Metabolomic screening of pre-diagnostic serum samples identifies association between α- and γ-tocopherols and glioblastoma risk

Blood based metabolic markers of glioma from pre-diagnosis to surgery

Gliomas are highly complex and metabolically active brain tumors associated with poor prognosis. Recent reports have found altered levels of blood metabolites during early tumor development, suggesting that tumor development could be detected several years before clinical manifestation. In this study, we performed metabolite analyses of blood samples collected from healthy controls and future glioma patients, up to eight years before glioma diagnosis, and on the day of glioma surgery. We discovered that metabolites related to early glioma development were associated with an increased energy turnover, as highlighted by elevated levels of TCA-related metabolites such as fumarate, malate, lactate and pyruvate in pre-diagnostic cases. We also found that metabolites related to glioma progression at surgery were primarily high levels of amino acids and metabolites of amino acid catabolism, with elevated levels of 11 amino acids and two branched-chain alpha-ketoacids, ketoleucine and ketoisoleucine. High amino acid turnover in glioma tumor tissue is currently utilized for PET imaging, diagnosis and delineation of tumor margins. By examining blood-based metabolic progression patterns towards disease onset, we demonstrate that this high amino acid turnover is also detectable in a simple blood sample. These findings provide additional insight of metabolic alterations during glioma development and progression.

Circulating Liquid Biopsy Biomarkers in Glioblastoma: Advances and Challenges

Gliomas, particularly glioblastoma (GBM), represent the most prevalent and aggressive tumors of the central nervous system (CNS). Despite recent treatment advancements, patient survival rates remain low. The diagnosis of GBM traditionally relies on neuroimaging methods such as magnetic resonance imaging (MRI) or computed tomography (CT) scans and postoperative confirmation via histopathological and molecular analysis. Imaging techniques struggle to differentiate between tumor progression and treatment-related changes, leading to potential misinterpretation and treatment delays. Similarly, tissue biopsies, while informative, are invasive and not suitable for monitoring ongoing treatments. These challenges have led to the emergence of liquid biopsy, particularly through blood samples, as a promising alternative for GBM diagnosis and monitoring. Presently, blood and cerebrospinal fluid (CSF) sampling offers a minimally invasive means of obtaining tumor-related information to guide therapy. The idea that blood or any biofluid tests can be used to screen many cancer types has huge potential. Tumors release various components into the bloodstream or other biofluids, including cell-free nucleic acids such as microRNAs (miRNAs), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), proteins, extracellular vesicles (EVs) or exosomes, metabolites, and other factors. These factors have been shown to cross the blood-brain barrier (BBB), presenting an opportunity for the minimally invasive monitoring of GBM as well as for the real-time assessment of distinct genetic, epigenetic, transcriptomic, proteomic, and metabolomic changes associated with brain tumors. Despite their potential, the clinical utility of liquid biopsy-based circulating biomarkers is somewhat constrained by limitations such as the absence of standardized methodologies for blood or CSF collection, analyte extraction, analysis methods, and small cohort sizes. Additionally, tissue biopsies offer more precise insights into tumor morphology and the microenvironment. Therefore, the objective of a liquid biopsy should be to complement and enhance the diagnostic accuracy and monitoring of GBM patients by providing additional information alongside traditional tissue biopsies. Moreover, utilizing a combination of diverse biomarker types may enhance clinical effectiveness compared to solely relying on one biomarker category, potentially improving diagnostic sensitivity and specificity and addressing some of the existing limitations associated with liquid biomarkers for GBM. This review presents an overview of the latest research on circulating biomarkers found in GBM blood or CSF samples, discusses their potential as diagnostic, predictive, and prognostic indicators, and discusses associated challenges and future perspectives.

HCMMD: systematic evaluation of metabolites in body fluids as liquid biopsy biomarker for human cancers

Metabolomics is a rapidly expanding field in systems biology used to measure alterations of metabolites and identify metabolic biomarkers in response to disease processes. The discovery of metabolic biomarkers can improve early diagnosis, prognostic prediction, and therapeutic intervention for cancers. However, there are currently no databases that provide a comprehensive evaluation of the relationship between metabolites and cancer processes. In this review, we summarize reported metabolites in body fluids across pan-cancers and characterize their clinical applications in liquid biopsy. We conducted a search for metabolic biomarkers using the keywords ("metabolomics" OR "metabolite") AND "cancer" in PubMed. Of the 22,254 articles retrieved, 792 were deemed potentially relevant for further review. Ultimately, we included data from 573,300 samples and 17,083 metabolic biomarkers. We collected information on cancer types, sample size, the human metabolome database (HMDB) ID, metabolic pathway, area under the curve (AUC), sensitivity and specificity of metabolites, sample source, detection method, and clinical features were collected. Finally, we developed a user-friendly online database, the Human Cancer Metabolic Markers Database (HCMMD), which allows users to query, browse, and download metabolite information. In conclusion, HCMMD provides an important resource to assist researchers in reviewing metabolic biomarkers for diagnosis and progression of cancers.

