Metabolomic signature of brain cancer

The impact of sampling time point on the lipidome composition

Lipidomic profiling has been reported as an effective approach for characterizing and differentiating brain tumors. However, since lipids can undergo non-specific enzymatic and nonenzymatic reactions due to tissue disruption, it is critical to consider the preanalytical phase of the diagnostic process (e.g., optimizing the sampling time and sampling conditions). Thus, this study assesses the ways in which the time point of sampling impacts the lipidome composition of brain tumors. Two histologically distinct brain tumors-namely, meningiomas and gliomas-were sampled using solid-phase microextraction (SPME) fibers at two time points: on-site directly after removal, and after 12 months of storage at -30 °C. The samples were analyzed via HILIC chromatography coupled with HRMS, which enabled the detection of a wide range of features, including phospholipids and sphingolipids, as well as changes in the profiles of these compounds. The samples obtained from the stored tissues tended to have elevated levels of analytes with lower m/z values. In addition, the samples obtained from the fresh and stored tissues were easily distinguished based on their lipidome compositions, regardless of the histological tumor type. Notably, while storage did not affect the possibility of differentiating meningiomas and gliomas, the biological interpretation of the obtained results were prone to bias.

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.

Prognostic impact of sodium fluorescein-guided microsurgery on cognitive function, neuropeptide dynamics, and short-term outcomes in brain glioma patients

This study conducted a retrospective analysis on 107 brain glioma patients treated from January 2018 to February 2020 to assess the impact of sodium fluorescein-guided microsurgery on postoperative cognitive function and short-term outcomes. Patients were divided into two groups: a control group (n=50 patients) undergoing routine surgery and a research group (n=57 patients) receiving sodium fluorescein-guided microsurgery. The study compared postoperative total resection rates, changes in cognitive scores, and neuropeptide levels in cerebrospinal fluid between the groups. The findings revealed that the research group experienced shorter surgical time and hospitalization duration, reduced blood loss, and higher total resection rates compared to the control group. Furthermore, the research group demonstrated improvements in cognitive scores and an increase in neuropeptide levels after surgery. There was no significant difference in the comparison of the incidence of postoperative complications between the two groups. The WHO classification and preoperative performance scores were independent prognostic factors for the evaluation of 3-year survival, highlighting the clinical significance of sodium fluorescein-guided microsurgery in improving quality of life and cognitive functions of patients without compromising their long-term survival outcomes.

Deciphering Glioblastoma: Fundamental and Novel Insights into the Biology and Therapeutic Strategies of Gliomas

Gliomas constitute a diverse and complex array of tumors within the central nervous system (CNS), characterized by a wide range of prognostic outcomes and responses to therapeutic interventions. This literature review endeavors to conduct a thorough investigation of gliomas, with a particular emphasis on glioblastoma (GBM), beginning with their classification and epidemiological characteristics, evaluating their relative importance within the CNS tumor spectrum. We examine the immunological context of gliomas, unveiling the intricate immune environment and its ramifications for disease progression and therapeutic strategies. Moreover, we accentuate critical developments in understanding tumor behavior, focusing on recent research breakthroughs in treatment responses and the elucidation of cellular signaling pathways. Analyzing the most novel transcriptomic studies, we investigate the variations in gene expression patterns in glioma cells, assessing the prognostic and therapeutic implications of these genetic alterations. Furthermore, the role of epigenetic modifications in the pathogenesis of gliomas is underscored, suggesting that such changes are fundamental to tumor evolution and possible therapeutic advancements. In the end, this comparative oncological analysis situates GBM within the wider context of neoplasms, delineating both distinct and shared characteristics with other types of tumors.

Untargeted metabolomic profiling reveals molecular signatures associated with type 2 diabetes in Nigerians

BACKGROUND

Type 2 diabetes (T2D) has reached epidemic proportions globally, including in Africa. However, molecular studies to understand the pathophysiology of T2D remain scarce outside Europe and North America. The aims of this study are to use an untargeted metabolomics approach to identify: (a) metabolites that are differentially expressed between individuals with and without T2D and (b) a metabolic signature associated with T2D in a population of Sub-Saharan Africa (SSA).

METHODS

A total of 580 adult Nigerians from the Africa America Diabetes Mellitus (AADM) study were studied. The discovery study included 310 individuals (210 without T2D, 100 with T2D). Metabolites in plasma were assessed by reverse phase, ultra-performance liquid chromatography and mass spectrometry (RP)/UPLC-MS/MS methods on the Metabolon Platform. Welch's two-sample t-test was used to identify differentially expressed metabolites (DEMs), followed by the construction of a biomarker panel using a random forest (RF) algorithm. The biomarker panel was evaluated in a replication sample of 270 individuals (110 without T2D and 160 with T2D) from the same study.

RESULTS

Untargeted metabolomic analyses revealed 280 DEMs between individuals with and without T2D. The DEMs predominantly belonged to the lipid (51%, 142/280), amino acid (21%, 59/280), xenobiotics (13%, 35/280), carbohydrate (4%, 10/280) and nucleotide (4%, 10/280) super pathways. At the sub-pathway level, glycolysis, free fatty acid, bile metabolism, and branched chain amino acid catabolism were altered in T2D individuals. A 10-metabolite biomarker panel including glucose, gluconate, mannose, mannonate, 1,5-anhydroglucitol, fructose, fructosyl-lysine, 1-carboxylethylleucine, metformin, and methyl-glucopyranoside predicted T2D with an area under the curve (AUC) of 0.924 (95% CI: 0.845-0.966) and a predicted accuracy of 89.3%. The panel was validated with a similar AUC (0.935, 95% CI 0.906-0.958) in the replication cohort. The 10 metabolites in the biomarker panel correlated significantly with several T2D-related glycemic indices, including Hba1C, insulin resistance (HOMA-IR), and diabetes duration.

CONCLUSIONS

We demonstrate that metabolomic dysregulation associated with T2D in Nigerians affects multiple processes, including glycolysis, free fatty acid and bile metabolism, and branched chain amino acid catabolism. Our study replicated previous findings in other populations and identified a metabolic signature that could be used as a biomarker panel of T2D risk and glycemic control thus enhancing our knowledge of molecular pathophysiologic changes in T2D. The metabolomics dataset generated in this study represents an invaluable addition to publicly available multi-omics data on understudied African ancestry populations.

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.

