism (all P < 10-8). Our findings identified various metabolites and candidate chemical class pathways that can be characterized by glycolysis and gluconeogenesis metabolism, fructose-mannose-galactose metabolism, branched-chain amino acids, diacylglycerol, acyl cholines, fatty acid oxidation, and mitochondrial dysfunction.NEW & NOTEWORTHY These metabolomic patterns may provide new additional evidence and potential insights relevant to the molecular basis of insulin resistance and the etiology of T2D.

Lipid Alterations in Glioma: A Systematic Review

Gliomas are highly lethal tumours characterised by heterogeneous molecular features, producing various metabolic phenotypes leading to therapeutic resistance. Lipid metabolism reprogramming is predominant and has contributed to the metabolic plasticity in glioma. This systematic review aims to discover lipids alteration and their biological roles in glioma and the identification of potential lipids biomarker. This systematic review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Extensive research articles search for the last 10 years, from 2011 to 2021, were conducted using four electronic databases, including PubMed, Web of Science, CINAHL and ScienceDirect. A total of 158 research articles were included in this study. All studies reported significant lipid alteration between glioma and control groups, impacting glioma cell growth, proliferation, drug resistance, patients' survival and metastasis. Different lipids demonstrated different biological roles, either beneficial or detrimental effects on glioma. Notably, prostaglandin (PGE2), triacylglycerol (TG), phosphatidylcholine (PC), and sphingosine-1-phosphate play significant roles in glioma development. Conversely, the most prominent anti-carcinogenic lipids include docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and vitamin D3 have been reported to have detrimental effects on glioma cells. Furthermore, high lipid signals were detected at 0.9 and 1.3 ppm in high-grade glioma relative to low-grade glioma. This evidence shows that lipid metabolisms were significantly dysregulated in glioma. Concurrent with this knowledge, the discovery of specific lipid classes altered in glioma will accelerate the development of potential lipid biomarkers and enhance future glioma therapeutics.

Prediagnostic biomarkers for early detection of glioma—using case–control studies from cohorts as study approach

Background

Understanding the trajectory and development of disease is important and the knowledge can be used to find novel targets for therapy and new diagnostic tools for early diagnosis.

Methods

Large cohorts from different parts of the world are unique assets for research as they have systematically collected plasma and DNA over long-time periods in healthy individuals, sometimes even with repeated samples. Over time, the population in the cohort are diagnosed with many different diseases, including brain tumors.

Results

Recent studies have detected genetic variants that are associated with increased risk of glioblastoma and lower grade gliomas specifically. The impact for genetic markers to predict disease in a healthy population has been deemed low, and a relevant question is if the genetic variants for glioma are associated with risk of disease or partly consist of genes associated to survival. Both metabolite and protein spectra are currently being explored for early detection of cancer.

Conclusions

We here present a focused review of studies of genetic variants, metabolomics, and proteomics studied in prediagnostic glioma samples and discuss their potential in early diagnostics.

Association between vitamins and risk of brain tumors: A systematic review and dose-response meta-analysis of observational studies

Background

Brain tumor is one of the important causes of cancer mortality, and the prognosis is poor. Therefore, early prevention of brain tumors is the key to reducing mortality due to brain tumors.

Objective

This review aims to quantitatively evaluate the association between vitamins and brain tumors by meta-analysis.

Methods

We searched articles on PubMed, Cochrane Library, Web of Science, and Embase databases from inception to 19 December 2021. According to heterogeneity, the fixed-effects model or random-effects model was selected to obtain the relative risk of the merger. Based on the methods described by Greenland and Longnecker, we explored the dose-response relationship between vitamins and the risk of brain tumors. Subgroup analysis, sensitivity analysis, and publication bias were also used for the analysis.

Results

The study reviewed 23 articles, including 1,347,426 controls and 6,449 brain tumor patients. This study included vitamin intake and circulating concentration. For intake, it mainly included vitamin A, vitamin B, vitamin C, vitamin E, β-carotene, and folate. For circulating concentrations, it mainly included vitamin E and vitamin D in the serum (25-hydroxyvitamin D and α-tocopherol). For vitamin intake, compared with the lowest intakes, the highest intakes of vitamin C (RR = 0.81, 95%CI:0.66–0.99, I² = 54.7%, P_{for heterogeneity} = 0.007), β-carotene (RR = 0.78, 95%CI:0.66–0.93, I² = 0, P_{for heterogeneity} = 0.460), and folate (RR = 0.66, 95%CI:0.55–0.80, I² = 0, P_{for heterogeneity} = 0.661) significantly reduced the risk of brain tumors. For serum vitamins, compared with the lowest concentrations, the highest concentrations of serum α-tocopherol (RR = 0.61, 95%CI:0.44–0.86, I² = 0, P_{for heterogeneity} = 0.656) significantly reduced the risk of brain tumors. The results of the dose-response relationship showed that increasing the intake of 100 μg folate per day reduced the risk of brain tumors by 7% (P_{−nonlinearity} = 0.534, RR = 0.93, 95%CI:0.90–0.96).