Modifications of Blood Molecular Components after Treatment with Low Ozone Concentrations

The ex vivo treatment of a limited volume of blood with gaseous oxygen-ozone (O2-O3) mixtures and its rapid reinfusion into the patient is a widespread medical procedure. O3 instantly reacts with the blood's antioxidant systems, disappearing before reinfusion, although the molecules formed act as messengers in the organism, inducing multiple antioxidant and anti-inflammatory responses. An appropriate dose of O3 is obviously essential to ensure both safety and therapeutic efficacy, and in recent years, the low-dose O3 concept has led to a significant reduction in the administered O3 concentrations. However, the molecular events triggered by such low concentrations in the blood still need to be fully elucidated. In this basic study, we analysed the molecular modifications induced ex vivo in sheep blood by 5 and 10 µg O3/mL O2 by means of a powerful metabolomics analysis in association with haemogas, light microscopy and bioanalytical assays. This combined approach revealed increased oxygenation and an increased antioxidant capacity in the O3-treated blood, which accorded with the literature. Moreover, original information was obtained on the impact of these low O3 concentrations on the metabolic pathways of amino acids, carbohydrates, lipids and nucleotides, with the modified metabolites being mostly involved in the preservation of the oxidant-antioxidant balance and in energy production.

Liquid biopsy: creating opportunities in brain space

In recent years, liquid biopsy has emerged as an alternative method to diagnose and monitor tumors. Compared to classical tissue biopsy procedures, liquid biopsy facilitates the repetitive collection of diverse cellular and acellular analytes from various biofluids in a non/minimally invasive manner. This strategy is of greater significance for high-grade brain malignancies such as glioblastoma as the quantity and accessibility of tumors are limited, and there are collateral risks of compromised life quality coupled with surgical interventions. Currently, blood and cerebrospinal fluid (CSF) are the most common biofluids used to collect circulating cells and biomolecules of tumor origin. These liquid biopsy analytes have created opportunities for real-time investigations of distinct genetic, epigenetic, transcriptomics, proteomics, and metabolomics alterations associated with brain tumors. This review describes different classes of liquid biopsy biomarkers present in the biofluids of brain tumor patients. Moreover, an overview of the liquid biopsy applications, challenges, recent technological advances, and clinical trials in the brain have also been provided.

Metabolic remodeling in astrocytes: Paving the path to brain tumor development

The brain is a highly metabolic organ, composed of multiple cell classes, that controls crucial functions of the body. Although neurons have traditionally been the main protagonist, astrocytes have gained significant attention over the last decade. In this regard, astrocytes are a type of glial cells that have recently emerged as critical regulators of central nervous system (CNS) function and play a significant role in maintaining brain energy metabolism. However, in certain scenarios, astrocyte behavior can go awry, which poses a significant threat to brain integrity and function. This is definitively the case for mutations that turn normal astrocytes and astrocytic precursors into gliomas, an aggressive type of brain tumor. In addition, healthy astrocytes can interact with tumor cells, becoming part of the tumor microenvironment and influencing disease progression. In this review, we discuss the recent evidence suggesting that disturbed metabolism in astrocytes can contribute to the development and progression of fatal human diseases such as cancer. Emphasis is placed on detailing the molecular bases and metabolic pathways of this disease and highlighting unique metabolic vulnerabilities that can potentially be exploited to develop successful therapeutic opportunities.

Adding Value to Liquid Biopsy for Brain Tumors: The Role of Imaging

Clinical management in neuro-oncology has changed to an integrative approach that incorporates molecular profiles alongside histopathology and imaging findings. While the World Health Organization (WHO) guideline recommends the genotyping of informative alterations as a routine clinical practice for central nervous system (CNS) tumors, the acquisition of tumor tissue in the CNS is invasive and not always possible. Liquid biopsy is a non-invasive approach that provides the opportunity to capture the complex molecular heterogeneity of the whole tumor through the detection of circulating tumor biomarkers in body fluids, such as blood or cerebrospinal fluid (CSF). Despite all of the advantages, the low abundance of tumor-derived biomarkers, particularly in CNS tumors, as well as their short half-life has limited the application of liquid biopsy in clinical practice. Thus, it is crucial to identify the factors associated with the presence of these biomarkers and explore possible strategies that can increase the shedding of these tumoral components into biological fluids. In this review, we first describe the clinical applications of liquid biopsy in CNS tumors, including its roles in the early detection of recurrence and monitoring of treatment response. We then discuss the utilization of imaging in identifying the factors that affect the detection of circulating biomarkers as well as how image-guided interventions such as focused ultrasound can help enhance the presence of tumor biomarkers through blood-brain barrier (BBB) disruption.

Altered plasma metabolite levels can be detected years before a glioma diagnosis

Genetic and metabolic changes in tissue and blood are reported to occur several years before glioma diagnosis. Since gliomas are currently detected late, a liquid biopsy for early detection could affect the quality of life and prognosis of patients. Here, we present a nested case-control study of 550 prediagnostic glioma cases and 550 healthy controls from the Northern Sweden Health and Disease study (NSHDS) and the European Prospective Investigation into Cancer and Nutrition (EPIC) study. We identified 93 significantly altered metabolites related to glioma development up to 8 years before diagnosis. Out of these metabolites, a panel of 20 selected metabolites showed strong disease correlation and a consistent progression pattern toward diagnosis in both the NSHDS and EPIC cohorts, and they separated future cases from controls independently of biological sex. The blood metabolite panel also successfully separated both lower-grade glioma and glioblastoma cases from controls, up to 8 years before diagnosis in patients within the NSHDS cohort and up to 2 years before diagnosis in EPIC. Pathway enrichment analysis detected metabolites related to the TCA cycle, Warburg effect, gluconeogenesis, and cysteine, pyruvate, and tyrosine metabolism as the most affected.