Nuclear magnetic resonance-based metabolomic study of rat serum after anterior cruciate ligament injury

Anterior cruciate ligament (ACL) injury, a common sports injury, is associated with a high risk of subsequent osteoarthritis (OA), which can cause serious pain and disability. Understanding the detailed mechanism underlying the predisposition of knee with ACL injury to secondary OA at an early stage is key to preventing future degradation and progression to a clinically significant disease. A total of 56 male Sprague Dawley rats (age, 8 weeks; weight, 180-220 g) were randomly divided into three experimental groups: control, ACL transection (ACLT; where surgical procedure was performed with ACLT), and sham (where surgical procedure was performed without ACLT). The ACLT and sham groups were further divided into three subgroups based on when the rats were sacrificed: 4, 8, and 12 weeks after the surgical procedure. The control group and the aforementioned subgroups contained 8 rats each. We used nuclear magnetic resonance (NMR)-based metabolomic analysis to analyze rat serum samples for the metabolic characteristics and the underlying mechanisms. In total, 28 metabolites were identified in the NMR spectra of the rat sera. At 4 and 8 weeks postoperatively, the sham group demonstrated metabolic profiles different from those of the ACLT group. However, this difference was not observed 12 weeks postoperatively. In total, five metabolites (acetate, succinate, sn-glycero-3-phosphocholine, glucose, and phenylalanine) and five metabolic pathways (phenylalanine, tyrosine, and tryptophan biosynthesis; phenylalanine metabolism; pyruvate metabolism; starch and sucrose metabolism; and histidine metabolism) demonstrated significant differences between the ACLT and sham groups. ACL injury was noted to considerably affect biochemical homeostasis and metabolism; however, these metabolic changes persisted briefly. Moreover, glucose was a characteristic metabolite, and several energy-related metabolic pathways were significantly disturbed. Therefore, an ACL injury may lead to considerable impairments in energy metabolism. Abnormal glucose levels facilitate chondrocyte function impairment and thereby lead to OA progression. Furthermore, lactate may aid in identifying metabolic changes specific to knee trauma not related to an ACL injury. Overall, the metabolic changes in rat serum after an ACL injury were closely related to disturbances in energy metabolism and amino acid metabolism. The current results may aid in understanding the pathogenesis of posttraumatic osteoarthritis.

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.

Identification of urine biomarkers associated with early puberty in children: An untargeted metabolomics analysis

A trend toward earlier pubertal maturation in both sexes has been shown in many countries. Early puberty affects an increasing proportion of children for reasons that remain obscure. Novel candidate biomarkers are strongly needed. We sought to apply untargeted metabolomic profiling to identify triggering mechanisms and candidate biomarkers in children with early puberty. Participants aged 7 - 12 years old were recruited directly from two elementary schools of Bengbu, Anhui Province, China, from Feb 2021 to May 2021. Early puberty was determined by breast and testicular development at baseline (May 2021) and 6-month later. Ultra-high-performance liquid chromatography-based untargeted metabolomic profiling was performed on urine samples of children with early puberty and control subjects. Metabolomic profiling for early puberty in a sex dependent manner. For boys, we identified several perturbed pathways, including histidine metabolism, glycine, serine and threonine metabolism, and selenoamino acid metabolism, associated with early puberty. In contrast, there were differences in pyruvate metabolism, one carbon pool by folate, and D-glutamine and D-glutamate metabolism pathways in girls with early puberty compared with controls. In addition, 4-hydroxyhippuric acid and 5-methoxytryptophol were shown as potential independent diagnostic biomarker for early puberty in boys, 3-hydroxybenzoic acid and glutaminylproline were shown as early biomarker for early puberty in girls, achieving area under the ROC curve of 0.71 and 0.72 in discriminating early puberty boys, and 0.70 and 0.74 in discriminating early puberty girls from controls. Through metabolomic analysis, we have identified metabolic perturbations and potential biomarkers of early puberty.

Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach

Glioblastoma (GBM), the most common human brain tumor, has been notoriously resistant to treatment. As a result, the dismal overall survival of GBM patients has not changed over the past three decades. GBM has been stubbornly resistant to checkpoint inhibitor immunotherapies, which have been remarkably effective in the treatment of other tumors. It is clear that GBM resistance to therapy is multifactorial. Although therapeutic transport into brain tumors is inhibited by the blood brain barrier, there is evolving evidence that overcoming this barrier is not the predominant factor. GBMs generally have a low mutation burden, exist in an immunosuppressed environment and they are inherently resistant to immune stimulation, all of which contribute to treatment resistance. In this review, we evaluate the contribution of multi-omic approaches (genomic and metabolomic) along with analyzing immune cell populations and tumor biophysical characteristics to better understand and overcome GBM multifactorial resistance to treatment.

Cerebrospinal fluid metabolite alterations in patients with different etiologies, diagnoses, and prognoses of disorders of consciousness

INTRODUCTION

Medical management of disorders of consciousness (DoC) is a growing issue imposing a major burden on families and societies. Recovery rates vary widely among patients with DoC, and recovery predictions strongly influence decisions on medical care. However, the specific mechanisms underlying different etiologies, consciousness levels, and prognoses are still unclear.

METHODS

We analyzed the comprehensive cerebrospinal fluid (CSF) metabolome through liquid chromatography-mass spectrometry. Metabolomic analyses were used to identify the metabolic differences between patients with different etiologies, diagnoses, and prognoses.

RESULTS

We found that the CSF levels of multiple acylcarnitines were lower in patients with traumatic DoC, suggesting mitochondrial function preservation in the CNS, which might contribute to the better consciousness outcomes of these patients. Metabolites related to glutamate and GABA metabolism were altered and showed a good ability to distinguish the patients in the minimally conscious state and the vegetative state. Moreover, we identified 8 phospholipids as potential biomarkers to predict the recovery of consciousness.

CONCLUSIONS

Our findings shed light on the differences in physiological activities underlying DoC with different etiologies and identified some potential biomarkers used for DoC diagnosis and prognosis.

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

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

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Application of Machine Learning to Metabolomic Profile Characterization in Glioblastoma Patients Undergoing Concurrent Chemoradiation

We here characterize changes in metabolite patterns in glioblastoma patients undergoing surgery and concurrent chemoradiation using machine learning (ML) algorithms to characterize metabolic changes during different stages of the treatment protocol. We examined 105 plasma specimens (before surgery, 2 days after surgical resection, before starting concurrent chemoradiation, and immediately after chemoradiation) from 36 patients with isocitrate dehydrogenase (IDH) wildtype glioblastoma. Untargeted GC-TOF mass spectrometry-based metabolomics was used given its superiority in identifying and quantitating small metabolites; this yielded 157 structurally identified metabolites. Using Multinomial Logistic Regression (MLR) and GradientBoostingClassifier (GB Classifier), ML models classified specimens based on metabolic changes. The classification performance of these models was evaluated using performance metrics and area under the curve (AUC) scores. Comparing post-radiation to pre-radiation showed increased levels of 15 metabolites: glycine, serine, threonine, oxoproline, 6-deoxyglucose, gluconic acid, glycerol-alpha-phosphate, ethanolamine, propyleneglycol, triethanolamine, xylitol, succinic acid, arachidonic acid, linoleic acid, and fumaric acid. After chemoradiation, a significant decrease was detected in 3-aminopiperidine 2,6-dione. An MLR classification of the treatment phases was performed with 78% accuracy and 75% precision (AUC = 0.89). The alternative GB Classifier algorithm achieved 75% accuracy and 77% precision (AUC = 0.91). Finally, we investigated specific patterns for metabolite changes in highly correlated metabolites. We identified metabolites with characteristic changing patterns between pre-surgery and post-surgery and post-radiation samples. To the best of our knowledge, this is the first study to describe blood metabolic signatures using ML algorithms during different treatment phases in patients with glioblastoma. A larger study is needed to validate the results and the potential application of this algorithm for the characterization of treatment responses.

LncRNA LINC01018/miR-942-5p/KNG1 axis regulates the malignant development of glioma in vitro and in vivo

AIMS

Since the inhibitory effect of KNG1 on glioma has been proved, this study further explores the regulation of the lncRNA/miRNA axis on KNG1 in glioma.

