Meta-analysis of clinical metabolic profiling studies in cancer: challenges and opportunities

The emerging role of lactate in tumor microenvironment and its clinical relevance

In recent years, the significant impact of lactate in the tumor microenvironment has been greatly documented. Acting not only as an energy substance in tumor metabolism, lactate is also an imperative signaling molecule. It plays key roles in metabolic remodeling, protein lactylation, immunosuppression, drug resistance, epigenetics and tumor metastasis, which has a tight relation with cancer patients' poor prognosis. This review illustrates the roles lactate plays in different aspects of tumor progression and drug resistance. From the comprehensive effects that lactate has on tumor metabolism and tumor immunity, the therapeutic targets related to it are expected to bring new hope for cancer therapy.

UHPLC-HRMS-based Multiomics to Explore the Potential Mechanisms and Biomarkers for Colorectal Cancer

BACKGROUND

Understanding the metabolic changes in colorectal cancer (CRC) and exploring potential diagnostic biomarkers is crucial for elucidating its pathogenesis and reducing mortality. Cancer cells are typically derived from cancer tissues and can be easily obtained and cultured. Systematic studies on CRC cells at different stages are still lacking. Additionally, there is a need to validate our previous findings from human serum.

METHODS

Ultrahigh-performance liquid chromatography tandem high-resolution mass spectrometry (UHPLC-HRMS)-based metabolomics and lipidomics were employed to comprehensively measure metabolites and lipids in CRC cells at four different stages and serum samples from normal control (NR) and CRC subjects. Univariate and multivariate statistical analyses were applied to select the differential metabolites and lipids between groups. Biomarkers with good diagnostic efficacy for CRC that existed in both cells and serum were screened by the receiver operating characteristic curve (ROC) analysis. Furthermore, potential biomarkers were validated using metabolite standards.

RESULTS

Metabolite and lipid profiles differed significantly among CRC cells at stages A, B, C, and D. Dysregulation of glycerophospholipid (GPL), fatty acid (FA), and amino acid (AA) metabolism played a crucial role in the CRC progression, particularly GPL metabolism dominated by phosphatidylcholine (PC). A total of 46 differential metabolites and 29 differential lipids common to the four stages of CRC cells were discovered. Eight metabolites showed the same trends in CRC cells and serum from CRC patients compared to the control groups. Among them, palmitoylcarnitine and sphingosine could serve as potential biomarkers with the values of area under the curve (AUC) more than 0.80 in the serum and cells. Their panel exhibited excellent performance in discriminating CRC cells at different stages from normal cells (AUC = 1.00).

CONCLUSIONS

To our knowledge, this is the first research to attempt to validate the results of metabolism studies of serum from CRC patients using cell models. The metabolic disorders of PC, FA, and AA were closely related to the tumorigenesis of CRC, with PC being the more critical factor. The panel composed of palmitoylcarnitine and sphingosine may act as a potential biomarker for the diagnosis of CRC, aiding in its prevention.

Potential of Targeting TDO2 as the Lung Adenocarcinoma Treatment

Tryptophan catabolism plays an important role in the metabolic reconnection in cancer cells to support special demands of tumor initiation and progression. The catabolic product of the tryptophan pathway, kynurenine, has the capability of suppressing the immune reactions of tumor cells. In this study, we conducted internal and external cohort studies to reveal the importance of tryptophan 2,3-dioxygenase (TDO) for lung adenocarcinoma (LUAD). Our study further demonstrated that the TDO2 expression was associated with the proliferation, survival, and invasion of LUAD cells, and targeting TDO2 for LUAD tumors could be a potential therapeutic strategy.

Alterations of the gut microbiota in patients with schizophrenia

Introduction

Schizophrenia is a complex psychiatric disorder, of which molecular pathogenesis remains largely unknown. Accumulating evidence suggest that gut microbiota may affect brain function via the complex gut-brain axis, which may be a potential contributor to schizophrenia. However, the alteration of gut microbiota showed high heterogeneity across different studies. Therefore, this study aims to identify the consistently altered gut microbial taxa associated with schizophrenia.

Methods

We conducted a systematic search and synthesis of the up-to-date human gut microbiome studies on schizophrenia, and performed vote counting analyses to identify consistently changed microbiota. Further, we investigated the effects of potential confounders on the alteration of gut microbiota.

Results

We obtained 30 available clinical studies, and found that there was no strong evidence to support significant differences in α-diversity and β-diversity between schizophrenic patients and healthy controls. Among 428 differential gut microbial taxa collected from original studies, we found that 8 gut microbial taxa were consistently up-regulated in schizophrenic patients, including Proteobacteria, Gammaproteobacteria, Lactobacillaceae, Enterobacteriaceae, Lactobacillus, Succinivibrio, Prevotella and Acidaminococcus. While 5 taxa were consistently down-regulated in schizophrenia, including Fusicatenibacter, Faecalibacterium, Roseburia, Coprococcus and Anaerostipes.

Discussion

These findings suggested that gut microbial changes in patients with schizophrenia were characterized by the depletion of anti-inflammatory butyrate-producing genera, and the enrichment of certain opportunistic bacteria genera and probiotics. This study contributes to further understanding the role of gut microbiota in schizophrenia, and developing microbiota-based diagnosis and therapy for schizophrenia.

Potential Metabolite Biomarkers of Multiple Sclerosis from Multiple Biofluids

Multiple sclerosis (MS) is a chronic and progressive neurological disorder without a cure, but early intervention can slow disease progression and improve the quality of life for MS patients. Obtaining an accurate diagnosis for MS is an arduous and error-prone task that requires a combination of a detailed medical history, a comprehensive neurological exam, clinical tests such as magnetic resonance imaging, and the exclusion of other possible diseases. A simple and definitive biofluid test for MS does not exist, but is highly desirable. To address this need, we employed NMR-based metabolomics to identify potentially unique metabolite biomarkers of MS from a cohort of age and sex-matched samples of cerebrospinal fluid (CSF), serum, and urine from 206 progressive MS (PMS) patients, 46 relapsing-remitting MS (RRMS) patients, and 99 healthy volunteers without a MS diagnosis. We identified 32 metabolites in CSF that varied between the control and PMS patients. Utilizing patient-matched serum samples, we were able to further identify 31 serum metabolites that may serve as biomarkers for PMS patients. Lastly, we identified 14 urine metabolites associated with PMS. All potential biomarkers are associated with metabolic processes linked to the pathology of MS, such as demyelination and neuronal damage. Four metabolites with identical profiles across all three biofluids were discovered, which demonstrate their potential value as cross-biofluid markers of PMS. We further present a case for using metabolic profiles from PMS patients to delineate biomarkers of RRMS. Specifically, three metabolites exhibited a variation from healthy volunteers without MS through RRMS and PMS patients. The consistency of metabolite changes across multiple biofluids, combined with the reliability of a receiver operating characteristic classification, may provide a rapid diagnostic test for MS.

A lactate metabolism-related signature predicting patient prognosis and immune microenvironment in ovarian cancer

Introduction

Ovarian cancer (OV) is a highly lethal gynecological malignancy with a poor prognosis. Lactate metabolism is crucial for tumor cell survival, proliferation, and immune evasion. Our study aims to investigate the role of lactate metabolism-related genes (LMRGs) in OV and their potential as biomarkers for prognosis, immune microenvironment, and immunotherapy response.

Methods

Ovarian samples were collected from the TCGA cohort. And 12 lactate-related pathways were identified from the MsigDB database. Differentially expressed genes within these pathways were designated as LMRGs, which undergo unsupervised clustering to identify distinct clusters based on LMRGs. Subsequently, we assessed survival outcomes, immune cell infiltration levels, Hallmaker pathway activation patterns, and chemotaxis among different subtypes. After conducting additional unsupervised clustering based on differentially expressed genes (DEGs), significant differences in the expression of LMRGs between the two clusters were observed. The differentially expressed genes were subjected to subsequent functional enrichment analysis. Furthermore, we construct a model incorporating LMRGs. Subsequently, the lactate score for each tumor sample was calculated based on this model, facilitating the classification of samples into high and low groups according to their respective lactate scores. Distinct groups examined disparities in survival prognosis, copy number variation (CNV), single nucleotide variation (SNV), and immune infiltration. The lactate score served as a quantitative measure of OV's lactate metabolism pattern and an independent prognostic factor.