Conclusion

Our analysis suggests that the intake of vitamin C, β-carotene, and folate can reduce the risk of brain tumors, while high serum α-tocopherol concentration also has a protective effect on brain tumors. Therefore, vitamins may provide new ideas for the prevention of brain tumors.

Systematic Review Registration

PROSPERO, identifier CRD42022300683.

Proline Metabolism in Malignant Gliomas: A Systematic Literature Review

Simple Summary

Studies of various types of cancers have found proline metabolism to be a key player in tumor development, involved in basic metabolic pathways, regulating cell proliferation, survival, and signaling. Here, we systematically searched the literature to find data on proline metabolism in malignant glial tumors. Despite limited availability, existing studies have found several ways in which proline metabolism may affect the development of gliomas, involving the maintenance of redox balance, providing essential glutamate, and affecting major signaling pathways. Metabolomic profiling has revealed the importance of proline as a link to basic cell metabolic cycles and shown it to be correlated with overall survival. Emerging knowledge on the role of proline in general oncology encourages further studies on malignant gliomas.

Abstract

Background: Proline has attracted growing interest because of its diverse influence on tumor metabolism and the discovery of the regulatory mechanisms that appear to be involved. In contrast to general oncology, data on proline metabolism in central nervous system malignancies are limited. Materials and Methods: We performed a systematic literature review of the MEDLINE and EMBASE databases according to PRISMA guidelines, searching for articles concerning proline metabolism in malignant glial tumors. From 815 search results, we identified 14 studies pertaining to this topic. Results: The role of the proline cycle in maintaining redox balance in IDH-mutated gliomas has been convincingly demonstrated. Proline is involved in restoring levels of glutamate, the main glial excitatory neurotransmitter. Proline oxidase influences two major signaling pathways: p53 and NF- κB. In metabolomics studies, the metabolism of proline and its link to the urea cycle was found to be a prognostic factor for survival and a marker of malignancy. Data on the prolidase concentration in the serum of glioblastoma patients are contradictory. Conclusions: Despite a paucity of studies in the literature, the available data are interesting enough to encourage further research, especially in terms of extrapolating what we have learned of proline functions from other neoplasms to malignant gliomas.

Metabolomic analysis of serum alpha-tocopherol among men in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study

BACKGROUND/OBJECTIVES

The role of vitamin E in chronic disease risk remains incompletely understood, particularly in an un-supplemented state, and evidence is sparse regarding the biological actions and pathways involved in its influence on health outcomes. Identifying vitamin-E-associated metabolites through agnostic metabolomics analyses can contribute to elucidating the specific associations and disease etiology. This study aims to investigate the association between circulating metabolites and serum α-tocopherol concentration in an un-supplemented state.

SUBJECTS/METHODS

Metabolomic analysis of 4,294 male participants was conducted based on pre-supplementation fasting serum in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study. The associations between 1,791 known metabolites measured by ultra-high-performance LC-MS/GC-MS and HPLC-determined α-tocopherol concentration were estimated using multivariable linear regression. Differences in metabolite levels per unit difference in α-tocopherol concentration were calculated as standardized β-coefficients and standard errors.

RESULTS

A total of 252 metabolites were associated with serum α-tocopherol at the Bonferroni-corrected p value (p < 2.79 × 10^-5). Most of these metabolites were of lipid and amino acid origin, with the respective subclasses of dicarboxylic fatty acids, and valine, leucine, and isoleucine metabolism, being highly represented. Among lipids, the strongest signals were observed for linoleoyl-arachidonoyl-glycerol (18:2/20:4)[2](β = 0.149; p = 8.65 × 10^-146) and sphingomyelin (D18:2/18:1) (β = 0.035; p = 1.36 × 10^-30). For amino acids, the strongest signals were aminoadipic acid (β = 0.021; p = 5.01 × 10^-13) and l-leucine (β = 0.007; p = 1.05 × 10^-12).

CONCLUSIONS

The large number of metabolites, particularly lipid and amino acid compounds associated with serum α-tocopherol provide leads regarding potential mechanisms through which vitamin E influences human health, including its role in cardiovascular disease and cancer.