Characteristics of Cancer Epidemiology Studies That Employ Metabolomics: A Scoping Review

An increasing number of cancer epidemiology studies use metabolomics assays. This scoping review characterizes trends in the literature in terms of study design, population characteristics, and metabolomics approaches and identifies opportunities for future growth and improvement. We searched PubMed/MEDLINE, Embase, Scopus, and Web of Science: Core Collection databases and included research articles that used metabolomics to primarily study cancer, contained a minimum of 100 cases in each main analysis stratum, used an epidemiologic study design, and were published in English from 1998 to June 2021. A total of 2,048 articles were screened, of which 314 full texts were further assessed resulting in 77 included articles. The most well-studied cancers were colorectal (19.5%), prostate (19.5%), and breast (19.5%). Most studies used a nested case-control design to estimate associations between individual metabolites and cancer risk and a liquid chromatography-tandem mass spectrometry untargeted or semi-targeted approach to measure metabolites in blood. Studies were geographically diverse, including countries in Asia, Europe, and North America; 27.3% of studies reported on participant race, the majority reporting White participants. Most studies (70.2%) included fewer than 300 cancer cases in their main analysis. This scoping review identified key areas for improvement, including needs for standardized race and ethnicity reporting, more diverse study populations, and larger studies.

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.

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.

Antioxidants in brain tumors: current therapeutic significance and future prospects

Brain cancer is regarded among the deadliest forms of cancer worldwide. The distinct tumor microenvironment and inherent characteristics of brain tumor cells virtually render them resistant to the majority of conventional and advanced therapies. Oxidative stress (OS) is a key disruptor of normal brain homeostasis and is involved in carcinogenesis of different forms of brain cancers. Thus, antioxidants may inhibit tumorigenesis by preventing OS induced by various oncogenic factors. Antioxidants are hypothesized to inhibit cancer initiation by endorsing DNA repair and suppressing cancer progression by creating an energy crisis for preneoplastic cells, resulting in antiproliferative effects. These effects are referred to as chemopreventive effects mediated by an antioxidant mechanism. In addition, antioxidants minimize chemotherapy-induced nonspecific organ toxicity and prolong survival. Antioxidants also support the prooxidant chemistry that demonstrate chemotherapeutic potential, particularly at high or pharmacological doses and trigger OS by promoting free radical production, which is essential for activating cell death pathways. A growing body of evidence also revealed the roles of exogenous antioxidants as adjuvants and their ability to reverse chemoresistance. In this review, we explain the influences of different exogenous and endogenous antioxidants on brain cancers with reference to their chemopreventive and chemotherapeutic roles. The role of antioxidants on metabolic reprogramming and their influence on downstream signaling events induced by tumor suppressor gene mutations are critically discussed. Finally, the review hypothesized that both pro- and antioxidant roles are involved in the anticancer mechanisms of the antioxidant molecules by killing neoplastic cells and inhibiting tumor recurrence followed by conventional cancer treatments. The requirements of pro- and antioxidant effects of exogenous antioxidants in brain tumor treatment under different conditions are critically discussed along with the reasons behind the conflicting outcomes in different reports. Finally, we also mention the influencing factors that regulate the pharmacology of the exogenous antioxidants in brain cancer treatment. In conclusion, to achieve consistent clinical outcomes with antioxidant treatments in brain cancers, rigorous mechanistic studies are required with respect to the types, forms, and stages of brain tumors. The concomitant treatment regimens also need adequate consideration.

Design, Synthesis and Activity of New N1-Alkyl Tryptophan Functionalized Dendrimeric Peptides against Glioblastoma

Background: Due to resistance to conventional therapy, a blood–brain barrier that results in poor drug delivery, and a high potential for metastasis, glioblastoma (GBM) presents a great medical challenge. Since the repertoire of the possible therapies is very limited, novel therapeutic strategies require new drugs as well as new approaches. The multiple roles played by L-tryptophan (Trp) in tumorigenesis of GBM and the previously found antiproliferative properties of Trp-bearing dendrimers against this malignancy prompted us to design novel polyfunctional peptide-based dendrimers covalently attached to N¹-alkyl tryptophan (Trp) residues. Their antiproliferative properties against GBM and normal human astrocytes (NHA) and their antioxidant potential were tested. Methods: Two groups of amphiphilic peptide dendrimers terminated with N¹-butyl and N¹-aminopentane tryptophan were designed. The influence of dendrimers on viability of NHA and human GBM cell lines, displaying different genetic backgrounds and tumorigenic potentials, was determined by the MTT test. The influence of compounds on the clonogenic potential of GBM cells was assessed by colony-formation assay. Dendrimers were tested for radical scavenging potency as well as redox capability (DPPH, ABTS, and FRAP models). Results: Several peptide dendrimers functionalized with N¹-alkyl-tryptophan at 5 µM concentration exhibited high selectivity towards GBM cells retaining 85–95% viable NHA cells while killing cancer cells. In both the MTT and colony-formation assays, compounds 21 (functionalized with N¹-butyl-Trp and (+)8 charged) and 25 (functionalized with N¹-aminopentane-Trp and (+)12 charged) showed the most promise for their development into anticancer drugs. According to ABTS, DPPH, and FRAP antioxidant tests, dendrimers functionalized with N¹-alkylated Trp expressed higher ROS-scavenging capacity (ABTS and DPPH) than those with unsubstituted Trp. Conclusions: Peptide dendrimers functionalized with N¹-alkyl-tryptophan showed varying toxicity to NHA, while all were toxic to GBM cells. Based on their activity towards inhibition of GBM viability and relatively mild effect on NHA cells the most advantageous were derivatives 21 and 25 with the respective di-dodecyl and dodecyl residue located at the C-terminus. As expected, peptide dendrimers functionalized with N¹-alkyl-tryptophan expressed higher scavenging potency against ROS than dendrimers with unsubstituted tryptophan.