Results

This study investigated the potential role of LMRGs in tumor microenvironment diversity and prognosis in OV, suggesting that LMRGs play a crucial role in OV progression and the tumor microenvironment, thus serving as novel indicators for prognosis, immune microenvironment status, and response to immunotherapy.

Metabolomic data presents challenges for epidemiological meta-analysis: a case study of childhood body mass index from the ECHO consortium

INTRODUCTION

Meta-analyses across diverse independent studies provide improved confidence in results. However, within the context of metabolomic epidemiology, meta-analysis investigations are complicated by differences in study design, data acquisition, and other factors that may impact reproducibility.

OBJECTIVE

The objective of this study was to identify maternal blood metabolites during pregnancy (> 24 gestational weeks) related to offspring body mass index (BMI) at age two years through a meta-analysis framework.

METHODS

We used adjusted linear regression summary statistics from three cohorts (total N = 1012 mother-child pairs) participating in the NIH Environmental influences on Child Health Outcomes (ECHO) Program. We applied a random-effects meta-analysis framework to regression results and adjusted by false discovery rate (FDR) using the Benjamini-Hochberg procedure.

RESULTS

Only 20 metabolites were detected in all three cohorts, with an additional 127 metabolites detected in two of three cohorts. Of these 147, 6 maternal metabolites were nominally associated (P < 0.05) with offspring BMI z-scores at age 2 years in a meta-analytic framework including at least two studies: arabinose (Coefmeta = 0.40 [95% CI 0.10,0.70], Pmeta = 9.7 × 10-3), guanidinoacetate (Coefmeta = - 0.28 [- 0.54, - 0.02], Pmeta = 0.033), 3-ureidopropionate (Coefmeta = 0.22 [0.017,0.41], Pmeta = 0.033), 1-methylhistidine (Coefmeta = - 0.18 [- 0.33, - 0.04], Pmeta = 0.011), serine (Coefmeta = - 0.18 [- 0.36, - 0.01], Pmeta = 0.034), and lysine (Coefmeta = - 0.16 [- 0.32, - 0.01], Pmeta = 0.044). No associations were robust to multiple testing correction.

CONCLUSIONS

Despite including three cohorts with large sample sizes (N > 100), we failed to identify significant metabolite associations after FDR correction. Our investigation demonstrates difficulties in applying epidemiological meta-analysis to clinical metabolomics, emphasizes challenges to reproducibility, and highlights the need for standardized best practices in metabolomic epidemiology.

Metabolic heterogeneity in cancer

Cancer cells rewire their metabolism to survive during cancer progression. In this context, tumour metabolic heterogeneity arises and develops in response to diverse environmental factors. This metabolic heterogeneity contributes to cancer aggressiveness and impacts therapeutic opportunities. In recent years, technical advances allowed direct characterisation of metabolic heterogeneity in tumours. In addition to the metabolic heterogeneity observed in primary tumours, metabolic heterogeneity temporally evolves along with tumour progression. In this Review, we summarize the mechanisms of environment-induced metabolic heterogeneity. In addition, we discuss how cancer metabolism and the key metabolites and enzymes temporally and functionally evolve during the metastatic cascade and treatment.

A Metabolites Merging Strategy (MMS): Harmonization to Enable Studies' Intercomparison

Metabolomics encounters challenges in cross-study comparisons due to diverse metabolite nomenclature and reporting practices. To bridge this gap, we introduce the Metabolites Merging Strategy (MMS), offering a systematic framework to harmonize multiple metabolite datasets for enhanced interstudy comparability. MMS has three steps. Step 1: Translation and merging of the different datasets by employing InChIKeys for data integration, encompassing the translation of metabolite names (if needed). Followed by Step 2: Attributes' retrieval from the InChIkey, including descriptors of name (title name from PubChem and RefMet name from Metabolomics Workbench), and chemical properties (molecular weight and molecular formula), both systematic (InChI, InChIKey, SMILES) and non-systematic identifiers (PubChem, CheBI, HMDB, KEGG, LipidMaps, DrugBank, Bin ID and CAS number), and their ontology. Finally, a meticulous three-step curation process is used to rectify disparities for conjugated base/acid compounds (optional step), missing attributes, and synonym checking (duplicated information). The MMS procedure is exemplified through a case study of urinary asthma metabolites, where MMS facilitated the identification of significant pathways hidden when no dataset merging strategy was followed. This study highlights the need for standardized and unified metabolite datasets to enhance the reproducibility and comparability of metabolomics studies.

Metabolic signature for a dysbiotic microbiome in the female genital tract: A systematic review and meta-analysis

BACKGROUND

The vaginal microbiome (VMB) is a critical determinant of reproductive health, where a microbial shift towards a dysbiotic environment has implications for susceptibility to, and clinical presentation of sexually transmitted infections (STIs). Metabolomic profiling of the vaginal microenvironment has led to the identification of metabolic responses to clinical conditions of dysbiosis. However, no studies have examined metabolic markers that are common across conditions and can serve as a signature for vaginal dysbiosis.

METHOD OF STUDY

We have conducted a comprehensive systematic review and meta-analysis to identify consistently deregulated metabolites along with their impact on host and microbial metabolism during dysbiosis. We employed two complementary approaches including a vote counting analysis for all eligible studies identified in the systematic review, in addition to a meta-analysis for a subset of studies with sufficient available data. Significantly deregulated metabolites were then selected for pathway enrichment analysis.

RESULTS

Our results revealed a total of 502 altered metabolites reported across 10 dysbiotic conditions from 16 studies. Following a rigorous, collective analysis, six metabolites which were consistently downregulated and could be generalized to all dysbiotic conditions were identified. In addition, five downregulated and one upregulated metabolite was identified from a bacterial vaginosis (BV) focused sub-analysis. These metabolites have the potential to serve as a metabolic signature for vaginal dysbiosis. Their role in eight altered metabolic pathways indicates a disruption of amino acid, carbohydrate, and energy metabolism during dysbiosis.

CONCLUSION

Based on this analysis, we propose a schematic model outlining the common metabolic perturbations associated with vaginal dysbiosis, which can be potential targets for therapeutics and prophylaxis.

SMetaS: A Sample Metadata Standardizer for Metabolomics

Metabolomics has advanced to an extent where it is desired to standardize and compare data across individual studies. While past work in standardization has focused on data acquisition, data processing, and data storage aspects, metabolomics databases are useless without ontology-based descriptions of biological samples and study designs. We introduce here a user-centric tool to automatically standardize sample metadata. Using such a tool in frontends for metabolomic databases will dramatically increase the FAIRness (Findability, Accessibility, Interoperability, and Reusability) of data, specifically for data reuse and for finding datasets that share comparable sets of metadata, e.g., study meta-analyses, cross-species analyses or large scale metabolomic atlases. SMetaS (Sample Metadata Standardizer) combines a classic database with an API and frontend and is provided in a containerized environment. The tool has two user-centric components. In the first component, the user designs a sample metadata matrix and fills the cells using natural language terminology. In the second component, the tool transforms the completed matrix by replacing freetext terms with terms from fixed vocabularies. This transformation process is designed to maximize simplicity and is guided by, among other strategies, synonym matching and typographical fixing in an n-grams/nearest neighbors model approach. The tool enables downstream analysis of submitted studies and samples via string equality for FAIR retrospective use.

The BinDiscover database: a biology-focused meta-analysis tool for 156,000 GC-TOF MS metabolome samples

Metabolomics by gas chromatography/mass spectrometry (GC/MS) provides a standardized and reliable platform for understanding small molecule biology. Since 2005, the West Coast Metabolomics Center at the University of California at Davis has collated GC/MS metabolomics data from over 156,000 samples and 2000 studies into the standardized BinBase database. We believe that the observations from these samples will provide meaningful insight to biologists and that our data treatment and webtool will provide insight to others who seek to standardize disparate metabolomics studies. We here developed an easy-to-use query interface, BinDiscover, to enable intuitive, rapid hypothesis generation for biologists based on these metabolomic samples. BinDiscover creates observation summaries and graphics across a broad range of species, organs, diseases, and compounds. Throughout the components of BinDiscover, we emphasize the use of ontologies to aggregate large groups of samples based on the proximity of their metadata within these ontologies. This adjacency allows for the simultaneous exploration of entire categories such as "rodents", "digestive tract", or "amino acids". The ontologies are particularly relevant for BinDiscover's ontologically grouped differential analysis, which, like other components of BinDiscover, creates clear graphs and summary statistics across compounds and biological metadata. We exemplify BinDiscover's extensive applicability in three showcases across biological domains.