METHODS

The miRNAs that target KNG1 and the lncRNA that targets miR-942-5p were predicted by bioinformatics analysis and verified by experiments. The correlations between miR-942-5p and the survival of patients and between KNG1 and miR-942-5p were analyzed. After transfection, cell migration, invasion, proliferation, and cell cycle were detected through wound healing, Transwell, colony formation, and flow cytometry assays. A mouse subcutaneous xenotransplanted tumor model was established. The expressions of miR-942-5p, KNG1, LINC01018, and related genes were evaluated by quantitative real-time reverse transcription polymerase chain reaction (RT-qPCR), Western blot, or immunohistochemistry.

RESULTS

MiR-942-5p targeted KNG1, and LINC01018 sponged miR-942-5p. The high survival rate of patients was related to low miR-942-5p level. MiR-942-5p was highly expressed, whereas KNG1 was lowly expressed in glioma. MiR-942-5p was negatively correlated with KNG1. Silent LINC01018 or KNG1 and miR-942-5p mimic enhanced the migration, invasion, and proliferation of glioma cells, and regulated the expressions of metastasis-related and proliferation-related genes. LINC01018 knockdown and miR-942-5p mimic promoted glioma tumor growth in mice. The levels of miR-942-5p and KNG1 were decreased by LINC01018 knockdown, and LINC01018 expression was suppressed by miR-942-5p mimic. MiR-942-5p inhibitor, KNG1, and LINC01018 had the opposite effect to miR-942-5p mimic.

CONCLUSION

LINC01018/miR-942-5p/KNG1 pathway regulates the development of glioma cells in vitro and in vivo.

Metabolomics application for the design of an optimal diet

Precision nutrition is an emerging branch of nutrition science that aims to use modern omics technologies (genomics, proteomics, and metabolomics) to assess an individual's response to specific foods or dietary patterns and thereby determine the most effective diet or lifestyle interventions to prevent or treat specific diseases. Metabolomics is vital to nearly every aspect of precision nutrition. It can be targeted or untargeted, and it has many applications. Indeed, it can be used to comprehensively characterize the thousands of chemicals in foods, identify food by-products in human biofluids or tissues, characterize nutrient deficiencies or excesses, monitor biochemical responses to dietary interventions, track long- or short-term dietary habits, and guide the development of nutritional therapies. Indeed, metabolomics can be coupled with genomics and proteomics to study and advance the field of precision nutrition. Integrating omics with epidemiological and clinical data will begin to define the beneficial effects of human food metabolites. In this review, we present the metabolome and its relationship to precision nutrition. Moreover, we describe the different techniques used in metabolomics and present how metabolomics has been applied to advance the field of precision nutrition by providing notable examples and cases.

Metabolomic and Lipidomic Profiling of Gliomas-A New Direction in Personalized Therapies

Simple Summary

Gliomas comprise an extremely diverse category of brain tumors that are difficult to diagnose and treat. As a result, scientists continue to search for new treatment solutions, with personalized medicine having emerged as a particularly promising therapeutic approach. Metabolomics and its sub-discipline, lipidomics, are two scientific fields well-suited to support this search. Metabolomics focuses on the physicochemical changes in the metabolome, which include all of the small endogenous and exogenous compounds in a biological system. As such, metabolic analysis can help identify important biochemical pathways which could be the targets for new therapeutic approaches. This review examines the new directions of personalized therapies for gliomas and how metabolomic and lipidomic analysis assists in developing these strategies and monitoring their effectiveness. The discussion of new strategies is preceded by a brief overview of the current “gold standard” treatment for gliomas and the obstacles that new treatment approaches must overcome.

Abstract

In addition to being the most common primary brain tumor, gliomas are also among the most difficult to diagnose and treat. At present, the “gold standard” in glioma treatment entails the surgical resection of the largest possible portion of the tumor, followed by temozolomide therapy and radiation. However, this approach does not always yield the desired results. Additionally, the ability to cross the blood-brain barrier remains a major challenge for new potential drugs. Thus, researchers continue to search for targeted therapies that can be individualized based on the specific characteristics of each case. Metabolic and lipidomic research may represent two of the best ways to achieve this goal, as they enable detailed insights into the changes in the profile of small molecules in a biological system/specimen. This article reviews the new approaches to glioma therapy based on the analysis of alterations to biochemical pathways, and it provides an overview of the clinical results that may support personalized therapies in the future.

Circulating metabolites associated with tumor hypoxia and early response to treatment in bevacizumab-refractory glioblastoma after combined bevacizumab and evofosfamide

Glioblastomas (GBM) are the most common and aggressive form of primary malignant brain tumor in the adult population, and, despite modern therapies, patients often develop recurrent disease, and the disease remains incurable with median survival below 2 years. Resistance to bevacizumab is driven by hypoxia in the tumor and evofosfamide is a hypoxia-activated prodrug, which we tested in a phase 2, dual center (University of Texas Health Science Center in San Antonio and Dana Farber Cancer Institute) clinical trial after bevacizumab failure. Tumor hypoxic volume was quantified by 18F-misonidazole PET. To identify circulating metabolic biomarkers of tumor hypoxia in patients, we used a high-resolution liquid chromatography-mass spectrometry-based approach to profile blood metabolites and their specific enantiomeric forms using untargeted approaches. Moreover, to evaluate early response to treatment, we characterized changes in circulating metabolite levels during treatment with combined bevacizumab and evofosfamide in recurrent GBM after bevacizumab failure. Gamma aminobutyric acid, and glutamic acid as well as its enantiomeric form D-glutamic acid all inversely correlated with tumor hypoxia. Intermediates of the serine synthesis pathway, which is known to be modulated by hypoxia, also correlated with tumor hypoxia (phosphoserine and serine). Moreover, following treatment, lactic acid was modulated by treatment, likely in response to a hypoxia mediated modulation of oxidative vs glycolytic metabolism. In summary, although our results require further validation in larger patients’ cohorts, we have identified candidate metabolic biomarkers that could evaluate the extent of tumor hypoxia and predict the benefit of combined bevacizumab and evofosfamide treatment in GBM following bevacizumab failure.

Identification of a Metabolic Reprogramming-Associated Risk Model Related to Prognosis, Immune Microenvironment, and Immunotherapy of Stomach Adenocarcinoma

Stomach adenocarcinoma (STAD) is one of the most common malignant digestive tumors. Metabolic reprogramming is an essential feature of tumorigenesis. The roles of metabolic reprogramming in STAD patients were investigated to explore the tumor immune microenvironment (TME) and potential therapeutic strategies. STAD samples' transcriptomic and clinical data were collected from The Cancer Genome Atlas (TCGA) set and the GSE84437 set. The signature based on the metabolism-related genes (MRGs) was built using the Cox regression model to predict prognosis in STAD. Notably, this MRG-based signature (MRGS) accurately predicted STAD patients' clinical survival in multiple datasets and could serve as an indicator independently. STAD patients with high scores on the MRGS were eligible for generating a type I/II interferon (IFN) response, according to a complete examination of the link between the MRGS and TME. Tumor Immune Dysfunction and Exclusion (TIDE) and immunophenoscore (IPS) analyses revealed that STAD patients with different MRGS scores had different reactions to immunotherapy. Consequently, assessing the pattern of these MRGs increases the understanding of TME features in STAD, hence directing the development of successful immunotherapy regimens.

