Metabolic profiles of cancer cells

Prostate cancer-derived extracellular vesicles metabolic biomarkers: Emerging roles for diagnosis and prognosis

Prostate cancer (PCa) is a global health concern, ranking as the most common cancer among men in Western countries. Traditional diagnostic methods are invasive with adverse effects on patients. Due to the heterogeneous nature of PCa and their multifocality, tissue biopsies often yield false-negative results. To address these challenges, researchers are exploring innovative approaches, particularly in the realms of proteomics and metabolomics, to identify more reliable biomarkers and improve PCa diagnosis. Liquid biopsy (LB) has emerged as a promising non-invasive strategy for PCa early detection, biopsy selection, active surveillance for low-risk cases, and post-treatment and progression monitoring. Extracellular vesicles (EVs) are lipid-bilayer nanovesicles released by all cell types and play an important role in intercellular communication. EVs have garnered attention as a valuable biomarker resource in LB for PCa-specific biomarkers, enhancing diagnosis, prognostication, and treatment guidance. Metabolomics provides insight into the body's metabolic response to both internal and external stimuli, offering quantitative measurements of biochemical alterations. It excels at detecting non-genetic influences, aiding in the discovery of more accurate cancer biomarkers for early detection and disease progression monitoring. This review delves into the potential of EVs as a resource for LB in PCa across various clinical applications. It also explores cancer-related metabolic biomarkers, both within and outside EVs in PCa, and summarises previous metabolomic findings in PCa diagnosis and risk assessment. Finally, the article addresses the challenges and future directions in the evolving field of EV-based metabolomic analysis, offering a comprehensive overview of its potential in advancing PCa management.

Optimising Extracellular Vesicle Metabolomic Methodology for Prostate Cancer Biomarker Discovery

Conventional diagnostic tools for prostate cancer (PCa), such as prostate-specific antigen (PSA), transrectal ultrasound (TRUS), digital rectal examination (DRE), and tissue biopsy face, limitations in individual risk stratification due to invasiveness or reliability issues. Liquid biopsy is a less invasive and more accurate alternative. Metabolomic analysis of extracellular vesicles (EVs) holds a promise for detecting non-genetic alterations and biomarkers in PCa diagnosis and risk assessment. The current research gap in PCa lies in the lack of accurate biomarkers for early diagnosis and real-time monitoring of cancer progression or metastasis. Establishing a suitable approach for observing dynamic EV metabolic alterations that often occur earlier than being detectable by other omics technologies makes metabolomics valuable for early diagnosis and monitoring of PCa. Using four distinct metabolite extraction approaches, the metabolite cargo of PC3-derived large extracellular vesicles (lEVs) was evaluated using a combination of methanol, cell shearing using microbeads, and size exclusion filtration, as well as two fractionation chemistries (pHILIC and C18 chromatography) that are also examined. The unfiltered methanol-microbeads approach (MB-UF), followed by pHILIC LC-MS/MS for EV metabolite extraction and analysis, is effective. Identified metabolites such as L-glutamic acid, pyruvic acid, lactic acid, and methylmalonic acid have important links to PCa and are discussed. Our study, for the first time, has comprehensively evaluated the extraction and separation methods with a view to downstream sample integrity across omics platforms, and it presents an optimised protocol for EV metabolomics in PCa biomarker discovery.

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

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

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.

Metabolic Fingerprint in Childhood Acute Lymphoblastic Leukemia

INTRODUCTION

Acute lymphoblastic leukemia (ALL) is the most prevalent childhood malignancy. Despite high cure rates, several questions remain regarding predisposition, response to treatment, and prognosis of the disease. The role of intermediary metabolism in the individualized mechanistic pathways of the disease is unclear. We have hypothesized that children with any (sub)type of ALL have a distinct metabolomic fingerprint at diagnosis when compared: (i) to a control group; (ii) to children with a different (sub)type of ALL; (iii) to the end of the induction treatment.

MATERIALS AND METHODS

In this prospective case-control study (NCT03035344), plasma and urinary metabolites were analyzed in 34 children with ALL before the beginning (D0) and at the end of the induction treatment (D33). Their metabolic fingerprint was defined by targeted analysis of 106 metabolites and compared to that of an equal number of matched controls. Multivariate and univariate statistical analyses were performed using SIMCAP and scripts under the R programming language.

RESULTS

Metabolomic analysis showed distinct changes in patients with ALL compared to controls on both D0 and D33. The metabolomic fingerprint within the patient group differed significantly between common B-ALL and pre-B ALL and between D0 and D33, reflecting the effect of treatment. We have further identified the major components of this metabolic dysregulation, indicating shifts in fatty acid synthesis, transfer and oxidation, in amino acid and glycerophospholipid metabolism, and in the glutaminolysis/TCA cycle.

CONCLUSIONS

The disease type and time point-specific metabolic alterations observed in pediatric ALL are of particular interest as they may offer potential for the discovery of new prognostic biomarkers and therapeutic targets.