Comprehensive metabolomics study on the pathogenesis of anaplastic astrocytoma via UPLC-Q/TOF-MS

Anaplastic astrocytoma (AA) is a malignant carcinoma whose pathogenesis remains to be fully elucidated. System biology techniques have been widely used to clarify the mechanism of diseases from a systematic perspective. The present study aimed to explore the pathogenesis and novel potential biomarkers for the diagnosis of AA according to metabolic differences. Patients with AA (n = 12) and healthy controls (n = 15) were recruited. Serum was assayed with untargeted ultraperformance liquid chromatography-quadrupole/time-of-flight-mass spectrometry (UPLC-Q/TOF-MS) metabolomic techniques. The data were further evaluated using multivariate analysis and bioinformatic methods based on the KEGG database to determine the distinct metabolites and perturbed pathways. Principal component analysis and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) identified the significance of the distinct metabolic pattern between patients with AA and healthy controls (P < .001) in both ESI modes. Permutation testing confirmed the validity of the OPLS-DA model (permutation = 200, Q2 < 0.5). In total, 24 differentiated metabolites and 5 metabolic pathways, including sphingolipid, glycerophospholipid, caffeine, linoleic acid, and porphyrin metabolism, were identified based on the OPLS-DA model. 3-Methylxanthine, sphinganine, LysoPC(18:1), and lactosylceramide were recognized as potential biomarkers with excellent sensitivity and specificity (area under the curve > 98%). These findings indicate that the perturbed metabolic pattern related to immune regulation and cellular signal transduction is associated with the pathogenesis of AA. 3-Methylxanthine, sphinganine, LysoPC(18:1), and lactosylceramide could be used as biomarkers of AA in future clinical practice. This study provides a therapeutic basis for further studies on the mechanism and precise clinical diagnosis of AA.

Proteomics and metabolomics approach in adult and pediatric glioma diagnostics

The diagnosis of glioma is mainly based on imaging methods that do not distinguish between stage and subtype prior to histopathological analysis. Patients with gliomas are generally diagnosed in the symptomatic stage of the disease. Additionally, healing scar tissue may be mistakenly identified based on magnetic resonance imaging (MRI) as a false positive tumor recurrence in postoperative patients. Current knowledge of molecular alterations underlying gliomagenesis and identification of tumoral biomarkers allow for their use as discriminators of the state of the organism. Moreover, a multiomics approach provides the greatest spectrum and the ability to track physiological changes and can serve as a minimally invasive method for diagnosing asymptomatic gliomas, preceding surgery and allowing for the initiation of prophylactic treatment. It is important to create a vast biomarker library for adults and pediatric patients due to their metabolic differences. This review focuses on the most promising proteomic, metabolomic and lipidomic glioma biomarkers, their pathways, the interactions, and correlations that can be considered characteristic of tumor grade or specific subtype.

Specific Gut Microbiome and Serum Metabolome Changes in Lung Cancer Patients

Background

Lung cancer (LC) is one of the most aggressive, prevalent and fatal malignancies. Gut microbes and their associated metabolites are thought to cause and modulate LC development, albeit influenced by the host genetic make-up and environment. Herein, we identified and classified gut microbiota and serum metabolites associated with LC.

Methods

Stool samples were collected from 41 LC patients and 40 healthy volunteers. The gut microbiota was analyzed using 16S rRNA gene sequencing. Serum samples were collected from the same LC patients (n=30) and healthy volunteers (n=30) and serum metabolites were analyzed using liquid chromatography-mass spectrometry (LC-MS). Microbiome and metabolome data were analyzed separately and integrated for combined analysis using various bioinformatics methods.

Results

Serum metabolomics uncovered 870 metabolites regulated in 76 metabolic pathways in both groups. Microbial diversity analyses identified 15967 operational taxonomic units (OTUs) in groups. Of these, the abundance of 232 OTUs was significantly different between HC and LC groups. Also, serum levels of glycerophospholipids (LysoPE 18:3, LysoPC 14:0, LysoPC 18:3), Imidazopyrimidines (Hypoxanthine), AcylGlcADG 66:18; AcylGlcADG (22:6/22:6/22:6) and Acylcarnitine 11:0 were substantially different between HC and LC groups. Combined analysis correlated LC-associated microbes with metabolites, such as Erysipelotrichaceae_UCG_003, Clostridium and Synergistes with glycerophospholipids.