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.

Sarcodia suae modulates the immunity and disease resistance of white shrimp Litopenaeus vannamei against Vibrio alginolyticus via the purine metabolism and phenylalanine metabolism

Red seaweeds have several biofunctional properties, including immunomodulatory, antitumor, antioxidant, and antibacterial activities. In this study, we examined the effects of diets containing Sarcodia suae on the immune response, immune-related gene expressions, and disease resistance against Vibrio alginolyticus in white shrimp Litopenaeus vannamei. In addition, ¹H NMR metabolomics was applied to analyze the metabolites extracted from shrimp fed with S. suae and their functions in regulating immunity. A diet containing only fish meal was used as the control diet (S₀), and three diets containing different concentrations of S. suae powder, 2.5% (S2.5), 5% (S5), and 7.5% (S7.5) were used as experimental diets. Shrimp were fed diets for 20 days. Compared to the control group (S0), results showed that (1) shrimp fed diets supplemented with 5-7.5% of S. suae powder significantly increased anti-V. alginolyticus activity; (2) phagocytic activity (PA) increased in all shrimp fed with S. suae, but total haemocyte count (THC) only increased in S7.5 group; and (3) the expression of glutathione peroxidase (GPx) in haemocyte were significantly higher in S7.5 groups. Results from the ¹H NMR analysis revealed that 19 heapatopancreatic metabolites were matched and identified among groups. Based on the KEGG enrichment analysis, the up-regulated metabolites in the shrimp fed S5 and S7.5 diets were primarily due to the metabolism of purine and phenylalanine and their respective pathways. Results from these trials reveal that diets containing S. suae can increase immune response, thereby increasing shrimp resistance to V. alginolyticus. The purine and phenylalanine metabolic pathways may be considered as the relevant pathways for optimizing immunomodulatory responses.

LC–MS-Based Urine Metabolomics Analysis for the Diagnosis and Monitoring of Medulloblastoma

Medulloblastoma (MB) is the most common type of brain cancer in pediatric patients. Body fluid biomarkers will be helpful for clinical diagnosis and treatment. In this study, liquid chromatography–mass spectrometry (LC–MS)-based metabolomics was used to identify specific urine metabolites of MB in a cohort, including 118 healthy controls, 111 MB patients, 31 patients with malignant brain cancer, 51 patients with benign brain disease, 29 MB patients 1 week postsurgery and 80 MB patients 1 month postsurgery. The results showed an apparent separation for MB vs. healthy controls, MB vs. benign brain diseases, and MB vs. other malignant brain tumors, with AUCs values of 0.947/0.906, 0.900/0.873, and 0.842/0.885, respectively, in the discovery/validation group. Among all differentially identified metabolites, 4 metabolites (tetrahydrocortisone, cortolone, urothion and 20-oxo-leukotriene E4) were specific to MB. The analysis of these 4 metabolites in pre- and postoperative MB urine samples showed that their levels returned to a healthy state after the operation (especially after one month), showing the potential specificity of these metabolites for MB. Finally, the combination of two metabolites, tetrahydrocortisone and cortolone, showed diagnostic accuracy for distinguishing MB from non-MB, with an AUC value of 0.851. Our data showed that urine metabolomics might be used for MB diagnosis and monitoring.

Glutamic Acid and Total Creatine as Predictive Markers for Epilepsy in Glioblastoma by Using Magnetic Resonance Spectroscopy Before Surgery

OBJECTIVE

Epilepsy in glioblastoma patients significantly reduces their quality of life; however, little is known about the association between predicting epilepsy and metabolites in tumors. In this study, we used 3.0-T magnetic resonance imaging and ¹H-magnetic resonance spectroscopy (MRS) to quantify metabolite concentrations in patients with varying epilepsy histories.

METHODS

Fifty-one patients with glioblastoma underwent pretreatment 3.0-T MRI/¹H-MRS scanning. Single-voxel (1.5 cm³) MRS, in an enhanced lesion, was acquired using a double-echo point-resolved spectroscopic sequence with chemical-shift selective water suppression. MRS data were quantified with linear combination model (LC-Model) software. We compared the MRS data between groups with and without epilepsy during the postoperative course (EP).

RESULTS

The ratios of glutamate (Glu) and glutamate + glutamine (Glx) to total creatine (Glu/tCr and Glx/tCr) in the tumor were associated with epilepsy history. The receiver operating characteristic curve analysis showed that a Glu/tCr value of 1.81 was 70% sensitive and 90% specific for the prediction of EP (area under curve: 0.82). In the analysis excluding patients with preoperative epilepsy, a Glu/tCr value of 1.81 was 75% sensitive and 88% specific for the prediction (area under curve: 0.87).

CONCLUSIONS

Intratumoral metabolite concentrations measured using pretreatment 3.0-T MRI/¹H-MRS changed characteristically in the group with EP. Our study suggests that the Glu/tCr ratio in tumors has adequate reliability in predicting EP. Pretreatment MRS is a minimally invasive and simple procedure that can provide useful information on glioblastoma patients.

Short TE downfield magnetic resonance spectroscopy in a mouse model of brain glioma

PURPOSE

Enhanced cell proliferation in tumors can be associated with altered metabolic profiles and dramatic microenvironmental changes. Downfield magnetic resonance spectroscopy (MRS) has received increasing attention due to its ability to report on labile resonances of molecules not easily detected in upfield ¹ H MRS. Image-selected-in-vivo-spectroscopy-relaxation enhanced MRS (iRE-MRS) was recently introduced for acquiring short echo-time (TE) spectra. Here, iRE-MRS was used to investigate in-vivo downfield spectra in glioma-bearing mice.

METHODS

Experiments were performed in vivo in an immunocompetent glioma mouse model at 9.4 T using a cryogenic coil. iRE-MRS spectra were acquired in N = 6 glioma-bearing mice (voxel size = 2.2³  mm³ ) and N = 6 control mice. Spectra were modeled by a sum of Lorentzian peaks simulating known downfield resonances, and differences between controls and tumors were quantified using relative peak areas.

RESULTS

Short TE tumor spectra exhibited large qualitative differences compared to control spectra. Most peaks appeared modulated, with strong attenuation of NAA (∼7.82, 7.86 ppm) and changes in relative peak areas between 6.75 and 8.49 ppm. Peak areas tended to be smaller for DF_6.83 , DF_7.60 , DF_8.18 and NAA; and larger for DF_7.95 and DF_8.24 . Differences were also detected in signals resonating above 8.5 ppm, assumed to arise from NAD+.

CONCLUSIONS

In-vivo downfield ¹ H iRE-MRS of mouse glioma revealed differences between controls and tumor bearing mice, including in metabolites which are not easily detectable in the more commonly investigated upfield spectrum. These findings motivate future downfield MRS investigations exploring pH and exchange contributions to these differences.