Cancer metabolites: promising biomarkers for cancer liquid biopsy

Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.

Altered neurometabolite levels in the brains of patients with depression: A systematic analysis of magnetic resonance spectroscopy studies

BACKGROUND

Numerous magnetic resonance spectroscopy (MRS) studies have reported metabolic abnormalities in the brains of patients with depression, although inconsistent results have been reported. The aim of this study was to explore changes in neurometabolite levels in patients with depression across large-scale MRS studies.

METHOD

A total of 307 differential metabolite entries associated with depression were retrieved from 180 MRS studies retrieved from the Metabolite Network of Depression Database. The vote-counting method was used to identify consistently altered metabolites in the whole brain and specific brain regions of patients with depression.

RESULTS

Only few differential neurometabolites showed a stable change trend. The levels of total choline (tCho) and the tCho/N-acetyl aspartate (NAA) ratio were consistently higher in the brains of patients with depression, and that the levels of NAA, glutamate and glutamine (Glx), and gamma-aminobutyric acid (GABA) were lower. For specific brain regions, we found lower Glx levels in the prefrontal cortex and lower GABA concentrations in the occipital cortex. We also found lower concentrations of NAA in the anterior cingulate cortex and prefrontal cortex. The levels of tCho were higher in the prefrontal cortex and putamen.

CONCLUSION

Our results revealed that most altered neurometabolites in previous studies lack of adequate reproducibility. Through vote-counting method with large-scale studies, downregulation of glutamatergic neurometabolites, impaired neuronal integrity, and disturbed membrane metabolism were found in the pathobiology of depression, which contribute to existing knowledge of neurometabolic changes in depression. Further studies based on a larger dataset are needed to confirm our findings.

Circulating amino acid levels and colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition and UK Biobank cohorts

BACKGROUND

Amino acid metabolism is dysregulated in colorectal cancer patients; however, it is not clear whether pre-diagnostic levels of amino acids are associated with subsequent risk of colorectal cancer. We investigated circulating levels of amino acids in relation to colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) and UK Biobank cohorts.

METHODS

Concentrations of 13-21 amino acids were determined in baseline fasting plasma or serum samples in 654 incident colorectal cancer cases and 654 matched controls in EPIC. Amino acids associated with colorectal cancer risk following adjustment for the false discovery rate (FDR) were then tested for associations in the UK Biobank, for which measurements of 9 amino acids were available in 111,323 participants, of which 1221 were incident colorectal cancer cases.

RESULTS

Histidine levels were inversely associated with colorectal cancer risk in EPIC (odds ratio [OR] 0.80 per standard deviation [SD], 95% confidence interval [CI] 0.69-0.92, FDR P-value=0.03) and in UK Biobank (HR 0.93 per SD, 95% CI 0.87-0.99, P-value=0.03). Glutamine levels were borderline inversely associated with colorectal cancer risk in EPIC (OR 0.85 per SD, 95% CI 0.75-0.97, FDR P-value=0.08) and similarly in UK Biobank (HR 0.95, 95% CI 0.89-1.01, P=0.09) In both cohorts, associations changed only minimally when cases diagnosed within 2 or 5 years of follow-up were excluded.

CONCLUSIONS

Higher circulating levels of histidine were associated with a lower risk of colorectal cancer in two large prospective cohorts. Further research to ascertain the role of histidine metabolism and potentially that of glutamine in colorectal cancer development is warranted.

Differential sensing with arrays of de novo designed peptide assemblies

Differential sensing attempts to mimic the mammalian senses of smell and taste to identify analytes and complex mixtures. In place of hundreds of complex, membrane-bound G-protein coupled receptors, differential sensors employ arrays of small molecules. Here we show that arrays of computationally designed de novo peptides provide alternative synthetic receptors for differential sensing. We use self-assembling α-helical barrels (αHBs) with central channels that can be altered predictably to vary their sizes, shapes and chemistries. The channels accommodate environment-sensitive dyes that fluoresce upon binding. Challenging arrays of dye-loaded barrels with analytes causes differential fluorophore displacement. The resulting fluorimetric fingerprints are used to train machine-learning models that relate the patterns to the analytes. We show that this system discriminates between a range of biomolecules, drink, and diagnostically relevant biological samples. As αHBs are robust and chemically diverse, the system has potential to sense many analytes in various settings.

Plasma metabolomics, lipidomics and cytokinomics profiling predict disease recurrence in metastatic colorectal cancer patients undergoing liver resection

Purpose

In metastatic colorectal cancer (mCRC) patients (pts), treatment strategies integrating liver resection with induction chemotherapy offer better 5-year survival rates than chemotherapy alone. However, liver resection is a complex and costly procedure, and recurrence occurs in almost 2/3rds of pts, suggesting the need to identify those at higher risk. The aim of this work was to evaluate whether the integration of plasma metabolomics and lipidomics combined with the multiplex analysis of a large panel of plasma cytokines can be used to predict the risk of relapse and other patient outcomes after liver surgery, beyond or in combination with clinical morphovolumetric criteria.

Experimental design

Peripheral blood metabolomics and lipidomics were performed by 600 MHz NMR spectroscopy on plasma from 30 unresectable mCRC pts treated with bevacizumab plus oxaliplatin-based regimens within the Obelics trial (NCT01718873) and subdivided into responder (R) and non-R (NR) according to 1-year disease-free survival (DFS): ≥ 1-year (R, n = 12) and < 1-year (NR, n = 18). A large panel of cytokines, chemokines, and growth factors was evaluated on the same plasma using Luminex xMAP-based multiplex bead-based immunoassay technology. A multiple biomarkers model was built using a support vector machine (SVM) classifier.

Results

Sparse partial least squares discriminant analysis (sPLS-DA) and loading plots obtained by analyzing metabolomics profiles of samples collected at the time of response evaluation when resectability was established showed significantly different levels of metabolites between the two groups. Two metabolites, 3-hydroxybutyrate and histidine, significantly predicted DFS and overall survival. Lipidomics analysis confirmed clear differences between the R and NR pts, indicating a statistically significant increase in lipids (cholesterol, triglycerides and phospholipids) in NR pts, reflecting a nonspecific inflammatory response. Indeed, a significant increase in proinflammatory cytokines was demonstrated in NR pts plasma. Finally, a multiple biomarkers model based on the combination of presurgery plasma levels of 3-hydroxybutyrate, cholesterol, phospholipids, triglycerides and IL-6 was able to correctly classify patients by their DFS with good accuracy.

Conclusion

Overall, this exploratory study suggests the potential of these combined biomarker approaches to predict outcomes in mCRC patients who are candidates for liver metastasis resection after induction treatment for defining personalized management and treatment strategies.

Relationship between 4-Hydroxynonenal (4-HNE) as Systemic Biomarker of Lipid Peroxidation and Metabolomic Profiling of Patients with Prostate Cancer

An oxidative degradation product of the polyunsaturated fatty acids, 4-hydroxynonenal (4-HNE), is of particular interest in cancer research due to its concentration-dependent pleiotropic activities affecting cellular antioxidants, metabolism, and growth control. Although an increase in oxidative stress and lipid peroxidation was already associated with prostate cancer progression a few decades ago, the knowledge of the involvement of 4-HNE in prostate cancer tumorigenesis is limited. This study investigated the appearance of 4-HNE-protein adducts in prostate cancer tissue by immunohistochemistry using a genuine 4-HNE monoclonal antibody. Plasma samples of the same patients and samples of the healthy controls were also analyzed for the presence of 4-HNE-protein adducts, followed by metabolic profiling using LC-ESI-QTOF-MS and GC-EI-Q-MS. Finally, the analysis of the metabolic pathways affected by 4-HNE was performed. The obtained results revealed the absence of 4-HNE-protein adducts in prostate carcinoma tissue but increased 4-HNE-protein levels in the plasma of these patients. Metabolomics revealed a positive association of different long-chain and medium-chain fatty acids with the presence of prostate cancer. Furthermore, while linoleic acid positively correlated with the levels of 4-HNE-protein adducts in the blood of healthy men, no correlation was obtained for cancer patients indicating altered lipid metabolism in this case. The metabolic pathway of unsaturated fatty acids biosynthesis emerged as significantly affected by 4-HNE. Overall, this is the first study linking 4-HNE adduction to plasma proteins with specific alterations in the plasma metabolome of prostate cancer patients. This study revealed that increased 4-HNE plasma protein adducts could modulate the unsaturated fatty acids biosynthesis pathway. It is yet to be determined if this is a direct result of 4-HNE or whether they are produced by the same underlying mechanisms. Further mechanistic studies are needed to grasp the biological significance of the observed changes in prostate cancer tumorigenesis.