Conclusions

There is an intricate relationship between gut microbiome and levels of several metabolites such as glycerophospholipids and imidazopyrimidines. Microbial-associated metabolites are potential diagnostic biomarkers and therapeutic targets for LC.

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.

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.

Identification of Pre-Diagnostic Metabolic Patterns for Glioma Using Subset Analysis of Matched Repeated Time Points

Simple Summary

Reprogramming of cellular metabolism is a major hallmark of cancer cells, and play an important role in tumor initiation and progression. The aim of our study is to discover circulating early metabolic markers of brain tumors, as discovery and development of reliable predictive molecular markers are needed for precision oncology applications. We use a study design tailored to minimize confounding factors and a novel machine learning and visualization approach (SMART) to identify a panel of 15 interlinked metabolites related to glioma development. The presented SMART strategy facilitates early molecular marker discovery and can be used for many types of molecular data.

Abstract

Here, we present a strategy for early molecular marker pattern detection—Subset analysis of Matched Repeated Time points (SMART)—used in a mass-spectrometry-based metabolomics study of repeated blood samples from future glioma patients and their matched controls. The outcome from SMART is a predictive time span when disease-related changes are detectable, defined by time to diagnosis and time between longitudinal sampling, and visualization of molecular marker patterns related to future disease. For glioma, we detect significant changes in metabolite levels as early as eight years before diagnosis, with longitudinal follow up within seven years. Elevated blood plasma levels of myo-inositol, cysteine, N-acetylglucosamine, creatinine, glycine, proline, erythronic-, 4-hydroxyphenylacetic-, uric-, and aceturic acid were particularly evident in glioma cases. We use data simulation to ensure non-random events and a separate data set for biomarker validation. The latent biomarker, consisting of 15 interlinked and significantly altered metabolites, shows a strong correlation to oxidative metabolism, glutathione biosynthesis and monosaccharide metabolism, linked to known early events in tumor development. This study highlights the benefits of progression pattern analysis and provide a tool for the discovery of early markers of disease.

Metabolomics of Interstitial Fluid, Plasma and Urine in Patients with Arterial Hypertension: New Insights into the Underlying Mechanisms

There is growing evidence that lymphatic system plays a pivotal role in the pathogenesis of hypertension. Here, for the first time, the metabolome of interstitial fluid is analyzed in patients with arterial hypertension. Due to ethical issues to obtain human interstitial fluid samples, this study included only oncological patients after axillary lymph node dissection (ALND). These patients were matched into hypertensive (n = 29) and normotensive (n = 35) groups with similar oncological status. Simultaneous evaluation of interstitial fluid, plasma, and urine was obtained by combining high-resolution proton nuclear magnetic resonance (¹H NMR) spectroscopy with chemometric analysis. Orthogonal partial least squares discriminant analysis (OPLS-DA) provided a clear differentiation between the hypertension and normotensive group, with the discrimination visible in each biofluid. In interstitial fluid nine potential metabolomic biomarkers for hypertension could be identified (creatinine, proline, pyroglutamine, glycine, alanine, 1-methylhistidine, the lysyl group of albumin, threonine, lipids), seven distinct markers in plasma (creatinine, mannose, isobutyrate, glycine, alanine, lactate, acetate, ornithine), and seven respectively in urine (methylmalonate, citrulline, phenylacetylglycine, fumarate, citrate, 1-methylnicotinamide, trans-aconitate). Biomarkers in plasma and urine allowed for the identification of specific biochemical pathways involved in hypertension, as previously suggested. Analysis of the interstitial fluid metabolome provided additional biomarkers compared to plasma or urine. Those biomarkers reflected primarily alterations in the metabolism of lipids and amino acids, and indicated increased levels of oxidative stress/inflammation in patients with hypertension.

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.

Metabolic response patterns in brain microdialysis fluids and serum during interstitial cisplatin treatment of high-grade glioma

Background

High-grade gliomas are associated with poor prognosis. Tumour heterogeneity and invasiveness create challenges for effective treatment and use of systemically administrated drugs. Furthermore, lack of functional predictive response-assays based on drug efficacy complicates evaluation of early treatment responses.

Methods

We used microdialysis to deliver cisplatin into the tumour and to monitor levels of metabolic compounds present in the tumour and non-malignant brain tissue adjacent to tumour, before and during treatment. In parallel, we collected serum samples and used multivariate statistics to analyse the metabolic effects.

Results

We found distinct metabolic patterns in the extracellular fluids from tumour compared to non-malignant brain tissue, including high concentrations of a wide range of amino acids, amino acid derivatives and reduced levels of monosaccharides and purine nucleosides. We found that locoregional cisplatin delivery had a strong metabolic effect at the tumour site, resulting in substantial release of glutamic acid, phosphate, and spermidine and a reduction of cysteine levels. In addition, patients with long-time survival displayed different treatment response patterns in both tumour and serum. Longer survival was associated with low tumour levels of lactic acid, glyceric acid, ketoses, creatinine and cysteine. Patients with longer survival displayed lower serum levels of ketohexoses, fatty acid methyl esters, glycerol-3-phosphate and alpha-tocopherol, while elevated phosphate levels were seen in both tumour and serum during treatment.