Harnessing oxidative stress for anti-glioma therapy

Glioma cells use intermediate levels of reactive oxygen species (ROS) and reactive nitrogen species (RNS) for growth and invasion, and suppressing these reactive molecules thus may compromise processes that are vital for glioma survival. Increased oxidative stress has been identified in glioma cells, in particular in glioma stem-like cells. Studies have shown that these cells harbor potent antioxidant defenses, although endogenous protection against nitrosative stress remains understudied. The enhancement of oxidative or nitrosative stress offers a potential target for triggering glioma cell death, but whether oxidative and nitrosative stresses can be combined for therapeutic effects requires further research. The optimal approach of harnessing oxidative stress for anti-glioma therapy should include the induction of free radical-induced oxidative damage and the suppression of antioxidant defense mechanisms selectively in glioma cells. However, selective induction of oxidative/nitrosative stress in glioma cells remains a therapeutic challenge, and research into selective drug delivery systems is ongoing. Because of multifactorial mechanisms of glioma growth, progression, and invasion, prospective oncological therapies may include not only therapeutic oxidative/nitrosative stress but also inhibition of oncogenic kinases, antioxidant molecules, and programmed cell death mediators.

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.

Precision Neurosurgery: A Path Forward

Since the inception of their profession, neurosurgeons have defined themselves as physicians with a surgical practice. Throughout time, neurosurgery has always taken advantage of technological advances to provide better and safer care for patients. In the ongoing precision medicine surge that drives patient-centric healthcare, neurosurgery strives to effectively embrace the era of data-driven medicine. Neuro-oncology best illustrates this convergence between surgery and precision medicine with the advent of molecular profiling, imaging and data analytics. This convenient convergence paves the way for new preventive, diagnostic, prognostic and targeted therapeutic perspectives. The prominent advances in healthcare and big data forcefully challenge the medical community to deeply rethink current and future medical practice. This work provides a historical perspective on neurosurgery. It also discusses the impact of the conceptual shift of precision medicine on neurosurgery through the lens of neuro-oncology.

Complementary Nuclear Magnetic Resonance-Based Metabolomics Approaches for Glioma Biomarker Identification in a Drosophila melanogaster Model

Human malignant gliomas are the most common type of primary brain tumor. Composed of glial cells and their precursors, they are aggressive and highly invasive, leading to a poor prognosis. Due to the difficulty of surgically removing tumors and their resistance to treatments, novel therapeutic approaches are needed to improve patient life expectancy and comfort. Drosophila melanogaster is a compelling genetic model to better understanding human neurological diseases owing to its high conservation in signaling pathways and cellular content of the brain. Here, glioma has been induced in Drosophila by co-activating the epidermal growth factor receptor and the phosphatidyl-inositol-3 kinase signaling pathways. Complementary nuclear magnetic resonance (NMR) techniques were used to obtain metabolic profiles in the third instar larvae brains. Fresh organs were directly studied by ¹H high resolution-magic angle spinning (HR-MAS) NMR, and brain extracts were analyzed by solution-state ¹H-NMR. Statistical analyses revealed differential metabolic signatures, impacted metabolic pathways, and glioma biomarkers. Each method was efficient to determine biomarkers. The highlighted metabolites including glucose, myo-inositol, sarcosine, glycine, alanine, and pyruvate for solution-state NMR and proline, myo-inositol, acetate, and glucose for HR-MAS show very good performances in discriminating samples according to their nature with data mining based on receiver operating characteristic curves. Combining results allows for a more complete view of induced disturbances and opens the possibility of deciphering the biochemical mechanisms of these tumors. The identified biomarkers provide a means to rebalance specific pathways through targeted metabolic therapy and to study the effects of pharmacological treatments using Drosophila as a model organism.

Blood-Based Biomarkers for Glioma in the Context of Gliomagenesis: A Systematic Review

BACKGROUND

Gliomas are the most common and aggressive tumors of the central nervous system. A robust and widely used blood-based biomarker for glioma has not yet been identified. In recent years, a plethora of new research on blood-based biomarkers for glial tumors has been published. In this review, we question which molecules, including proteins, nucleic acids, circulating cells, and metabolomics, are most promising blood-based biomarkers for glioma diagnosis, prognosis, monitoring and other purposes, and align them to the seminal processes of cancer.

METHODS

The Pubmed and Embase databases were systematically searched. Biomarkers were categorized in the identified biomolecules and biosources. Biomarker characteristics were assessed using the area under the curve (AUC), accuracy, sensitivity and/or specificity values and the degree of statistical significance among the assessed clinical groups was reported.

RESULTS

7,919 references were identified: 3,596 in PubMed and 4,323 in Embase. Following screening of titles, abstracts and availability of full-text, 262 articles were included in the final systematic review. Panels of multiple biomarkers together consistently reached AUCs >0.8 and accuracies >80% for various purposes but especially for diagnostics. The accuracy of single biomarkers, consisting of only one measurement, was far more variable, but single microRNAs and proteins are generally more promising as compared to other biomarker types.