New molecular mechanisms in cholangiocarcinoma: signals triggering interleukin-6 production in tumor cells and KRAS co-opted epigenetic mediators driving metabolic reprogramming

Background

Cholangiocarcinoma (CCA) is still a deadly tumour. Histological and molecular aspects of thioacetamide (TAA)-induced intrahepatic CCA (iCCA) in rats mimic those of human iCCA. Carcinogenic changes and therapeutic vulnerabilities in CCA may be captured by molecular investigations in bile, where we performed bile proteomic and metabolomic analyses that help discovery yet unknown pathways relevant to human iCCA.

Methods

Cholangiocarcinogenesis was induced in rats (TAA) and mice (Jnk^Δhepa + CCl₄ + DEN model). We performed proteomic and metabolomic analyses in bile from control and CCA-bearing rats. Differential expression was validated in rat and human CCAs. Mechanisms were addressed in human CCA cells, including Huh28-KRAS^G12D cells. Cell signaling, growth, gene regulation and [U-¹³C]-D-glucose-serine fluxomics analyses were performed. In vivo studies were performed in the clinically-relevant iCCA mouse model.

Results

Pathways related to inflammation, oxidative stress and glucose metabolism were identified by proteomic analysis. Oxidative stress and high amounts of the oncogenesis-supporting amino acids serine and glycine were discovered by metabolomic studies. Most relevant hits were confirmed in rat and human CCAs (TCGA). Activation of interleukin-6 (IL6) and epidermal growth factor receptor (EGFR) pathways, and key genes in cancer-related glucose metabolic reprogramming, were validated in TAA-CCAs. In TAA-CCAs, G9a, an epigenetic pro-tumorigenic writer, was also increased. We show that EGFR signaling and mutant KRAS^G12D can both activate IL6 production in CCA cells. Furthermore, phosphoglycerate dehydrogenase (PHGDH), the rate-limiting enzyme in serine-glycine pathway, was upregulated in human iCCA correlating with G9a expression. In a G9a activity-dependent manner, KRAS^G12D promoted PHGDH expression, glucose flow towards serine synthesis, and increased CCA cell viability. KRAS^G12D CAA cells were more sensitive to PHGDH and G9a inhibition than controls. In mouse iCCA, G9a pharmacological targeting reduced PHGDH expression.

Conclusions

In CCA, we identified new pro-tumorigenic mechanisms: Activation of EGFR signaling or KRAS mutation drives IL6 expression in tumour cells; Glucose metabolism reprogramming in iCCA includes activation of the serine-glycine pathway; Mutant KRAS drives PHGDH expression in a G9a-dependent manner; PHGDH and G9a emerge as therapeutic targets in iCCA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02386-2.

Urine NMR Metabolomics for Precision Oncology in Colorectal Cancer

Metabolomics is a fundamental approach to discovering novel biomarkers and their potential use for precision medicine. When applied for population screening, NMR-based metabolomics can become a powerful clinical tool in precision oncology. Urine tests can be more widely accepted due to their intrinsic non-invasiveness. Our review provides the first exhaustive evaluation of NMR metabolomics for the determination of colorectal cancer (CRC) in urine. A specific search in PubMed, Web of Science, and Scopus was performed, and 10 studies met the required criteria. There were no restrictions on the query for study type, leading to not only colorectal cancer samples versus control comparisons, but also prospective studies of surgical effects. With this review, all compounds in the included studies were merged into a database. In doing so, we identified up to 100 compounds in urine samples, and 11 were found in at least three articles. Results were analyzed in three groups: case (CRC and adenomas)/control, pre-/post-surgery, and combining both groups. When combining the case-control and the pre-/post-surgery groups, up to twelve compounds were found to be relevant. Seven down-regulated metabolites in CRC were identified, creatinine, 4-hydroxybenzoic acid, acetone, carnitine, d-glucose, hippuric acid, l-lysine, l-threonine, and pyruvic acid, and three up-regulated compounds in CRC were identified, acetic acid, phenylacetylglutamine, and urea. The pathways and enrichment analysis returned only two pathways significantly expressed: the pyruvate metabolism and the glycolysis/gluconeogenesis pathway. In both cases, only the pyruvic acid (down-regulated in urine of CRC patients, with cancer cell proliferation effect in the tissue) and acetic acid (up-regulated in urine of CRC patients, with chemoprotective effect) were present.

Optimized Systematic Review Tool: Application to Candidate Biomarkers for the Diagnosis of Hepatocellular Carcinoma

This review aims to develop an appropriate review tool for systematically collating metabolites that are dysregulated in disease and applies the method to identify novel diagnostic biomarkers for hepatocellular carcinoma (HCC). Studies that analyzed metabolites in blood or urine samples where HCC was compared with comparison groups (healthy, precirrhotic liver disease, cirrhosis) were eligible. Tumor tissue was included to help differentiate primary and secondary biomarkers. Searches were conducted on Medline and EMBASE. A bespoke "risk of bias" tool for metabolomic studies was developed adjusting for analytic quality. Discriminant metabolites for each sample type were ranked using a weighted score accounting for the direction and extent of change and the risk of bias of the reporting publication. A total of 84 eligible studies were included in the review (54 blood, 9 urine, and 15 tissue), with six studying multiple sample types. High-ranking metabolites, based on their weighted score, comprised energy metabolites, bile acids, acylcarnitines, and lysophosphocholines. This new review tool addresses an unmet need for incorporating quality of study design and analysis to overcome the gaps in standardization of reporting of metabolomic data. Validation studies, standardized study designs, and publications meeting minimal reporting standards are crucial for advancing the field beyond exploratory studies.

Discovery of Synergistic Drug Combinations for Colorectal Cancer Driven by Tumor Barcode Derived from Metabolomics “Big Data”

The accumulation of cancer metabolomics data in the past decade provides exceptional opportunities for deeper investigations into cancer metabolism. However, integrating a large amount of heterogeneous metabolomics data to draw a full picture of the metabolic reprogramming and to discover oncometabolites of certain cancers remains challenging. In this study, a tumor barcode constructed based upon existing metabolomics “big data” using the Bayesian vote-counting method is proposed to identify oncometabolites in colorectal cancer (CRC). Specifically, a panel of oncometabolites of CRC was generated from 39 clinical studies with 3202 blood samples (1332 CRC vs. 1870 controls) and 990 tissue samples (495 CRC vs. 495 controls). Next, an oncometabolite-protein network was constructed by combining the tumor barcode and its involved proteins/enzymes. The effect of anti-cancer drugs or drug combinations was then mapped into this network by the random walk with restart process. Utilizing this network, potential Irinotecan (CPT-11)-sensitizing agents for CRC treatment were discovered by random forest and Xgboost. Finally, a compound named MK-2206 was highlighted and its synergy with CPT-11 was validated on two CRC cell lines. To summarize, we demonstrate in the present study that the metabolomics “big data”-based tumor barcodes and the subsequent network analyses are potentially useful for drug combination discovery or drug repositioning.