Conclusion

We highlight distinct metabolic patterns associated with high-grade tumour metabolism, and responses to cytotoxic cisplatin treatment.

Role of monoamine-oxidase-A-gene variation in the development of glioblastoma in males: a case control study

Background

The Mono-amine oxidase-A (MAO-A) enzyme is involved in the degradation and regulation of catecholamines such as serotonin, dopamine, epinephrine and nor-epinephrine. Preclinical studies suggest that this enzyme may contribute to an environment favorable for growth of malignant glioma. The MAO-A gene is located on the X-chromosome and has at least one functional genetic polymorphism. The aim of the present study was to explore possible effects of MAO-A genotype on development of glioblastoma in males.

Methods

Genotypes for 437 glioma cases and 876 population-based controls from the Swedish Glioma International Case–Control study (GICC) were compared. We analyzed the germline DNA using the Illumina Oncoarray. We selected seven single nucleotide polymorphisms (SNPs) located in the MAO-A gene, and imputed genotypes based on data from the 1000 genomes project. We used 1579 male glioblastoma cases and 1875 controls comprising the whole GICC cohort for subsequent validation of findings.

Results

The rs144551722 SNP was a significant predictor of development of glioblastoma in males (p-value = 0.0056) but not in females even after correction for multiple testing. We conducted haplotype analysis to confirm an association between MAO-A gene and risk of glioblastoma (p-value = 0.016). We found similar results in the validation sample.

Conclusions

These results suggest the possibility of a role for the MAO-A enzyme and the MAO-A gene in the development of glioblastoma in males.

Liquid Biopsy in Glioblastoma: Opportunities, Applications and Challenges

Liquid biopsy represents a minimally invasive procedure that can provide similar information from body fluids to what is usually obtained from a tissue biopsy sample. Its implementation in the clinical setting might significantly renew the field of medical oncology, facilitating the introduction of the concepts of precision medicine and patient-tailored therapies. These advances may be useful in the diagnosis of brain tumors that currently require surgery for tissue collection, or to perform genetic tumor profiling for disease classification and guidance of therapy. In this review, we will summarize the most recent advances and putative applications of liquid biopsy in glioblastoma, the most common and malignant adult brain tumor. Moreover, we will discuss the remaining challenges and hurdles in terms of technology and biology for its clinical application.

NMR metabolomic signatures reveal predictive plasma metabolites associated with long-term risk of developing breast cancer

Background

Combination of metabolomics and epidemiological approaches opens new perspectives for ground-breaking discoveries. The aim of the present study was to investigate for the first time whether plasma untargeted metabolomic profiles, established from a simple blood draw from healthy women, could contribute to predict the risk of developing breast cancer within the following decade and to better understand the aetiology of this complex disease.

Methods

A prospective nested case-control study was set up in the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, including 206 breast cancer cases diagnosed during a 13-year follow-up and 396 matched controls. Untargeted nuclear magnetic resonance (NMR) metabolomic profiles were established from baseline plasma samples. Multivariable conditional logistic regression models were computed for each individual NMR variable and for combinations of variables derived by principal component analysis.

Results

Several metabolomic variables from 1D NMR spectroscopy were associated with breast cancer risk. Women characterized by higher fasting plasma levels of valine, lysine, arginine, glutamine, creatine, creatinine and glucose, and lower plasma levels of lipoproteins, lipids, glycoproteins, acetone, glycerol-derived compounds and unsaturated lipids had a higher risk of developing breast cancer. P-values ranged from 0.00007 [odds ratio (OR)T3vsT1=0.37 (0.23-0.61) for glycerol-derived compounds] to 0.04 [ORT3vsT1=1.61 (1.02-2.55) for glutamine].

Conclusion

This study highlighted associations between baseline NMR plasma metabolomic signatures and long-term breast cancer risk. These results provide interesting insights to better understand complex mechanisms involved in breast carcinogenesis and evoke plasma metabolic disorders favourable for carcinogenesis initiation. This study may contribute to develop screening strategies for the identification of at-risk women for breast cancer well before symptoms appear.

Metabolomic Profile Predicts Development of Microalbuminuria in Individuals with Type 1 Diabetes

Elevated urinary albumin excretion (microalbuminuria) is an early marker of diabetic nephropathy, but there is an unmet need for better biomarkers that capture the individuals at risk with higher accuracy and earlier than the current markers do. We performed an untargeted metabolomic study to assess baseline differences between individuals with type 1 diabetes who either developed microalbuminuria or remained normoalbuminuric. A total of 102 individuals progressed to microalbuminuria during a median follow-up of 3.2 years, whereas 98 sex-, age- and body mass index (BMI) matched non-progressors remained normoalbuminuric during a median follow-up of 7.1 years. Metabolomic screening identified 1,242 metabolites, out of which 111 differed significantly between progressors and non-progressors after adjustment for age of diabetes onset, baseline glycosylated hemoglobin A1c (HbA1c), and albumin excretion rate (AER). The metabolites that predicted development of microalbumiuria included several uremic toxins and carnitine metabolism related molecules. Iterative variable selection indicated erythritol, 3-phenylpropionate, and N-trimethyl-5-aminovalerate as the best set of variables to predict development of microalbuminuria. A metabolomic index based on these metabolites improved the prediction of incident microalbuminuria on top of the clinical variables age of diabetes onset, baseline HbA1c and AER (ROCAUC = 0.842 vs 0.797), highlighting their ability to predict early-phase diabetic nephropathy.