Untargeted metabolomics analysis of esophageal squamous cell cancer progression

90% of esophageal cancer are esophageal squamous cell carcinoma (ESCC) and ESCC has a very poor prognosis and high mortality. Nevertheless, the key metabolic pathways associated with ESCC progression haven’t been revealed yet. Metabolomics has become a new platform for biomarker discovery over recent years. We aim to elucidate dominantly metabolic pathway in all ESCC tumor/node/metastasis (TNM) stages and adjacent cancerous tissues. We collected 60 postoperative esophageal tissues and 15 normal tissues adjacent to the tumor, then performed Liquid Chromatography with tandem mass spectrometry (LC–MS/MS) analyses. The metabolites data was analyzed with metabolites differential and correlational expression heatmap according to stage I vs. con., stage I vs. stage II, stage II vs. stage III, and stage III vs. stage IV respectively. Metabolic pathways were acquired by Kyoto Encyclopedia of Genes and Genomes. (KEGG) pathway database. The metabolic pathway related genes were obtained via Gene Set Enrichment Analysis (GSEA). mRNA expression of ESCC metabolic pathway genes was detected by two public datasets: gene expression data series (GSE)23400 and The Cancer Genome Atlas (TCGA). Receiver operating characteristic curve (ROC) analysis is applied to metabolic pathway genes. 712 metabolites were identified in total. Glycerophospholipid metabolism was significantly distinct in ESCC progression. 16 genes of 77 genes of glycerophospholipid metabolism mRNA expression has differential significance between ESCC and normal controls. Phosphatidylserine synthase 1 (PTDSS1) and Lysophosphatidylcholine Acyltransferase1 (LPCAT1) had a good diagnostic value with Area under the ROC Curve (AUC) > 0.9 using ROC analysis. In this study, we identified glycerophospholipid metabolism was associated with the ESCC tumorigenesis and progression. Glycerophospholipid metabolism could be a potential therapeutic target of ESCC progression.

PSMC2 is overexpressed in glioma and promotes proliferation and anti-apoptosis of glioma cells

Background

This study aims to investigate the effect of PSMC2 expression on the clinical prognosis of glioma patients and its molecular mechanism.

Methods

TCGA multi-tumor screening and survival analysis were combined to explore the differential expression of PSMC2 in multi-tumor. PSMC2 expression in glioma and normal tissues was detected by Western blot and RT-qPCR. Kaplan-Meier survival curve was used to visualize the effect of PSMC2 expression on the overall survival rate and disease-free survival rate of patients with glioma. The highly expressed cell line U343MG was selected to construct a PSMC2 knockdown model by siRNA transfection, and the effect of PSMC2 knockdown on cell proliferation ability was evaluated by CCK-8 assay. Gene-set enrichment analysis of PSMC2 co-expression genes was carried out to predict the molecular mechanism of their regulation of tumor cell phenotypes, and the analysis results were verified by flow cytometry and Western blot.

Results

Through broad-spectrum screening of 31 kinds of tumors, we found that PSMC2 was upregulated in most tumors, but PSMC2 was most significantly overexpressed in gliomas and correlated with poor prognosis in glioma patients. The results of Western blot and qRT-PCR showed that PSMC2 was significantly overexpressed in glioma tissues. Further survival analysis revealed that the overall survival and disease-free survival of patients with low PSMC2 expression were significantly better than that of patients with high PSMC2 expression. The proliferation of U343MG cells was significantly inhibited after PSMC2 knockdown. Enrichment analysis of PSMC2 co-expression genes indicated that PSMC2 affected the apoptosis process. The expression of apoptosis-related proteins also significantly changed following PSMC2 knockdown.

Conclusions

PSMC2 promotes the proliferation of glioma cells and inhibits the apoptosis, which is expected to be a potential therapeutic target for glioma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02533-1.

Establishing Imaging Biomarkers of Host Immune System Efficacy during Glioblastoma Therapy Response: Challenges, Obstacles and Future Perspectives

This hypothesis proposal addresses three major questions: (1) Why do we need imaging biomarkers for assessing the efficacy of immune system participation in glioblastoma therapy response? (2) Why are they not available yet? and (3) How can we produce them? We summarize the literature data supporting the claim that the immune system is behind the efficacy of most successful glioblastoma therapies but, unfortunately, there are no current short-term imaging biomarkers of its activity. We also discuss how using an immunocompetent murine model of glioblastoma, allowing the cure of mice and the generation of immune memory, provides a suitable framework for glioblastoma therapy response biomarker studies. Both magnetic resonance imaging and magnetic resonance-based metabolomic data (i.e., magnetic resonance spectroscopic imaging) can provide non-invasive assessments of such a system. A predictor based in nosological images, generated from magnetic resonance spectroscopic imaging analyses and their oscillatory patterns, should be translational to clinics. We also review hurdles that may explain why such an oscillatory biomarker was not reported in previous imaging glioblastoma work. Single shot explorations that neglect short-term oscillatory behavior derived from immune system attack on tumors may mislead actual response extent detection. Finally, we consider improvements required to properly predict immune system-mediated early response (1–2 weeks) to therapy. The sensible use of improved biomarkers may enable translatable evidence-based therapeutic protocols, with the possibility of extending preclinical results to human patients.

Comprehensive Analyses of Glucose Metabolism in Glioma Reveal the Glioma-Promoting Effect of GALM

Glioma is the most common tumor with the worst prognosis in the central nervous system. Current studies showed that glucose metabolism could affect the malignant progression of tumors. However, the study on the dysregulation of glucose metabolism in glioma is still limited. Herein, we firstly screened 48 differentially expressed glucose metabolism-related genes (DE-GMGs) by comparing glioblastomas to low-grade gliomas. Then a glucose metabolism-related gene (GMG)-based model (PC, lactate dehydrogenase A (LDHA), glucuronidase beta (GUSB), galactosidase beta 1 (GLB1), galactose mutarotase (GALM), or fructose-bisphosphatase 1 (FBP1)) was constructed by a protein-protein interaction (PPI) network and Lasso regression. Thereinto, the high-risk group encountered a worse prognosis than the low-risk group, and the M2 macrophage was positively relevant to the risk score. Various classical tumor-related functions were enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Since protein GALM was rarely studied in glioma, we detected high expression of GALM by western blot and immunohistochemistry in glioma tissues. And experiments in vitro showed that GALM could promote the epithelial-to-mesenchymal transition (EMT) process of glioma cells and could be regulated by TNFAIP3 in glioma cells. Overall, our study revealed the critical role of glucose metabolism in the prognosis of patients with glioma. Furthermore, we demonstrated that GALM was significantly related to the malignancy of glioma and could promote glioma cells' EMT process.

Tracers progress for positron emission tomography imaging of glial-related disease

Glial cells play an essential part in the neuron system. They can not only serve as structural blocks in the human brain but also participate in many biological processes. Extensive studies have shown that astrocytes and microglia play an important role in neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, Huntington's disease, as well as glioma, epilepsy, ischemic stroke, and infections. Positron emission tomography is a functional imaging technique providing molecular-level information before anatomic changes are visible and has been widely used in many above-mentioned diseases. In this review, we focus on the positron emission tomography tracers used in pathologies related to glial cells, such as glioma, Alzheimer's disease, and neuroinflammation.