CONCLUSION

Panels of microRNAs and proteins are most promising biomarkers, while single biomarkers such as GFAP, IL-10 and individual miRNAs also hold promise. It is possible that panels are more accurate once these are involved in different, complementary cancer-related molecular pathways, because not all pathways may be dysregulated in cancer patients. As biomarkers seem to be increasingly dysregulated in patients with short survival, higher tumor grades and more pathological tumor types, it can be hypothesized that more pathways are dysregulated as the degree of malignancy of the glial tumor increases. Despite, none of the biomarkers found in the literature search seem to be currently ready for clinical implementation, and most of the studies report only preliminary application of the identified biomarkers. Hence, large-scale validation of currently identified and potential novel biomarkers to show clinical utility is warranted.

Identification of colorectal neoplasia by using serum bile acid profile

BACKGROUND

Colonoscopy is the gold standard for detecting earlier stages of CRC, although screening of patients is difficult because of invasiveness, low compliance and procedural health risks. Therefore, the need for new screening methods for CRC is rising. Previous studies have demonstrated the diagnostic ability of serum BAs; however, the results have been inconsistent. In this study, we conducted a comprehensive analysis of serum BAs from patients with CRC and verified their diagnostic ability to detect CRC.

METHODS

A total of 56 CRC patients (n = 14 each of stages I-IV), 59 patients with colonic adenoma and 60 healthy controls were included. Age and sex were matched for each group. Serum BA compositions were measured by LC-MS/MS and serum concentration of 30 types of BAs were analysed by discriminant analysis with multidimensional scaling method.

RESULTS

Free CA, 3epi-DCA&CDCA, 3-dehydro CA, GCA and TCA were extracted as principal component (PC) 1 and free 3-dehydroDCA as PC 2 by canonical discriminant function coefficients. The verification of discriminability using cross-validation method revealed that the correct classification rate was 66.3% for original data and 52.6% for cross-validation data.

CONCLUSIONS

A combined analysis using comprehensive serum BA concentration can be an efficient method for screening CRC.

Pre-diagnostic circulating concentrations of fat-soluble vitamins and risk of glioma in three cohort studies

Few prospective studies have evaluated the relation between fat-soluble vitamins and glioma risk. Using three cohorts-UK Biobank (UKB), Nurses' Health Study (NHS), and Health Professionals Follow-Up Study (HPFS), we investigated associations of pre-diagnostic concentrations of fat-soluble vitamins D, A, and E with incident glioma. In 346,785 participants (444 cases) in UKB, associations with vitamin D (25-hydroxyvitamin D [25(OH)D]) were evaluated by Cox proportional hazards regression. In NHS (52 cases, 104 controls) and HPFS (32 cases, 64 controls), associations with 25(OH)D, vitamin A (retinol), and vitamin E (α- and γ-tocopherol) were assessed using conditional logistic regression. Our results suggested plasma concentrations of 25(OH)D and retinol were not associated with glioma risk. Comparing the highest to lowest tertile, the multivariable hazard ratio (MVHR) for 25(OH)D was 0.87 (95% confidence interval [CI] 0.68-1.11) in UKB and the multivariable risk ratio (MVRR) was 0.97 (95% CI 0.51-1.85) in NHS and HPFS. In NHS and HPFS, the MVRR for the same comparison for retinol was 1.16 (95% CI 0.56-2.38). Nonsignificant associations were observed for α-tocopherol (MVRRtertile3vs1 = 0.61, 95% CI 0.29-1.32) and γ-tocopherol (MVRR tertile3vs1 = 1.30, 95% CI 0.63-2.69) that became stronger in 4-year lagged analyses. Further investigation is warranted on a potential association between α- and γ-tocopherol and glioma risk.

Metabolomics of Glioma

Metabolic reprogramming is an important characteristics of glioma, the most common form of malignant brain tumor. In this chapter, we aim to discuss some of the recently discovered metabolic alterations in glioma, including the dysregulated TCA cycle, amino acid, nucleotide, and lipid metabolism. We have also detailed some of the metabolomic applications in gliomas, particularly the analyses of body fluids and tissues of glioma patients. With new improvement of the technology, metabolomics will become a powerful tool to discover truly meaningful biomarkers for clinical applications in gliomas. Metabolomic studies of gliomas will also facilitate a better understanding of the molecular targets/pathways and the development of new therapeutic treatments for this devastating disease.