Pharmacometabolomics Informs Quantitative Radiomics for Glioblastoma Diagnostic Innovation

Applications of omics systems biology technologies have enormous promise for radiology and diagnostics in surgical fields. In this context, the emerging fields of radiomics (a systems scale approach to radiology using a host of technologies, including omics) and pharmacometabolomics (use of metabolomics for patient and disease stratification and guiding precision medicine) offer much synergy for diagnostic innovation in surgery, particularly in neurosurgery. This synthesis of omics fields and applications is timely because diagnostic accuracy in central nervous system tumors still challenges decision-making. Considering the vast heterogeneity in brain tumors, disease phenotypes, and interindividual variability in surgical and chemotherapy outcomes, we believe that diagnostic accuracy can be markedly improved by quantitative radiomics coupled to pharmacometabolomics and related health information technologies while optimizing economic costs of traditional diagnostics. In this expert review, we present an innovation analysis on a systems-level multi-omics approach toward diagnostic accuracy in central nervous system tumors. For this, we suggest that glioblastomas serve as a useful application paradigm. We performed a literature search on PubMed for articles published in English between 2006 and 2016. We used the search terms "radiomics," "glioblastoma," "biomarkers," "pharmacogenomics," "pharmacometabolomics," "pharmacometabonomics/pharmacometabolomics," "collaborative informatics," and "precision medicine." A list of the top 4 insights we derived from this literature analysis is presented in this study. For example, we found that (i) tumor grading needs to be better refined, (ii) diagnostic precision should be improved, (iii) standardization in radiomics is lacking, and (iv) quantitative radiomics needs to prove clinical implementation. We conclude with an interdisciplinary call to the metabolomics, pharmacy/pharmacology, radiology, and surgery communities that pharmacometabolomics coupled to information technologies (chemoinformatics tools, databases, collaborative systems) can inform quantitative radiomics, thus translating Big Data and information growth to knowledge growth, rational drug development and diagnostics innovation for glioblastomas, and possibly in other brain tumors.

Identification of metabolites in plasma for predicting survival in glioblastoma

Circulating metabolomics profiling holds prognostic potential. However, such efforts have not been extensively carried out in glioblastoma. In this study, two-step (training and testing) metabolomics profiling was conducted from the plasma samples of 159 glioblastoma patients. Metabolomics profiling was tested for correlation with 2-year overall and disease-free survivals. Arginine, methionine, and kynurenate levels were significantly associated with 2-year overall survival in both the training and testing sets. In the combined sets, elevated levels of arginine and methionine were associated with a 34% and 37% increased probability whereas kynurenate was associated with a 55% decreased probability of 2-year overall survival. These three metabolites were also significantly associated with 2-year disease-free survival. Risk scores were generated using the linear combination of levels of these significant metabolites. Glioblastoma patients with a high-risk score exhibited a 2.41-fold decreased probability of 2-year overall survival (hazard ratio (HR) = 2.41; 95% Confidence Interval (CI) = 1.20-4.93) and a 3.17-fold decreased probability of 2-year disease free survival (HR = 3.17, 95%CI = 1.42-7.54) relative to those with a low-risk score. In conclusion, we identified a unique plasma metabolite profile that is predictive of glioblastoma prognosis.

Global and targeted serum metabolic profiling of colorectal cancer progression

BACKGROUND

Patients with colorectal adenoma polyps (PLPs) are at higher risk for developing colorectal cancer (CRC). However, the development of improved and robust biomarkers to enable the screening, surveillance, and early detection of PLPs and CRC continues to be a challenge. The aim of this study was to identify biomarkers of progression to CRC through metabolomic profiling of human serum samples with a multistage approach.

METHODS

Metabolomic profiling was conducted with the Metabolon platform for 30 CRC patients, 30 PLP patients, and 30 control subjects, and this was followed by the targeted validation of the top metabolites in an additional set of 50 CRC patients, 50 PLP patients, and 50 controls with liquid chromatography-tandem mass spectrometry. Unconditional multivariate logistic regression models, adjusted for covariates, were used to evaluate associations with PLP and CRC risk.

RESULTS

For the discovery phase, 404 serum metabolites were detected, with 50 metabolites showing differential levels between CRC patients, PLP patients, and controls (P for trend < .05). After validation, the 3 top metabolites (xanthine, hypoxanthine, and d-mannose) were validated: lower levels of xanthine and hypoxanthine and higher levels of d-mannose were found in PLP and CRC cases versus controls. A further exploratory analysis of metabolic pathways revealed key roles for the urea cycle and caffeine metabolism associated with PLP and CRC risk. In addition, a joint effect of the top metabolites with smoking and a significant interaction with the body mass index were observed. An analysis of the ratio of hypoxanthine levels to xanthine levels indicated an association with CRC progression.