Metabolomic Profiling of Blood-Derived Microvesicles in Breast Cancer Patients

Malignant cells differ from benign ones in their metabolome and it is largely unknown whether this difference is reflected in the metabolic profile of their microvesicles (MV), which are secreted into the blood of cancer patients. Here, they are present together with MV from the various blood and endothelial cells. Harvesting MV from 78 breast cancer patients (BC) and 30 controls, we characterized the whole blood MV metabolome using targeted and untargeted mass spectrometry. Especially (lyso)-phosphatidylcholines and sphingomyelins were detected in a relevant abundance. Eight metabolites showed a significant discriminatory power between BC and controls. High concentrations of lysoPCaC26:0 and PCaaC38:5 were associated with shorter overall survival. Comparing BC subtype-specific metabolome profiles, 24 metabolites were differentially expressed between luminal A and luminal B. Pathway analysis revealed alterations in the glycerophospholipid metabolism for the whole cancer cohort and in the ether lipid metabolism for the molecular subtype luminal B. Although this mixture of blood-derived MV contains only a minor number of tumor MV, a combination of metabolites was identified that distinguished between BC and controls as well as between molecular subtypes, and was predictive for overall survival. This suggests that these metabolites represent promising biomarkers and, moreover, that they may be functionally relevant for tumor progression.

Targeted metabolomics analysis of serum amino acid profiles in patients with Moyamoya disease

Amino acids are one of the main metabolites in the body, and provide energy for the body and brain. The purpose of this study is to provide a profile of amino acid changes in the serum of patients with Moyamoya disease (MMD) and identify potential disease biomarkers. In this paper, we quantitatively determined the serum amino acid metabolic profiles of 43 MMD patients and 42 healthy controls (HCs). T test, multivariate statistical analysis, and receiver operating characteristic (ROC) curve analysis were used to identify candidate markers. Thirty-nine amino acids were quantified, and 12 amino acid levels differed significantly between the MMD patients and HCs. Moreover, based on ROC curve analysis, four amino acid (L-methionine, L-glutamic acid, β-alanine and o-phosphoserine) biomarkers showed high sensitivity and specificity (AUC > 0.90), and showed the best sensitivity and specificity in MetaboAnalyst 5.0 using binary logistic regression analysis. We have provided serum amino acid metabolic profiles of MMD patients, and identified four potential biomarkers which may both provide clinicians with an objective diagnostic method for early detection of MMD and further our understanding of MMD pathogenesis.

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.

Metabolic profiles of human brain parenchyma and glioma for rapid tissue diagnosis by targeted desorption electrospray ionization mass spectrometry

Desorption electrospray ionization mass spectrometry (DESI-MS) is well suited for intraoperative tissue analysis since it requires little sample preparation and offers rapid and sensitive molecular diagnostics. Currently, intraoperative assessment of the tumor cell percentage of glioma biopsies can be made by measuring a single metabolite, N-acetylaspartate (NAA). The inclusion of additional biomarkers will likely improve the accuracy when distinguishing brain parenchyma from glioma by DESI-MS. To explore this possibility, mass spectra were recorded for extracts from 32 unmodified human brain samples with known pathology. Statistical analysis of data obtained from full-scan and multiple reaction monitoring (MRM) profiles identified discriminatory metabolites, namely gamma-aminobutyric acid (GABA), creatine, glutamic acid, carnitine, and hexane-1,2,3,4,5,6-hexol (abbreviated as hexol), as well as the established biomarker NAA. Brain parenchyma was readily differentiated from glioma based on these metabolites as measured both in full-scan mass spectra and by the intensities of their characteristic MRM transitions. New DESI-MS methods (5 min acquisition using full scans and MS/MS), developed to measure ion abundance ratios among these metabolites, were tested using smears of 29 brain samples. Ion abundance ratios based on signals for GABA, creatine, carnitine, and hexol all had sensitivities > 90%, specificities > 80%, and accuracies > 85%. Prospectively, the implementation of diagnostic ion abundance ratios should strengthen the discriminatory power of individual biomarkers and enhance method robustness against signal fluctuations, resulting in an improved DESI-MS method of glioma diagnosis.

Bioengineered Models to Study Microenvironmental Regulation of Glioblastoma Metabolism

Despite extensive research and aggressive therapies, glioblastoma (GBM) remains a central nervous system malignancy with poor prognosis. The varied histopathology of GBM suggests a landscape of differing microenvironments and clonal expansions, which may influence metabolism, driving tumor progression. Indeed, GBM metabolic plasticity in response to differing nutrient supply within these microenvironments has emerged as a key driver of aggressiveness. Additionally, emergent biophysical and biochemical interactions in the tumor microenvironment (TME) are offering new perspectives on GBM metabolism. Perivascular and hypoxic niches exert crucial roles in tumor maintenance and progression, facilitating metabolic relationships between stromal and tumor cells. Alterations in extracellular matrix and its biophysical characteristics, such as rigidity and topography, regulate GBM metabolism through mechanotransductive mechanisms. This review highlights insights gained from deployment of bioengineering models, including engineered cell culture and mathematical models, to study the microenvironmental regulation of GBM metabolism. Bioengineered approaches building upon histopathology measurements may uncover potential therapeutic strategies that target both TME-dependent mechanotransductive and biomolecular drivers of metabolism to tackle this challenging disease. Longer term, a concerted effort integrating in vitro and in silico models predictive of patient therapy response may offer a powerful advance toward tailoring of treatment to patient-specific GBM characteristics.

ME2 Promotes Proneural-Mesenchymal Transition and Lipogenesis in Glioblastoma

Malic enzyme 2 (ME2) catalyzes the formation of pyruvate from malic acid and is abnormally expressed in some tumors. However, the exact effects of ME2 on proneural–mesenchymal transition (PMT) and lipogenesis in glioblastoma multiforme (GBM) remain unexplored. Here, we found that ME2 expression was significantly higher in GBM than in normal brain tissues and negatively correlated with overall survival of patients with GBM. Furthermore, we demonstrated that ME2 was positively correlated with mesenchymal features in GBM and promoted proliferation, migration, and invasion of glioma cells. Moreover, ME2 upregulated the expression of mesenchymal markers (N-cadherin, vimentin, YKL40, and MET), whereas it inhibited the expression of proneural maker OLIG2, indicating that ME2 might promote PMT in GBM. We also found that ME2 inhibited the production of mitochondrial reactive oxygen species and AMPK phosphorylation, resulting in SREBP-1 maturation and nuclear localization and enhancing the ACSS2 lipogenesis pathway. Taken together, these results suggest that ME2 promotes PMT and is linked with reprogramming of lipogenesis via AMPK–SREBP-1–ACSS2 signaling in GBM. Therefore, ME2 has potential as a new classification marker in GBM and could provide a new approach to glioma treatment.