A Prospective Investigation of Circulating Metabolome Identifies Potential Biomarkers for Gastric Cancer Risk

BACKGROUND

Metabolomics is widely used to identify potential novel biomarkers for cancer risk. No investigation, however, has been conducted to prospectively evaluate the role of perturbation of metabolome in gastric cancer development.

METHODS

250 incident cases diagnosed with primary gastric cancer were selected from the Shanghai Women's Health and the Shanghai Men's Health Study, and each was individually matched to one control by incidence density sampling. An untargeted global profiling platform was used to measure approximately 1,000 metabolites in prediagnostic plasma. Conditional logistic regression was utilized to generate ORs and P values.

RESULTS

Eighteen metabolites were associated with gastric cancer risk at P < 0.01. Among them, 11 metabolites were lysophospholipids or lipids of other classes; for example, 1-(1-enyl-palmitoyl)-GPE (P-16:0) (OR = 1.56; P = 1.89 × 10^-4). Levels of methylmalonate, a suggested biomarker of vitamin B12 deficiency, was correlated with increased gastric cancer risk (OR = 1.42; P = 0.004). Inverse associations were found for three biomarkers for coffee/tea consumption (3-hydroxypyridine sulfate, quinate and N-(2-furoyl) glycine), although the associations were only significant when comparing cases that were diagnosed within 5 years after the blood collection to matched controls. Most of the identified associations were more profound in women and never smokers than their male or ever smoking counterparts and some with notable significant interactions.

CONCLUSIONS

Our study identified multiple potential risk biomarkers for gastric cancer independent of Helicobacter pylori infection and other major risk factors.

IMPACT

New risk-assessment tools to identify high-risk population could be developed to improve prevention of gastric cancer.See related commentary by Drew et al., p. 1601.

Metabolomic Analysis of Small Extracellular Vesicles Derived from Pancreatic Cancer Cells Cultured under Normoxia and Hypoxia

Extracellular vesicles (EVs) released from cancer cells contribute to various malignant phenotypes of cancer, including metastasis, cachexia, and angiogenesis. Although DNA, mRNAs, miRNAs, and proteins contained in EVs have been extensively studied, the function of metabolites in EVs remains unclear. In this study, we performed a comprehensive metabolomic analysis of pancreatic cancer cells, PANC-1, cultured under different oxygen concentrations, and small EVs (sEVs) released from them, considering the fact that hypoxia contributes to the malignant behavior of cells in pancreatic cancer, which is a poorly diagnosed cancer. sEVs were collected by ultracentrifugation, and hydrophilic metabolites were analyzed using capillary ion chromatography-mass spectrometry and liquid chromatography-mass spectrometry, and lipids were analyzed by supercritical fluid chromatography-tandem mass spectrometry. A total of 140 hydrophilic metabolites and 494 lipids were detected in sEVs, and their profiles were different from those in cells. In addition, the metabolomic profile of sEVs was observed to change under hypoxic stress, and an increase in metabolites involved in angiogenesis was also detected. We reveal the hallmark of the metabolites contained in sEVs and the effect of tumor hypoxia on their profiles, which may help in understanding EV-mediated cancer malignancy.

Metabolomic profiling for second primary lung cancer: A pilot case-control study

OBJECTIVES

Lung cancer survivors have a high risk of developing a second primary lung cancer (SPLC). While national screening guidelines have been established for initial primary lung cancer (IPLC), no consensus guidelines exist for SPLC. Furthermore, the factors that contribute to SPLC risk have not been established. This study examines the potential for using serum metabolomics to identify metabolite biomarkers that differ between SPLC cases and IPLC controls.

MATERIAL AND METHODS

In this pilot case-control study, we applied an untargeted metabolomics approach based on ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) to serum samples of 82 SPLC cases and 82 frequency matched IPLC controls enrolled in the Boston Lung Cancer Study. Random forest and unconditional logistic regression models identified metabolites associated with SPLC. Candidate metabolites were integrated into a SPLC risk prediction model and the model performance was evaluated through a risk stratification approach.

RESULTS

The untargeted analysis detected 1008 named and 316 unnamed metabolites among all study participants. Metabolites that were significantly associated with SPLC (False Discovery Rate q-value < 0.2) included 5-methylthioadenosine (odds ratio [OR] = 2.04, 95 % confidence interval [CI] 1.39-3.01; P = 2.8 × 10^-4) and phenylacetylglutamine (OR = 2.65, 95 % CI 1.56-4.51; P = 3.2 × 10^-4), each exhibiting approximately 1.5-fold increased levels among SPLC cases versus IPLC controls. In stratifying the study participants across quartiles of estimated SPLC risk, the risk prediction model identified a significantly higher proportion of SPLC cases in the fourth compared to the first quartile (68.3 % versus 39.0 %; P = 0.044).