A pan-cancer metabolic atlas of the tumor microenvironment

Tumors are heterogeneous cellular environments with entwined metabolic dependencies. Here, we use a tumor transcriptome deconvolution approach to profile the metabolic states of cancer and non-cancer (stromal) cells in bulk tumors of 20 solid tumor types. We identify metabolic genes and processes recurrently altered in cancer cells across tumor types, highlighting pan-cancer upregulation of deoxythymidine triphosphate (dTTP) production. In contrast, the tryptophan catabolism rate-limiting enzymes IDO1 and TDO2 are highly overexpressed in stroma, raising the hypothesis that kynurenine-mediated suppression of antitumor immunity may be predominantly constrained by the stroma. Oxidative phosphorylation is the most upregulated metabolic process in cancer cells compared to both stromal cells and a large atlas of cancer cell lines, suggesting that the Warburg effect may be less pronounced in cancer cells in vivo. Overall, our analysis highlights fundamental differences in metabolic states of cancer and stromal cells inside tumors and establishes a pan-cancer resource to interrogate tumor metabolism.

Evaluating and minimizing batch effects in metabolomics

Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in analytical tools such as ultrahigh-resolution chromatography and mass spectrometry, an increasing number of metabolites can now be profiled with high quantification accuracy. The increased detectability and accuracy raise the level of stringiness required to reduce or control any experimental artifacts that can interfere with the measurement of phenotype-related metabolome changes. One of the artifacts is the batch effect that can be caused by multiple sources. In this review, we discuss the origins of batch effects, approaches to detect interbatch variations, and methods to correct unwanted data variability due to batch effects. We recognize that minimizing batch effects is currently an active research area, yet a very challenging task from both experimental and data processing perspectives. Thus, we try to be critical in describing the performance of a reported method with the hope of stimulating further studies for improving existing methods or developing new methods.

Effects of pharmacological treatment on metabolomic alterations in animal models of depression

Numerous studies have investigated metabolite alterations resulting from pharmacological treatment in depression models although few quantitative studies explored metabolites exhibiting constant alterations. This study aimed to identify consistently dysregulated metabolites across such studies using a knowledgebase-driven approach. This study was based on 157 studies that identified an assembly of 2757 differential metabolites in the brain, blood, urine, liver, and feces samples of depression models with pharmacological medication. The use of a vote-counting approach to identify consistently upregulated and downregulated metabolites showed that serotonin, dopamine, norepinephrine, gamma-aminobutyric acid, anandamide, tryptophan, hypoxanthine, and 3-methoxytyramine were upregulated in the brain, while quinolinic acid, glutamic acid, 5-hydroxyindoleacetic acid, myo-inositol, lactic acid, and the kynurenine/tryptophan ratio were downregulated. Circulating levels of trimethylamine N-oxide, isoleucine, leucine, tryptophan, creatine, serotonin, valine, betaine, and low-density lipoprotein were elevated. In contrast, levels of alpha-D-glucose, lactic acid, N-acetyl glycoprotein, glutamine, beta-D-glucose, corticosterone, alanine, phenylacetylglycine, glycine, high-density lipoprotein, arachidonic acid, myo-inositol, allantoin, and taurine were decreased. Moreover, 12 metabolites in urine and nine metabolites in the liver were dysregulated after treatment. Pharmacological treatment also increased fecal levels of butyric acid, acetic acid, propionic acid, and isovaleric acid. Collectively, metabolite disturbances induced by depression were reversed by pharmacological treatment. Pharmacological medication reversed the reduction of brain neurotransmitters caused by depression, modulated disturbance of the tryptophan-kynurenine pathway and inflammatory activation, and alleviated abnormalities of amino acid metabolism, energy metabolism, lipid metabolism, and gut microbiota-derived metabolites.

Gene-environment interaction analysis of redox-related metals and genetic variants with plasma metabolic patterns in a general population from Spain: The Hortega Study

Background

Limited studies have evaluated the joint influence of redox-related metals and genetic variation on metabolic pathways. We analyzed the association of 11 metals with metabolic patterns, and the interacting role of candidate genetic variants, in 1145 participants from the Hortega Study, a population-based sample from Spain.

Methods

Urine antimony (Sb), arsenic, barium (Ba), cadmium (Cd), chromium (Cr), cobalt (Co), molybdenum (Mo) and vanadium (V), and plasma copper (Cu), selenium (Se) and zinc (Zn) were measured by ICP-MS and AAS, respectively. We summarized 54 plasma metabolites, measured with targeted NMR, by estimating metabolic principal components (mPC). Redox-related SNPs (N = 291) were measured by oligo-ligation assay.

Results

In our study, the association with metabolic principal component (mPC) 1 (reflecting non-essential and essential amino acids, including branched chain, and bacterial co-metabolism versus fatty acids and VLDL subclasses) was positive for Se and Zn, but inverse for Cu, arsenobetaine-corrected arsenic (As) and Sb. The association with mPC2 (reflecting essential amino acids, including aromatic, and bacterial co-metabolism) was inverse for Se, Zn and Cd. The association with mPC3 (reflecting LDL subclasses) was positive for Cu, Se and Zn, but inverse for Co. The association for mPC4 (reflecting HDL subclasses) was positive for Sb, but inverse for plasma Zn. These associations were mainly driven by Cu and Sb for mPC1; Se, Zn and Cd for mPC2; Co, Se and Zn for mPC3; and Zn for mPC4. The most SNP-metal interacting genes were NOX1, GSR, GCLC, AGT and REN. Co and Zn showed the highest number of interactions with genetic variants associated to enriched endocrine, cardiovascular and neurological pathways.

Conclusions

Exposures to Co, Cu, Se, Zn, As, Cd and Sb were associated with several metabolic patterns involved in chronic disease. Carriers of redox-related variants may have differential susceptibility to metabolic alterations associated to excessive exposure to metals.

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In a population-based sample, cobalt, copper, selenium, zinc, arsenic, cadmium and antimony exposures were related to some metabolic patterns.

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Carriers of redox-related variants displayed differential susceptibility to metabolic alterations associated to excessive metal exposures.

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Cobalt and zinc showed a number of statistical interactions with variants from genes sharing biological pathways with a role in chronic diseases.

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The metabolic impact of metals combined with variation in redox-related genes might be large in the population, given metals widespread exposure.

Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach

The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology’s potential, a set of transcriptomic datasets are meta-analyzed as an example.

Fibroblast pyruvate carboxylase is required for collagen production in the tumour microenvironment

The aberrant production of collagen by fibroblasts is a hallmark of many solid tumours and can influence cancer progression. How the mesenchymal cells in the tumour microenvironment maintain their production of extracellular matrix proteins as the vascular delivery of glutamine and glucose becomes compromised remains unclear. Here we show that pyruvate carboxylase (PC)-mediated anaplerosis in tumour-associated fibroblasts contributes to tumour fibrosis and growth. Using cultured mesenchymal and cancer cells, as well as mouse allograft models, we provide evidence that extracellular lactate can be utilized by fibroblasts to maintain tricarboxylic acid (TCA) cycle anaplerosis and non-essential amino acid biosynthesis through PC activity. Furthermore, we show that fibroblast PC is required for collagen production in the tumour microenvironment. These results establish TCA cycle anaplerosis as a determinant of extracellular matrix collagen production, and identify PC as a potential target to inhibit tumour desmoplasia.

CD8+ T cell metabolism in infection and cancer

Cytotoxic CD8⁺ T cells play a key role in the elimination of intracellular infections and malignant cells and can provide long-term protective immunity. In the response to infection, CD8⁺ T cell metabolism is coupled to transcriptional, translational and epigenetic changes that are driven by extracellular metabolites and immunological signals. These programmes facilitate the adaptation of CD8⁺ T cells to the diverse and dynamic metabolic environments encountered in the circulation and in the tissues. In the setting of disease, both cell-intrinsic and cell-extrinsic metabolic cues contribute to CD8⁺ T cell dysfunction. In addition, changes in whole-body metabolism, whether through voluntary or disease-induced dietary alterations, can influence CD8⁺ T cell-mediated immunity. Defining the metabolic adaptations of CD8⁺ T cells in specific tissue environments informs our understanding of how these cells protect against pathogens and tumours and maintain tissue health at barrier sites. Here, we highlight recent findings revealing how metabolic networks enforce specific CD8⁺ T cell programmes and discuss how metabolism is integrated with CD8⁺ T cell differentiation and function and determined by environmental cues.