Evaluation of the automated micro-solid phase extraction clean-up system for the analysis of pesticide residues in cereals by gas chromatography-Orbitrap mass spectrometry

Food analysis is a tremendously broad field that is constantly evolving. New methods have emerged to increase productivity, such as modern miniaturized and robotic analytical techniques. In this paper, a micro-solid-phase extraction system (µ-SPE) for clean-up was combined with a robotic autosampler to yield ready-to-analyze extracts. The system was evaluated for its applicability in routine laboratories. The new, automated, high-throughput µ-SPE clean-up method was applied to acetonitrile extracts and was developed for the analysis of pesticide residues in cereals by gas chromatography-Orbitrap mass spectrometry (GC-Orbitrap-MS). The µ-SPE clean-up efficiency was demonstrated in the removal of matrix-interfering components and in the recovery of pesticides. The sorbent bed mixture consisted of magnesium sulfate, primary-secondary amine, C₁₈, and CarbonX, and effectively retained matrix components without loss of target analytes. Analysis of five types of cereals (barley, oat, rice, rye, and wheat) by GC-Orbitrap-MS showed that the method removed more than 70% of matrix components. The clean-up method was validated for 170 pesticides in rye, 159 pesticides in wheat, 142 pesticides in barley, 130 pesticides in oat, and 127 pesticides in rice. Spike recovery values were 70-120% for all pesticides and the repeatability, calculated as the relative standard deviation, was less than 20%. The limits of quantitation achieved were 0.005 mg kg^-1 for almost all analytes, ensuring compliance with the maximum residue limits.

Metabolic Remodeling in Glioma Immune Microenvironment: Intercellular Interactions Distinct From Peripheral Tumors

During metabolic reprogramming, glioma cells and their initiating cells efficiently utilized carbohydrates, lipids and amino acids in the hypoxic lesions, which not only ensured sufficient energy for rapid growth and improved the migration to normal brain tissues, but also altered the role of immune cells in tumor microenvironment. Glioma cells secreted interferential metabolites or depriving nutrients to injure the tumor recognition, phagocytosis and lysis of glioma-associated microglia/macrophages (GAMs), cytotoxic T lymphocytes, natural killer cells and dendritic cells, promoted the expansion and infiltration of immunosuppressive regulatory T cells and myeloid-derived suppressor cells, and conferred immune silencing phenotypes on GAMs and dendritic cells. The overexpressed metabolic enzymes also increased the secretion of chemokines to attract neutrophils, regulatory T cells, GAMs, and dendritic cells, while weakening the recruitment of cytotoxic T lymphocytes and natural killer cells, which activated anti-inflammatory and tolerant mechanisms and hindered anti-tumor responses. Therefore, brain-targeted metabolic therapy may improve glioma immunity. This review will clarify the metabolic properties of glioma cells and their interactions with tumor microenvironment immunity, and discuss the application strategies of metabolic therapy in glioma immune silence and escape.

Metabolomics Monitoring of Treatment Response to Brain Tumor Immunotherapy

Immunotherapy has revolutionized care for many solid tissue malignancies, and is being investigated for efficacy in the treatment of malignant brain tumors. Identifying a non-invasive monitoring technique such as metabolomics monitoring to predict patient response to immunotherapy has the potential to simplify treatment decision-making and to ensure therapy is tailored based on early patient response. Metabolomic analysis of peripheral immune response is feasible due to large metabolic shifts that immune cells undergo when activated. The utility of this approach is under investigation. In this review, we discuss the metabolic changes induced during activation of an immune response, and the role of metabolic profiling to monitor immune responses in the context of immunotherapy for malignant brain tumors. This review provides original insights into how metabolomics monitoring could have an important impact in the field of tumor immunotherapy if achievable.

Predicting cancer malignancy and proliferation in glioma patients: intra-subject inter-metabolite correlation analyses using MRI and MRSI contrast scans

Background

The non-invasive characterization of glioma metabolites would greatly assist the management of glioma patients in the clinical setting. This study investigated the applicability of intra-subject inter-metabolite correlation analyses for differentiating glioma malignancy and proliferation.

Methods

A total of 17 negative controls (NCs), 39 low-grade gliomas (LGGs) patients, and 25 high-grade gliomas (HGGs) subjects were included in this retrospective study. Amide proton transfer (APT) and magnetization transfer contrast (MTC) imaging contrasts, as well as total choline/total creatine (tCho/tCr) and total N-acetylaspartate/total creatine (tNAA/tCr) ratios quantified from magnetic resonance spectroscopic imaging (MRSI) were co-registered voxel-wise and used to produce three intra-subject inter-metabolite correlation coefficients (IMCCs), namely, R_{APT vs . MTC}, R_{APT vs . tCho/tCr}, and R_{MTC vs . tNAA/tCr}. The correlation between the IMCCs and tumor grade and Ki-67 labeling index (LI) for tumor proliferation were explored. The differences in the IMCCs between the three groups were compared with one-way analysis of variance (ANOVA). Finally, regression analysis was used to build a combined model with multiple IMCCs to improve the diagnostic performance for tumor grades based on receiver operator characteristic curves.

Results

Compared with the NCs, gliomas showed stronger inter-metabolic correlations. R_{APT vs . MTC} was significantly different among the three groups (NC vs. LGGs vs. HGGs: -0.18±0.38 vs. -0.40±0.34 vs. -0.70±0.29, P<0.0001). No significant differences were detected in R_{MTC vs . tNAA/tCr} among the three groups. R_{APT vs . MTC} and R_{APT vs . tCho/tCr} correlated significantly with tumor grade (R=-0.41, P=0.001 and R=0.448, P<0.001, respectively). However, only R_{APT vs . MTC} was mildly correlated with Ki-67 (R=-0.33, P=0.02). R_{APT vs . MTC} and R_{APT vs . tCho/tCr} achieved areas under the curve (AUCs) of 0.754 and 0.71, respectively, for differentiating NCs from gliomas; and 0.77 and 0.78, respectively, for differentiating LGGs from HGGs. The combined multi-IMCCs model improved the correlation with the Ki-67 LI (R=0.46, P=0.0008) and the tumor-grade stratification with AUC increased to 0.85 (sensitivity: 80.0%, specificity: 79.5%).

Conclusions

This study demonstrated that glioma patients showed stronger inter-metabolite correlations than control subjects, and the IMCCs were significantly correlated with glioma grade and proliferation. The multi-IMCCs combined model further improved the performance of clinical diagnosis.

Immune System-Related Changes in Preclinical GL261 Glioblastoma under TMZ Treatment: Explaining MRSI-Based Nosological Imaging Findings with RT-PCR Analyses

Glioblastomas (GB) are brain tumours with poor prognosis even after aggressive therapy. Previous work suggests that magnetic resonance spectroscopic imaging (MRSI) could act as a biomarker of efficient immune system attack onto GB, presenting oscillatory changes. Glioma-associated microglia/macrophages (GAMs) constitute the most abundant non-tumour cell type within the GB and can be polarised into anti-tumour (M1) or pro-tumour (M2) phenotypes. One of the mechanisms to mediate immunosuppression in brain tumours is the interaction between programmed cell death-1 ligand 1 (PD-L1) and programmed cell death-1 receptor (PD-1). We evaluated the subpopulations of GAMs in responding and control GB tumours to correlate PD-L1 expression to GAM polarisation in order to explain/validate MRSI-detected findings. Mice were evaluated by MRI/MRSI to assess the extent of response to treatment and with qPCR for GAMs M1 and M2 polarisation analyses. M1/M2 ratios and PD-L1 expression were higher in treated compared to control tumours. Furthermore, PD-L1 expression was positively correlated with the M1/M2 ratio. The oscillatory change in the GAMs prevailing population could be one of the key causes for the differential MRSI-detected pattern, allowing this to act as immune system activity biomarker in future work.