CONCLUSION

SPLC cases may have distinct metabolomic profiles compared to those in IPLC patients without SPLC. A risk stratification approach integrating metabolomics may be useful for distinguishing patients based on SPLC risk. Prospective validation studies are needed to further evaluate the potential for leveraging metabolomics in SPLC surveillance and screening.

Cysteine metabolic circuitries: druggable targets in cancer

To enable survival in adverse conditions, cancer cells undergo global metabolic adaptations. The amino acid cysteine actively contributes to cancer metabolic remodelling on three different levels: first, in its free form, in redox control, as a component of the antioxidant glutathione or its involvement in protein s-cysteinylation, a reversible post-translational modification; second, as a substrate for the production of hydrogen sulphide (H2S), which feeds the mitochondrial electron transfer chain and mediates per-sulphidation of ATPase and glycolytic enzymes, thereby stimulating cellular bioenergetics; and, finally, as a carbon source for epigenetic regulation, biomass production and energy production. This review will provide a systematic portrayal of the role of cysteine in cancer biology as a source of carbon and sulphur atoms, the pivotal role of cysteine in different metabolic pathways and the importance of H2S as an energetic substrate and signalling molecule. The different pools of cysteine in the cell and within the body, and their putative use as prognostic cancer markers will be also addressed. Finally, we will discuss the pharmacological means and potential of targeting cysteine metabolism for the treatment of cancer.

Inference of Subpathway Activity Profiles Reveals Metabolism Abnormal Subpathway Regions in Glioblastoma Multiforme

Glioblastoma, also known as glioblastoma multiforme (GBM), is the most malignant form of glioma and represents 81% of malignant brain and central nervous system (CNS) tumors. Like most cancers, GBM causes metabolic recombination to promote cell survival, proliferation, and invasion of cancer cells. In this study, we propose a method for constructing the metabolic subpathway activity score matrix to accurately identify abnormal targets of GBM metabolism. By integrating gene expression data from different sequencing methods, our method identified 25 metabolic subpathways that were significantly abnormal in the GBM patient population, and most of these subpathways have been reported to have an effect on GBM. Through the analysis of 25 GBM-related metabolic subpathways, we found that (S)-2,3-Epoxysqualene, which was at the central region of the sterol biosynthesis subpathway, may have a greater impact on the entire pathway, suggesting a potential high association with GBM. Analysis of CCK8 cell activity indicated that (S)-2,3-Epoxysqualene can indeed inhibit the activity of U87-MG cells. By flow cytometry, we demonstrated that (S)-2,3-Epoxysqualene not only arrested the U87-MG cell cycle in the G0/G1 phase but also induced cell apoptosis. These results confirm the reliability of our proposed metabolic subpathway identification method and suggest that (S)-2,3-Epoxysqualene has potential therapeutic value for GBM. In order to make the method more broadly applicable, we have developed an R system package crmSubpathway to perform disease-related metabolic subpathway identification and it is freely available on the GitHub (https://github.com/hanjunwei-lab/crmSubpathway).

Using arterial-venous analysis to characterize cancer metabolic consumption in patients

Understanding tumor metabolism holds the promise of new insights into cancer biology, diagnosis and treatment. To assess human cancer metabolism, here we report a method to collect intra-operative samples of blood from an artery directly upstream and a vein directly downstream of a brain tumor, as well as samples from dorsal pedal veins of the same patients. After performing targeted metabolomic analysis, we characterize the metabolites consumed and produced by gliomas in vivo by comparing the arterial supply and venous drainage. N-acetylornithine, D-glucose, putrescine, and L-acetylcarnitine are consumed in relatively large amounts by gliomas. Conversely, L-glutamine, agmatine, and uridine 5-monophosphate are produced in relatively large amounts by gliomas. Further we verify that D-2-hydroxyglutarate (D-2HG) is high in venous plasma from patients with isocitrate dehydrogenases1 (IDH1) mutations. Through these paired comparisons, we can exclude the interpatient variation that is present in plasma samples usually taken from the cubital vein.

Cellular metabolism is altered in many cancer types and the advent of metabolomics has allowed us to understand more about how this is dysregulated. Here, the authors report a method named CARVE to analyse the arterial supply and venous drainage of glioma patients during surgery and identify the metabolites that may be consumed and produced by the cancer.