CONCLUSIONS

These results suggest the potential utility of circulating metabolites as novel biomarkers for the early detection of CRC. Cancer 2017;123:4066-74. © 2017 American Cancer Society.

Short overview on metabolomics approach to study pathophysiology of oxidative stress in cancer

Association of oxidative stress with carcinogenesis is well known, but not understood well, as is pathophysiology of oxidative stress generated during different types of anti-cancer treatments. Moreover, recent findings indicate that cancer associated lipid peroxidation might eventually help defending adjacent nonmalignant cells from cancer invasion. Therefore, untargeted metabolomics studies designed for advanced translational and clinical studies are needed to understand the existing paradoxes in oncology, including those related to controversial usage of antioxidants aiming to prevent or treat cancer. In this short review we have tried to put emphasis on the importance of pathophysiology of oxidative stress and lipid peroxidation in cancer development in relation to metabolic adaptation of particular types of cancer allowing us to conclude that adaptation to oxidative stress is one of the main driving forces of cancer pathophysiology. With the help of metabolomics many novel findings are being achieved thus encouraging further scientific breakthroughs. Combined with targeted qualitative and quantitative methods, especially immunochemistry, further research might reveal bio-signatures of individual patients and respective malignant diseases, leading to individualized treatment approach, according to the concepts of modern integrative medicine.

A prospective study of serum metabolites and glioma risk

Malignant glioma is one of the most lethal adult cancers, yet its etiology remains largely unknown. We conducted a prospective serum metabolomic analysis of glioma based on 64 cases and 64 matched controls selected from Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Median time from collection of baseline fasting serum to diagnosis was nine years (inter-decile range 3-20 years). LC/MS-MS identified 730 known metabolites, and conditional logistic regression models estimated odds ratios for one-standard deviation differences in log-metabolite signals. Forty-three metabolites were associated with glioma at P<0.05. 2-Oxoarginine, cysteine, alpha-ketoglutarate, chenodeoxycholate and argininate yielded the strongest metabolite signals and were inversely related to overall glioma risk (0.0065≤P<0.0083). Also, seven xanthine metabolites related to caffeine metabolism were higher in cases than in controls (0.017≤P<0.042). Findings were mostly similar in high-grade glioma cases, although prominent inversely associated metabolites included the secondary bile acids glycocholenate sulfate and 3β-hydroxy-5-cholenoic acid, xenobiotic methyl 4-hydroxybenzoate sulfate, sex steroid 5alpha-pregnan-3beta, 20beta-diol-monosulfate, and cofactor/vitamin oxalate (0.0091≤P<0.021). A serum metabolomic profile of glioma identified years in advance of clinical diagnoses is characterized by altered signals in arginine/proline, antioxidant, and coffee-related metabolites. The observed pattern provides new potential leads regarding the molecular basis relevant to etiologic or sub-clinical biomarkers for glioma.

Metabolomic signature of brain cancer

Despite advances in surgery and adjuvant therapy, brain tumors represent one of the leading causes of cancer-related mortality and morbidity in both adults and children. Gliomas constitute about 60% of all cerebral tumors, showing varying degrees of malignancy. They are difficult to treat due to dismal prognosis and limited therapeutics. Metabolomics is the untargeted and targeted analyses of endogenous and exogenous small molecules, which charact erizes the phenotype of an individual. This emerging "omics" science provides functional readouts of cellular activity that contribute greatly to the understanding of cancer biology including brain tumor biology. Metabolites are highly informative as a direct signature of biochemical activity; therefore, metabolite profiling has become a promising approach for clinical diagnostics and prognostics. The metabolic alterations are well-recognized as one of the key hallmarks in monitoring disease progression, therapy, and revealing new molecular targets for effective therapeutic intervention. Taking advantage of the latest high-throughput analytical technologies, that is, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), metabolomics is now a promising field for precision medicine and drug discovery. In the present report, we review the application of metabolomics and in vivo metabolic profiling in the context of adult gliomas and paediatric brain tumors. Analytical platforms such as high-resolution (HR) NMR, in vivo magnetic resonance spectroscopic imaging and high- and low-resolution MS are discussed. Moreover, the relevance of metabolic studies in the development of new therapeutic strategies for treatment of gliomas are reviewed.

Chemometrics Methods and Strategies in Metabolomics

Chemometrics has been a fundamental discipline for the development of metabolomics, while symbiotically growing with it. From design of experiments, through data processing, to data analysis, chemometrics tools are used to design, process, visualize, explore and analyse metabolomics data.In this chapter, the most commonly used chemometrics methods for data analysis and interpretation of metabolomics experiments will be presented, with focus on multivariate analysis. These are projection-based linear methods, like principal component analysis (PCA) and orthogonal projection to latent structures (OPLS), which facilitate interpretation of the causes behind the observed sample trends, correlation with outcomes or group discrimination analysis. Validation procedures for multivariate methods will be presented and discussed.Univariate analysis is briefly discussed in the context of correlation-based linear regression methods to find associations to outcomes or in analysis of variance-based and logistic regression methods for class discrimination. These methods rely on frequentist statistics, with the determination of p-values and corresponding multiple correction procedures.Several strategies of design-analysis of metabolomics experiments will be discussed, in order to guide the reader through different setups, adopted to better address some experimental issues and to better test the scientific hypotheses.