Glioblastoma Metabolomics-In Vitro Studies

In 2016, the WHO introduced new guidelines for the diagnosis of brain gliomas based on new genomic markers. The addition of these new markers to the pre-existing diagnostic methods provided a new level of precision for the diagnosis of glioma and the prediction of treatment effectiveness. Yet, despite this new classification tool, glioblastoma (GBM), a grade IV glioma, continues to have one of the highest mortality rates among central nervous system tumors. Metabolomics is a particularly promising tool for the analysis of GBM tumors and potential methods of treating them, as it is the only "omics" approach that is capable of providing a metabolic signature of a tumor's phenotype. With careful experimental design, cell cultures can be a useful matrix in GBM metabolomics, as they ensure stable conditions and, under proper conditions, are capable of capturing different tumor phenotypes. This paper reviews in vitro metabolomic profiling studies of high-grade gliomas, with a particular focus on sample-preparation techniques, crucial metabolites identified, cell culture conditions, in vitro-in vivo extrapolation, and pharmacometabolomics. Ultimately, this review aims to elucidate potential future directions for in vitro GBM metabolomics.

Sequential Orbitrap Secondary Ion Mass Spectrometry and Liquid Extraction Surface Analysis-Tandem Mass Spectrometry-Based Metabolomics for Prediction of Brain Tumor Relapse from Sample-Limited Primary Tissue Archives

We present here a novel surface mass spectrometry strategy to perform untargeted metabolite profiling of formalin-fixed paraffin-embedded pediatric ependymoma archives. Sequential Orbitrap secondary ion mass spectrometry (3D OrbiSIMS) and liquid extraction surface analysis-tandem mass spectrometry (LESA-MS/MS) permitted the detection of 887 metabolites (163 chemical classes) from pediatric ependymoma tumor tissue microarrays (diameter: <1 mm; thickness: 4 μm). From these 163 classes, 60 classes were detected with both techniques, whilst LESA-MS/MS and 3D OrbiSIMS individually allowed the detection of another 83 and 20 unique metabolite classes, respectively. Through data fusion and multivariate analysis, we were able to identify key metabolites and corresponding pathways predictive of tumor relapse, which were retrospectively confirmed by gene expression analysis with publicly available data. Altogether, this sequential mass spectrometry strategy has shown to be a versatile tool to perform high-throughput metabolite profiling on sample-limited tissue archives.

Imaging of Neurotransmitters and Small Molecules in Brain Tissues Using Laser Desorption/Ionization Mass Spectrometry Assisted with Zinc Oxide Nanoparticles

Inorganic nanostructured materials such as silicon, carbon, metals, and metal oxides have been explored as matrices of low-background signals to assist the laser desorption/ionization (LDI) mass spectrometric (MS) analysis of small molecules, but their applications for imaging of small molecules in biological tissues remain limited in the literature. Titanium dioxide is one of the known nanoparticles (NP) that can effectively assist LDI MS imaging of low molecular weight molecules (LMWM). TiO₂ NP is commercially available as dispersions, which can be applied using a chemical solution sprayer. However, aggregation of NP can occur in the dispersions, and the aggregated NP can slowly clog the sprayer nozzle. In this work, the use of zinc oxide (ZnO) NP for LDI MS imaging is investigated as a superior alternative due to its dissolution in acidic pH. ZnO NP was found to deliver similar or better results in the imaging of LMWM in comparison to TiO₂ NP. The regular acid washes were effective in minimizing clogging and maintaining high reproducibility. High-quality images of mouse sagittal and rat coronal tissue sections were obtained. Ions were detected predominately as Na⁺ or K⁺ adducts in the positive ion mode. The number of LMWM detected with ZnO NP was similar to that obtained with TiO₂ NP, and only a small degree of specificity was observed.

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.

Integrative Analysis of Metabolomic and Transcriptomic Data Reveals Metabolic Alterations in Glioma Patients

Glioma is a malignant brain tumor. There is growing evidence that its progression involves altered metabolism. This study's objective was to understand how those metabolic perturbations were manifested in plasma and urine. Metabolic signatures in blood and urine were characterized by liquid chromatography-tandem mass spectrometry. The results were linked to gene expression using data from the Gene Expression Omnibus database. Genes and pathways associated with the disease were thus identified. Forty metabolites were identified, which were differentially expressed in the plasma of glioma patients, and 61 were identified in their urine. Twenty-two metabolites and five disturbed pathways were found both in plasma and urine. Twelve metabolites in plasma and three in urine exhibited good diagnostic potential for glioma. Transcriptomic analyses revealed specific changes in the expression of 1437 genes associated with glioma. Seventeen differentially expressed genes were found to be correlated with four of the metabolites. Enrichment analysis indicated that dysregulation of glutamatergic synapse pathway might affect the pathology of glioma. Integration of metabolomics with transcriptomics can provide both a broad picture of novel cancer signatures and preliminary information about the molecular perturbations underlying glioma. These results may suggest promising targets for developing effective therapies.

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.

Characterization of the fatty acid metabolism in colorectal cancer to guide clinical therapy

Colorectal cancer (CRC) is one of the most common malignant tumors, with the second-highest mortality of all 36 cancers worldwide. The roles of fatty acid metabolism in CRC were investigated to explore potential therapeutic strategies. The data files were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to construct a prognostic risk score model with fatty acid metabolism-related genes for predicting prognosis in CRC. Patients with a high-risk score had a poorer prognosis in TCGA cohort than those with a low-risk score and were confirmed in the GEO cohort. Further analysis using the "pRRophetic" R package revealed that low-risk patients were more sensitive to 5-fluorouracil. A comprehensive evaluation of the association between prognostic risk score model and tumor microenvironment (TME) characteristics showed that high-risk patients were suitable for activating a type I/II interferon (IFN) response and inflammation-promoting function. Tumor Immune Dysfunction and Exclusion (TIDE) and SubMap algorithm results also demonstrated that high-risk patients are more suitable for anti-CTLA4 immunotherapy. Therefore, the evaluation of the fatty acid metabolism pattern promotes our comprehension of TME infiltration characteristics, thus guiding effective immunotherapy regimens.

ADO/hypotaurine: a novel metabolic pathway contributing to glioblastoma development

Significant advance has been made towards understanding glioblastoma metabolism through global metabolomic profiling. However, hitherto little is known about the role by which altered metabolism plays in driving the aggressive glioma phenotype. We have previously identified hypotaurine as one of the top-ranked metabolites for differentiating low- and high-grade tumors, and that there is also a strong association between the levels of intratumoral hypotaurine and expression of its biosynthetic enzyme, cysteamine (2-aminoethanethiol) dioxygenase (ADO). Using transcription profiling, we further uncovered that the ADO/hypotaurine axis targets CCL20 secretion through activating the NF-κB pathway to drive the self-renewal and maintenance of glioma 'cancer stem cells' or glioma cancer stem-like cells. Conversely, abrogating the ADO/hypotaurine axis using CRISPR/Cas9-mediated gene editing limited glioblastoma cell proliferation and self-renewal in vitro and tumor growth in vivo in an orthotopical mouse model, indicating that this metabolic pathway is a potential key therapeutic target. Collectively, our results unveil a targetable metabolic pathway, which contributes to the growth and progression of aggressive high-grade gliomas, as well as a novel predictive marker for glioblastoma diagnosis and therapy.