Metabolic Variations between Low-Grade and High-Grade Gliomas-Profiling by 1H NMR Spectroscopy

Altered cellular metabolism is one of the crucial hallmarks of glioma that deserves exploration, as the metabolites act as direct indicators of protein function and genetic variations. The current study focused on the metabolomic profiling of patients from whom glioma specimens were obtained for the identification of specific metabolites that could distinguish the low grade and high grade. In the current study, ¹H NMR spectroscopy was carried out and the data were analyzed by partial least-squares discriminant analysis (PLS-DA) and orthogonal projection to latent structure with discriminant analysis (OPLS-DA). Pathway analysis was done to associate characteristic metabolites with the grades of sample using MetaboAnalyst 4.0 software based on the KEGG metabolic pathways database. Distinctive metabolic profiles among low- and high-grade gliomas with top 15 characteristic metabolites that could discriminate these grades were identified on the basis of their VIP scores from the OPLS-DA model. The major altered metabolic pathways include choline, taurine and hypotaurine, glutamate/glutamine, glutathione, and phenyl alanine/tyrosine, which were found to be consistent with the particular grade of a sample. Our study clearly demonstrated a characteristic metabolic profile of individual grades of glioma, suggesting that an altered metabolism is consistent with the specific grades of glioma appreciation and could lead to the development novel treatment strategies.

Liquid biopsies for diagnosing and monitoring primary tumors of the central nervous system

Obtaining diagnostic specimens, notably to monitor disease course in cancer patients undergoing therapy, is an emerging area of research, however, with few clinical implications so far in the field of Neuro-oncology. Specifically for patients with primary brain tumors where repeat biosampling from the tumor and clinical decision making based on neuroimaging alone remain challenging, this area may assume a central role. In principle, sampling could focus on blood, cerebrospinal fluid or urine with differential sensitivities and specificities of findings that differ between specific parameters and target molecules. These include protein, mRNA, miRNA, cell-free DNA, either freely circulating or as cargo of extracellular vesicles, as well circulating tumor cells. The most solid biomarkers are those directly reflecting neoplastic disease, e.g., in the case of primary brain tumors isocitrate dehydrogenase mutation or epidermal growth factor receptor variant III. Importantly, the main goals of liquid biopsy marker development are to better understand response to therapy, natural evolution and emergence of resistant clones, rather than obviating the need for surgical interventions which remain to be a mainstay of therapy for the vast majority of primary brain tumors.

Mass spectrometry based proteomics and metabolomics in personalized oncology

Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.

Current and Future Trends on Diagnosis and Prognosis of Glioblastoma: From Molecular Biology to Proteomics

Glioblastoma multiforme is the most aggressive malignant tumor of the central nervous system. Due to the absence of effective pharmacological and surgical treatments, the identification of early diagnostic and prognostic biomarkers is of key importance to improve the survival rate of patients and to develop new personalized treatments. On these bases, the aim of this review article is to summarize the current knowledge regarding the application of molecular biology and proteomics techniques for the identification of novel biomarkers through the analysis of different biological samples obtained from glioblastoma patients, including DNA, microRNAs, proteins, small molecules, circulating tumor cells, extracellular vesicles, etc. Both benefits and pitfalls of molecular biology and proteomics analyses are discussed, including the different mass spectrometry-based analytical techniques, highlighting how these investigation strategies are powerful tools to study the biology of glioblastoma, as well as to develop advanced methods for the management of this pathology.

Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS)

The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.

The Consortium of Metabolomics Studies (COMETS): Metabolomics in 47 Prospective Cohort Studies

The Consortium of Metabolomics Studies (COMETS) was established in 2014 to facilitate large-scale collaborative research on the human metabolome and its relationship with disease etiology, diagnosis, and prognosis. COMETS comprises 47 cohorts from Asia, Europe, North America, and South America that together include more than 136,000 participants with blood metabolomics data on samples collected from 1985 to 2017. Metabolomics data were provided by 17 different platforms, with the most frequently used labs being Metabolon, Inc. (14 cohorts), the Broad Institute (15 cohorts), and Nightingale Health (11 cohorts). Participants have been followed for a median of 23 years for health outcomes including death, cancer, cardiovascular disease, diabetes, and others; many of the studies are ongoing. Available exposure-related data include common clinical measurements and behavioral factors, as well as genome-wide genotype data. Two feasibility studies were conducted to evaluate the comparability of metabolomics platforms used by COMETS cohorts. The first study showed that the overlap between any 2 different laboratories ranged from 6 to 121 metabolites at 5 leading laboratories. The second study showed that the median Spearman correlation comparing 111 overlapping metabolites captured by Metabolon and the Broad Institute was 0.79 (interquartile range, 0.56-0.89).