Non-invasive metabolic biomarkers for early diagnosis of diabetic nephropathy: Meta-analysis of profiling metabolomics studies

AIM

Diabetic nephropathy (DN) is one of the worst complications of diabetes. Despite a growing number of DN metabolite profiling studies, most studies are suffering from inconsistency in their findings. The main goal of this meta-analysis was to reach to a consensus panel of significantly dysregulated metabolites as potential biomarkers in DN.

DATA SYNTHESIS

To identify the significant dysregulated metabolites, meta-analysis was performed by "vote-counting rank" and "robust rank aggregation" strategies. Bioinformatics analyses were performed to identify the most affected genes and pathways. Among 44 selected studies consisting of 98 metabolite profiles, 17 metabolites (9 up-regulated and 8 down-regulated metabolites), were identified as significant ones by both the meta-analysis strategies (p-value<0.05 and OR>2 or <0.5) and selected as DN metabolite meta-signature. Furthermore, enrichment analyses confirmed the involvement of various effective biological pathways in DN pathogenesis, such as urea cycle, TCA cycle, glycolysis, and amino acid metabolisms. Finally, by performing a meta-analysis over existing time-course studies in DN, the results indicated that lactic acid, hippuric acid, allantoin (in urine), and glutamine (in blood), are the topmost non-invasive early diagnostic biomarkers.

CONCLUSION

The identified metabolites are potentially involved in diabetic nephropathy pathogenesis and could be considered as biomarkers or drug targets in the disease.

PROSPERO REGISTRATION NUMBER

CRD42020197697.

Individual variability in human urinary metabolites identifies age-related, body mass index-related, and sex-related biomarkers

Background

Metabolites present in human urine can be influenced by individual physiological parameters (e.g., body mass index [BMI], age, and sex). Observation of altered metabolites concentrations could provide insight into underlying disease pathology, disease prognosis and diagnosis, and facilitate discovery of novel biomarkers.

Methods

Quantitative metabolomics analysis in the urine of 183 healthy individuals was performed based on high‐resolution liquid chromatography–mass spectrometry (LC–MS). Coefficients of variation were obtained for 109 urine metabolites of all the 183 human healthy subjects.

Results

Three urine metabolites (such as dehydroepiandrosterone sulfate, acetaminophen glucuronide, and p‐anisic acid) with CV₁₈₃ > 0.3, for which metabolomics studies have been scarce, are considered highly variable here. We identified 30 age‐related metabolites, 18 BMI‐related metabolites, and 42 sex‐related metabolites. Among the identified metabolites, three metabolites were found to be associated with all three physiological parameters (age, BMI, and sex), which included dehydroepiandrosterone sulfate, 3‐methylcrotonylglycine and N‐acetyl‐aspartic acid. Pearson's coefficients demonstrated that some age‐, BMI‐, and sex‐related compounds are strongly correlated, suggesting that age, BMI, and sex could affect them concomitantly.

Conclusion

Metabolic differences between distinct physiological statuses were found to be related to several metabolic pathways (such as the caffeine metabolism, the amino acid metabolism, and the carbohydrate metabolism), and these findings may be key for the discovery of new diagnostics and treatments as well as new understandings on the mechanisms of some related diseases.

Metabolic Reprogramming of Colorectal Cancer Cells and the Microenvironment: Implication for Therapy

Colorectal carcinoma (CRC) is one of the most frequently diagnosed carcinomas and one of the leading causes of cancer-related death worldwide. Metabolic reprogramming, a hallmark of cancer, is closely related to the initiation and progression of carcinomas, including CRC. Accumulating evidence shows that activation of oncogenic pathways and loss of tumor suppressor genes regulate the metabolic reprogramming that is mainly involved in glycolysis, glutaminolysis, one-carbon metabolism and lipid metabolism. The abnormal metabolic program provides tumor cells with abundant energy, nutrients and redox requirements to support their malignant growth and metastasis, which is accompanied by impaired metabolic flexibility in the tumor microenvironment (TME) and dysbiosis of the gut microbiota. The metabolic crosstalk between the tumor cells, the components of the TME and the intestinal microbiota further facilitates CRC cell proliferation, invasion and metastasis and leads to therapy resistance. Hence, to target the dysregulated tumor metabolism, the TME and the gut microbiota, novel preventive and therapeutic applications are required. In this review, the dysregulation of metabolic programs, molecular pathways, the TME and the intestinal microbiota in CRC is addressed. Possible therapeutic strategies, including metabolic inhibition and immune therapy in CRC, as well as modulation of the aberrant intestinal microbiota, are discussed.

Comprehensive Volatilome and Metabolome Signatures of Colorectal Cancer in Urine: A Systematic Review and Meta-Analysis

To increase compliance with colorectal cancer screening programs and to reduce the recommended screening age, cheaper and easy non-invasiveness alternatives to the fecal immunochemical test should be provided. Following the PRISMA procedure of studies that evaluated the metabolome and volatilome signatures of colorectal cancer in human urine samples, an exhaustive search in PubMed, Web of Science, and Scopus found 28 studies that met the required criteria. There were no restrictions on the query for the type of study, leading to not only colorectal cancer samples versus control comparison but also polyps versus control and prospective studies of surgical effects, CRC staging and comparisons of CRC with other cancers. With this systematic review, we identified up to 244 compounds in urine samples (3 shared compounds between the volatilome and metabolome), and 10 of them were relevant in more than three articles. In the meta-analysis, nine studies met the criteria for inclusion, and the results combining the case-control and the pre-/post-surgery groups, eleven compounds were found to be relevant. Four upregulated metabolites were identified, 3-hydroxybutyric acid, L-dopa, L-histidinol, and N1, N12-diacetylspermine and seven downregulated compounds were identified, pyruvic acid, hydroquinone, tartaric acid, and hippuric acid as metabolites and butyraldehyde, ether, and 1,1,6-trimethyl-1,2-dihydronaphthalene as volatiles.

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.

Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma

Multiple myeloma (MM), the second most common hematologic cancer, is caused by accumulation of aberrant plasma cells in the bone marrow. Its molecular causes are not fully understood and its great heterogeneity among patients complicates therapeutic decision-making. In the past decades, development of new therapies and drugs have significantly improved survival of MM patients. However, resistance to drugs and relapse remain the most common causes of mortality and are the major challenges to overcome. The advent of high throughput omics technologies capable of analyzing big amount of clinical and biological data has changed the way to diagnose and treat MM. Integration of omics data (gene mutations, gene expression, epigenetic information, and protein and metabolite levels) with clinical histories of thousands of patients allows to build scores to stratify the risk at diagnosis and predict the response to treatment, helping clinicians to make better educated decisions for each particular case. There is no doubt that the future of MM treatment relies on personalized therapies based on predictive models built from omics studies. This review summarizes the current treatments and the use of omics technologies in MM, and their importance in the implementation of personalized medicine.

Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma

Multiple myeloma (MM), the second most common hematologic cancer, is caused by accumulation of aberrant plasma cells in the bone marrow. Its molecular causes are not fully understood and its great heterogeneity among patients complicates therapeutic decision-making. In the past decades, development of new therapies and drugs have significantly improved survival of MM patients. However, resistance to drugs and relapse remain the most common causes of mortality and are the major challenges to overcome. The advent of high throughput omics technologies capable of analyzing big amount of clinical and biological data has changed the way to diagnose and treat MM. Integration of omics data (gene mutations, gene expression, epigenetic information, and protein and metabolite levels) with clinical histories of thousands of patients allows to build scores to stratify the risk at diagnosis and predict the response to treatment, helping clinicians to make better educated decisions for each particular case. There is no doubt that the future of MM treatment relies on personalized therapies based on predictive models built from omics studies. This review summarizes the current treatments and the use of omics technologies in MM, and their importance in the implementation of personalized medicine.

The ins and outs of serine and glycine metabolism in cancer

Cancer cells reprogramme their metabolism to support unrestrained proliferation and survival in nutrient-poor conditions. Whereas non-transformed cells often have lower demands for serine and glycine, several cancer subtypes hyperactivate intracellular serine and glycine synthesis and become addicted to de novo production. Copy-number amplifications of serine- and glycine-synthesis genes and genetic alterations in common oncogenes and tumour-suppressor genes enhance serine and glycine synthesis, resulting in high production and secretion of these oncogenesis-supportive metabolites. In this Review, we discuss the contribution of serine and glycine synthesis to cancer progression. By relying on de novo synthesis pathways, cancer cells are able to enhance macromolecule synthesis, neutralize high levels of oxidative stress and regulate methylation and tRNA formylation. Furthermore, we discuss the immunosuppressive potential of serine and glycine, and the essentiality of both amino acids to promoting survival of non-transformed neighbouring cells. Finally, we point to the emerging data proposing moonlighting functions of serine- and glycine-synthesis enzymes and examine promising small molecules targeting serine and glycine synthesis.