Prospective serum metabolomic profiling of lethal prostate cancer

Impaired metabolism may play an important role in the pathogenesis of lethal prostate cancer, yet there is a paucity of evidence regarding the association. We conducted a large prospective serum metabolomic analysis of lethal prostate cancer in 523 cases and 523 matched controls nested within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Median time from baseline fasting serum collection to prostate cancer death was 18 years (maximum 30 years). We identified 860 known biochemicals through an ultrahigh-performance LC-MS/MS platform. Conditional logistic regression models estimated odds ratios (OR) and 95% confidence intervals of risk associated with 1-standard deviation (s.d.) increases in log-metabolite signals. We identified 34 metabolites associated with lethal prostate cancer with a false discovery rate (FDR) < 0.15. Notably, higher serum thioproline, and thioproline combined with two other cysteine-related amino acids and redox metabolites, cystine and cysteine, were associated with reduced risk (1-s.d. OR = 0.75 and 0.71, respectively; p ≤ 8.2 × 10^-5 ). By contrast, the dipeptide leucylglycine (OR = 1.36, p = 8.2 × 10^-5 ), and three gamma-glutamyl amino acids (OR = 1.28-1.30, p ≤ 4.6 × 10^-4 ) were associated with increased risk of lethal prostate cancer. Cases with metastatic disease at diagnosis (n = 179) showed elevated risk for several lipids, including especially the ketone body 3-hydroxybutyrate (BHBA), acyl carnitines, and dicarboxylic fatty acids (1.37 ≤ OR ≤ 1.49, FDR < 0.15). These findings provide a prospective metabolomic profile of lethal prostate cancer characterized by altered biochemicals in the redox, dipeptide, pyrimidine, and gamma-glutamyl amino acid pathways, whereas ketone bodies and fatty acids were associated specifically with metastatic disease.

Raman spectroscopic screening of high and low molecular weight fractions of human serum

This study explores the suitability of Raman spectroscopy as a bioanalytical tool, when coupled with ultra-filtration and multivariate analysis, to detect imbalances in both high molecular weight and low molecular weight fractions of the same samples of human patient serum, in the native liquid form.

Glioma Cell Secretion: A Driver of Tumor Progression and a Potential Therapeutic Target

Cellular secretion is an important mediator of cancer progression. Secreted molecules in glioma are key components of complex autocrine and paracrine pathways that mediate multiple oncogenic pathologies. In this review, we describe tumor cell secretion in high-grade glioma and highlight potential novel therapeutic opportunities. Cancer Res; 78(21); 6031-9. ©2018 AACR.

Profiling the Metabolism of Human Cells by Deep 13C Labeling

Studying metabolic activities in living cells is crucial for understanding human metabolism, but facile methods for profiling metabolic activities in an unbiased, hypothesis-free manner are still lacking. To address this need, we here introduce the deep-labeling method, which combines a custom ¹³C medium with high-resolution mass spectrometry. A proof-of-principle study on human cancer cells demonstrates that deep labeling can identify hundreds of endogenous metabolites as well as active and inactive pathways. For example, protein and nucleic acids were almost exclusively de novo synthesized, while lipids were partly derived from serum; synthesis of cysteine, carnitine, and creatine was absent, suggesting metabolic dependencies; and branched-chain keto acids (BCKAs) were formed and metabolized to short-chain acylcarnitines, but did not enter the tricarboxylic acid cycle. Remarkably, BCKAs could substitute for essential amino acids to support growth. The deep-labeling method may prove useful to map metabolic phenotypes across a range of cell types and conditions.

Cortex and hippocampus DNA epigenetic response to a long-term arsenic exposure via drinking water

The neurotoxicity of arsenic is a serious health problem, especially for children. DNA epigenetic change may be an important pathogenic mechanism, but the molecular pathway remains obscure. In this study, the weaned male Sprague-Dawly (SD) rats were treated with arsenic trioxide via drinking water for 6 months, simulating real developmental exposure situation of children. Arsenic exposure impaired the cognitive abilities, and altered the expression of neuronal activity-regulated genes. Total arsenic concentrations of cortex and hippocampus tissues were significantly increased in a dose-dependent manner. The reduction in 5-methylcytosine (5 mC) and 5-hydroxymethylcytosine (5hmC) levels as well as the down-regulation of DNA methyltransferases (DNMTs) and ten-eleven translocations (TETs) expression suggested that DNA methylation/demethylation processes were significantly suppressed in brain tissues. S-adenosylmethionine (SAM) level wasn't changed, but the expression of the important indicators of oxidative/anti-oxidative balance and tricarboxylic acid (TCA) cycle was significantly deregulated. Overall, arsenic can disrupt oxidative/anti-oxidative balance, further inhibit TETs expression through TCA cycle and alpha-ketoglutarate (α-KG) pathway, and consequently cause DNA methylation/demethylation disruption. The present study implies oxidative stress but not SAM depletion may lead to DNA epigenetic alteration and arsenic neurotoxicity.