Cancers in Agreement? Exploring the Cross-Talk of Cancer Metabolomic and Transcriptomic Landscapes Using Publicly Available Data

Simple Summary

Changes in metabolism are a well-known characteristic of cancer cells. Different cancer types are unique in their genetic aspects, but also in their metabolism, which is in turn, governed by genetics. The aim of our study was to find these differences in metabolic behavior across different cancer types and uncovering intersections between gene expression and metabolic deregulations. We scoured the public domain for metabolomics and transcriptomics data from clinical profiling studies to perform a comprehensive comparison study. By combining evidence from both the genetic and the metabolic aspects, we described the most prominently aberrated pathways across eight different cancer types together with their metabolomic and transcriptomics similarities.

Abstract

One of the major hallmarks of cancer is the derailment of a cell’s metabolism. The multifaceted nature of cancer and different cancer types is transduced by both its transcriptomic and metabolomic landscapes. In this study, we re-purposed the publicly available transcriptomic and metabolomics data of eight cancer types (breast, lung, gastric, renal, liver, colorectal, prostate, and multiple myeloma) to find and investigate differences and commonalities on a pathway level among different cancer types. Topological analysis of inferred graphical Gaussian association networks showed that cancer was strongly defined in genetic networks, but not in metabolic networks. Using different statistical approaches to find significant differences between cancer and control cases, we highlighted the difficulties of high-level data-merging and in using statistical association networks. Cancer transcriptomics and metabolomics and landscapes were characterized by changed macro-molecule production, however, only major metabolic deregulations with highly impacted pathways were found in liver cancer. Cell cycle was enriched in breast, liver, and colorectal cancer, while breast and lung cancer were distinguished by highly enriched oncogene signaling pathways. A strong inflammatory response was observed in lung cancer and, to some extent, renal cancer. This study highlights the necessity of combining different omics levels to obtain a better description of cancer characteristics.

Metabolomic changes in animal models of depression: a systematic analysis

Extensive research has been carried out on the metabolomic changes in animal models of depression; however, there is no general agreement about which metabolites exhibit constant changes. Therefore, the aim of this study was to identify consistently altered metabolites in large-scale metabolomics studies of depression models. We performed vote counting analyses to identify consistently upregulated or downregulated metabolites in the brain, blood, and urine of animal models of depression based on 3743 differential metabolites from 241 animal metabolomics studies. We found that serotonin, dopamine, gamma-aminobutyric acid, norepinephrine, N-acetyl-L-aspartic acid, anandamide, and tryptophan were downregulated in the brain, while kynurenine, myo-inositol, hydroxykynurenine, and the kynurenine to tryptophan ratio were upregulated. Regarding blood metabolites, tryptophan, leucine, tyrosine, valine, trimethylamine N-oxide, proline, oleamide, pyruvic acid, and serotonin were downregulated, while N-acetyl glycoprotein, corticosterone, and glutamine were upregulated. Moreover, citric acid, oxoglutaric acid, proline, tryptophan, creatine, betaine, L-dopa, palmitic acid, and pimelic acid were downregulated, and hippuric acid was upregulated in urine. We also identified consistently altered metabolites in the hippocampus, prefrontal cortex, serum, and plasma. These findings suggested that metabolomic changes in depression models are characterized by decreased neurotransmitter and increased kynurenine metabolite levels in the brain, decreased amino acid and increased corticosterone levels in blood, and imbalanced energy metabolism and microbial metabolites in urine. This study contributes to existing knowledge of metabolomic changes in depression and revealed that the reproducibility of candidate metabolites was inadequate in previous studies.

An integrated meta-analysis of peripheral blood metabolites and biological functions in major depressive disorder

Major depressive disorder (MDD) is a serious mental illness, characterized by high morbidity, which has increased in recent decades. However, the molecular mechanisms underlying MDD remain unclear. Previous studies have identified altered metabolic profiles in peripheral tissues associated with MDD. Using curated metabolic characterization data from a large sample of MDD patients, we meta-analyzed the results of metabolites in peripheral blood. Pathway and network analyses were then performed to elucidate the biological themes within these altered metabolites. We identified 23 differentially expressed metabolites between MDD patients and controls from 46 studies. MDD patients were characterized by higher levels of asymmetric dimethylarginine, tyramine, 2-hydroxybutyric acid, phosphatidylcholine (32:1), and taurochenodesoxycholic acid and lower levels of L-acetylcarnitine, creatinine, L-asparagine, L-glutamine, linoleic acid, pyruvic acid, palmitoleic acid, L-serine, oleic acid, myo-inositol, dodecanoic acid, L-methionine, hypoxanthine, palmitic acid, L-tryptophan, kynurenic acid, taurine, and 25-hydroxyvitamin D compared with controls. L-tryptophan and kynurenic acid were consistently downregulated in MDD patients, regardless of antidepressant exposure. Depression rating scores were negatively associated with decreased levels of L-tryptophan. Pathway and network analyses revealed altered amino acid metabolism and lipid metabolism, especially for the tryptophan-kynurenine pathway and fatty acid metabolism, in the peripheral system of MDD patients. Taken together, our integrated results revealed that metabolic changes in the peripheral blood were associated with MDD, particularly decreased L-tryptophan and kynurenic acid levels, and alterations in the tryptophan-kynurenine and fatty acid metabolism pathways. Our findings may facilitate biomarker development and the elucidation of the molecular mechanisms that underly MDD.

Prostate cancer castrate resistant progression usage of non-canonical androgen receptor signaling and ketone body fuel

Prostate cancer (PCa) that progresses after androgen deprivation therapy (ADT) remains incurable. The underlying mechanisms that account for the ultimate emergence of resistance to ADT, progressing to castrate-resistant prostate cancer (CRPC), include those that reactivate androgen receptor (AR), or those that are entirely independent or cooperate with androgen signaling to underlie PCa progression. The intricacy of metabolic pathways associated with PCa progression spurred us to develop a metabolism-centric analysis to assess the metabolic shift occurring in PCa that progresses with low AR expression. We used PCa patient-derived xenografts (PDXs) to assess the metabolic changes after castration of tumor-bearing mice and subsequently confirmed main findings in human donor tumor that progressed after ADT. We found that relapsed tumors had a significant increase in fatty acids and ketone body (KB) content compared with baseline. We confirmed that critical ketolytic enzymes (ACAT1, OXCT1, BDH1) were dysregulated after castrate-resistant progression. Further, these enzymes are increased in the human donor tissue after progressing to ADT. In an in silico approach, increased ACAT1, OXCT1, BDH1 expression was also observed for a subset of PCa patients that relapsed with low AR and ERG (ETS-related gene) expression. Further, expression of these factors was also associated with decreased time to biochemical relapse and decreased progression-free survival. Our studies reveal the key metabolites fueling castration resistant progression in the context of a partial or complete loss of AR dependence.

Identification of metabolic biomarkers to predict treatment outcome and disease progression in multiple myeloma

The relationship between metabolites and multiple myeloma (MM) is becoming a research focus in the field. In this study, we performed metabolic profiling of multiple myeloma and identified potential metabolites associated with clinical characteristics, therapeutic efficacy, and prognosis of the disease. Fifty-five patients with newly-diagnosed multiple myeloma and thirty-seven healthy controls from August 2016 to October 2017 were randomly collected. The serum metabolic profiling was investigated by gas chromatography-mass spectrometry (GC-MS) technique and underwent statistical analysis. Twenty-seven metabolites were found to be significantly different between healthy controls and multiple myeloma patients. Eleven metabolites were significantly elevated, while sixteen metabolites were decreased in the multiple myeloma population. Metabolic changes were also observed in patients with renal impairment and bone destruction. Levels of urea were significantly decreased after treatment while levels of hypotaurine showed significant increase in the good-effect group (P<0.05), but not in the no-good-effect group (P>0.05). In multivariate statistical analyses, high cysteine and high hypotaurine are independent risk factors for poor treatment outcome. After adjustment for critical clinical characteristics, patients with high levels of glycolic acid and xylitol were found to be less likely to experience disease progression. Multiple myeloma demonstrates different metabolic characteristics compared with the healthy population. Among multiple myeloma patients, renal impairment and bone destruction showed additional metabolic characteristics. Cysteine and hypotaurine have value in predicting the treatment outcome, while glycolic acid and xylitol may be important prognostic factors for multiple myeloma.

Therapeutic Targeting of Mitochondrial One-Carbon Metabolism in Cancer

One-carbon (1C) metabolism encompasses folate-mediated 1C transfer reactions and related processes, including nucleotide and amino acid biosynthesis, antioxidant regeneration, and epigenetic regulation. 1C pathways are compartmentalized in the cytosol, mitochondria, and nucleus. 1C metabolism in the cytosol has been an important therapeutic target for cancer since the inception of modern chemotherapy, and "antifolates" targeting cytosolic 1C pathways continue to be a mainstay of the chemotherapy armamentarium for cancer. Recent insights into the complexities of 1C metabolism in cancer cells, including the critical role of the mitochondrial 1C pathway as a source of 1C units, glycine, reducing equivalents, and ATP, have spurred the discovery of novel compounds that target these reactions, with particular focus on 5,10-methylene tetrahydrofolate dehydrogenase 2 and serine hydroxymethyltransferase 2. In this review, we discuss key aspects of 1C metabolism, with emphasis on the importance of mitochondrial 1C metabolism to metabolic homeostasis, its relationship with the oncogenic phenotype, and its therapeutic potential for cancer.

The complexity of the serine glycine one-carbon pathway in cancer

Perturbations in cellular metabolism are ubiquitous in cancer. Here Reina-Campos et al. review the role of one-carbon metabolism in tumorigenesis.

The serine glycine and one-carbon pathway (SGOCP) is a crucially important metabolic network for tumorigenesis, of unanticipated complexity, and with implications in the clinic. Solving how this network is regulated is key to understanding the underlying mechanisms of tumor heterogeneity and therapy resistance. Here, we review its role in cancer by focusing on key enzymes with tumor-promoting functions and important products of the SGOCP that are of physiological relevance for tumorigenesis. We discuss the regulatory mechanisms that coordinate the metabolic flux through the SGOCP and their deregulation, as well as how the actions of this metabolic network affect other cells in the tumor microenvironment, including endothelial and immune cells.

Defining a metabolic landscape of tumours: genome meets metabolism

Cancer is a complex disease of multiple alterations occuring at the epigenomic, genomic, transcriptomic, proteomic and/or metabolic levels. The contribution of genetic mutations in cancer initiation, progression and evolution is well understood. However, although metabolic changes in cancer have long been acknowledged and considered a plausible therapeutic target, the crosstalk between genetic and metabolic alterations throughout cancer types is not clearly defined. In this review, we summarise the present understanding of the interactions between genetic drivers of cellular transformation and cancer-associated metabolic changes, and how these interactions contribute to metabolic heterogeneity of tumours. We discuss the essential question of whether changes in metabolism are a cause or a consequence in the formation of cancer. We highlight two modes of how metabolism contributes to tumour formation. One is when metabolic reprogramming occurs downstream of oncogenic mutations in signalling pathways and supports tumorigenesis. The other is where metabolic reprogramming initiates transformation being either downstream of mutations in oncometabolite genes or induced by chronic wounding, inflammation, oxygen stress or metabolic diseases. Finally, we focus on the factors that can contribute to metabolic heterogeneity in tumours, including genetic heterogeneity, immunomodulatory factors and tissue architecture. We believe that an in-depth understanding of cancer metabolic reprogramming, and the role of metabolic dysregulation in tumour initiation and progression, can help identify cellular vulnerabilities that can be exploited for therapeutic use.

The Immune Consequences of Lactate in the Tumor Microenvironment

The tumor microenvironment consists of complex and dynamic networks of cytokines, growth factors, and metabolic products. These contribute to significant alterations in tissue architecture, cell growth, immune cell phenotype, and function. Increased glycolytic flux is commonly observed in solid tumors and is associated with significant changes in metabolites, generating high levels of lactate. While elevated glycolytic flux is a characteristic metabolic adaption of tumor cells, glycolysis is also a key metabolic program utilized by a variety of inflammatory immune cells. As such lactate and the pH changes associated with lactate transport affect not only tumor cells but also immune cells. Here we provide an overview of lactate metabolic pathways and the effects lactate has on tumor growth and immune cell function. This knowledge provides opportunities for synergistic therapeutic approaches that combine metabolic drugs, which limit tumor growth and support immune cell function, together with immunotherapies to enhance tumor eradication.

The Metabolic Profile of Tumor and Virally Infected Cells Shapes Their Microenvironment Counteracting T Cell Immunity

Upon activation naïve T cells undergo metabolic changes to support the differentiation into subsets of effector or regulatory cells, and enable subsequent metabolic adaptations to form memory. Interfering with these metabolic alterations leads to abrogation or reprogramming of T cell differentiation, demonstrating the importance of these pathways in T cell development. It has long been appreciated that the conversion of a healthy cell to a cancerous cell is accompanied by metabolic changes, which support uncontrolled proliferation. Especially in solid tumors these metabolic changes significantly influence the tumor microenvironment (TME) and affect tumor infiltrating immune cells. The TME is often hypoxic and nutrient depleted, additionally tumor cells produce co-inhibitory signals, together suppressing the immune response. Interestingly, viruses can stimulate a metabolism akin to that seen in tumor cells in their host cells and even in neighboring cells (e.g., via transfer of virally modified extracellular vesicles). Thus, viruses create their own niche which favors viral persistence and propagation, while again keeping the immune response at bay. In this review we will focus on the mechanisms employed by tumor cells and viruses influencing T cell metabolic regulation and the impact they have on shaping T cell fate.

Mechanisms and Implications of Metabolic Heterogeneity in Cancer

Tumors display reprogrammed metabolic activities that promote cancer progression. We currently possess a limited understanding of the processes governing tumor metabolism in vivo and of the most efficient approaches to identify metabolic vulnerabilities susceptible to therapeutic targeting. While much of the literature focuses on stereotyped, cell-autonomous pathways like glycolysis, recent work emphasizes heterogeneity and flexibility of metabolism between tumors and even within distinct regions of solid tumors. Metabolic heterogeneity is important because it influences therapeutic vulnerabilities and may predict clinical outcomes. This Review describes current concepts about metabolic regulation in tumors, focusing on processes intrinsic to cancer cells and on factors imposed upon cancer cells by the tumor microenvironment. We discuss experimental approaches to identify subtype-selective metabolic vulnerabilities in preclinical cancer models. Finally, we describe efforts to characterize metabolism in primary human tumors, which should produce new insights into metabolic heterogeneity in the context of clinically relevant microenvironments.

MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis

We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.

Tissue amino acid profiles are characteristic of tumor type, malignant phenotype, and tumor progression in pancreatic tumors

Tissue amino acid profiles depend on the cell types and extracellular components that constitute the tissue, and their functions and activities. We aimed to characterize the tissue amino acid profiles in several types of pancreatic tumors and lesions. We examined tissue amino acid profiles in 311 patients with pancreatic tumors or lesions. We used newly developed LC-MS/MS methods to obtain the profiles, which were compared with clinicopathological data. Each tumor or lesion presented a characteristic tissue amino acid profile. Certain amino acids were markedly altered during the multistep pancreatic carcinogenesis and pancreatic ductal adenocarcinoma (PDAC) progression. A tissue amino acid index (TAAI) was developed based on the amino acids that were notably changed during both carcinogenesis and cancer progression. Univariate and multivariate survival analyses revealed that PDAC patients with a high TAAI exhibited a significantly shorter survival rate, and these findings were validated using a second cohort. We suggest that tissue amino acid profiles are characteristic for normal tissue type, tumor histological type, and pathological lesion, and are representative of the cancer grade or progression stage in multistep carcinogenesis and of malignant characteristics. The TAAI could serve as an independent prognosticator for patients with PDAC.