Cooperative STAT3-NFkB signaling modulates mitochondrial dysfunction and metabolic profiling in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) continues to pose a significant health challenge and is often diagnosed at advanced stages. Metabolic reprogramming is a hallmark of many cancer types, including HCC and it involves alterations in various metabolic or nutrient-sensing pathways within liver cells to facilitate the rapid growth and progression of tumours. However, the role of STAT3-NFκB in metabolic reprogramming is still not clear.

APPROACH AND RESULTS

Diethylnitrosamine (DEN) administered animals showed decreased body weight and elevated level of serum enzymes. Also, Transmission electron microscopy (TEM) analysis revealed ultrastructural alterations. Increased phosphorylated signal transducer and activator of transcription-3 (p-STAT3), phosphorylated nuclear factor kappa B (p-NFκβ), dynamin related protein 1 (Drp-1) and alpha-fetoprotein (AFP) expression enhance the carcinogenicity as revealed in immunohistochemistry (IHC). The enzyme-linked immunosorbent assay (ELISA) concentration of IL-6 was found to be elevated in time dependent manner both in blood serum and liver tissue. Moreover, immunoblot analysis showed increased level of p-STAT3, p-NFκβ and IL-6 stimulated the upregulation of mitophagy proteins such as Drp-1, Phosphatase and tensin homolog (PTEN)-induced putative kinase 1 (PINK-1). Meanwhile, downregulation of Poly [ADP-ribose] polymerase 1 (PARP-1) and cleaved caspase 3 suppresses apoptosis and enhanced expression of AFP supports tumorigenesis. The mRNA level of STAT3 and Drp-1 was also found to be significantly increased. Furthermore, we performed high-field 800 MHz Nuclear Magnetic Resonance (NMR) based tissue and serum metabolomics analysis to identify metabolic signatures associated with the progression of liver cancer. The metabolomics findings revealed aberrant metabolic alterations in liver tissue and serum of 75th and 105th days of intervention groups in comparison to control, 15th and 45th days of intervention groups. Tissue metabolomics analysis revealed the accumulation of succinate in the liver tissue samples, whereas, serum metabolomics analysis revealed significantly decreased circulatory levels of ketone bodies (such as 3-hydroxybutyrate, acetate, acetone, etc.) and membrane metabolites suggesting activated ketolysis in advanced stages of liver cancer.

CONCLUSION

STAT3-NFκβ signaling axis has a significant role in mitochondrial dysfunction and metabolic alterations in the development of HCC.

Evaluating the causal relationship between human blood metabolites and gastroesophageal reflux disease

BACKGROUND

Gastroesophageal reflux disease (GERD) affects approximately 13% of the global population. However, the pathogenesis of GERD has not been fully elucidated. The development of metabolomics as a branch of systems biology in recent years has opened up new avenues for the investigation of disease processes. As a powerful statistical tool, Mendelian randomization (MR) is widely used to explore the causal relationship between exposure and outcome.

AIM

To analyze of the relationship between 486 blood metabolites and GERD.

METHODS

Two-sample MR analysis was used to assess the causal relationship between blood metabolites and GERD. A genome-wide association study (GWAS) of 486 metabolites was the exposure, and two different GWAS datasets of GERD were used as endpoints for the base analysis and replication and meta-analysis. Bonferroni correction is used to determine causal correlation features (P < 1.03 × 10-4). The results were subjected to sensitivity analysis to assess heterogeneity and pleiotropy. Using the MR Steiger filtration method to detect whether there is a reverse causal relationship between metabolites and GERD. In addition, metabolic pathway analysis was conducted using the online database based MetaboAnalyst 5.0 software.

RESULTS

In MR analysis, four blood metabolites are negatively correlated with GERD: Levulinate (4-oxovalerate), stearate (18:0), adrenate (22:4n6) and p-acetamidophenylglucuronide. However, we also found a positive correlation between four blood metabolites and GERD: Kynurenine, 1-linoleoylglycerophosphoethanolamine, butyrylcarnitine and guanosine. And bonferroni correction showed that butyrylcarnitine (odd ratio 1.10, 95% confidence interval: 1.05-1.16, P = 7.71 × 10-5) was the most reliable causal metabolite. In addition, one significant pathways, the "glycerophospholipid metabolism" pathway, can be involved in the pathogenesis of GERD.

CONCLUSION

Our study found through the integration of genomics and metabolomics that butyrylcarnitine may be a potential biomarker for GERD, which will help further elucidate the pathogenesis of GERD and better guide its treatment. At the same time, this also contributes to early screening and prevention of GERD. However, the results of this study require further confirmation from both basic and clinical real-world studies.

Fingerprinting and profiling in metabolomics of biosamples

This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.

Effectiveness of Treatments That Alter Metabolomics in Cancer Patients-A Systematic Review

INTRODUCTION

Cancer is the leading cause of death worldwide, with the most frequent being breast cancer in women, prostate cancer in men and colon cancer in both sexes. The use of metabolomics to find new biomarkers can provide knowledge about possible interventions based on the presence of oncometabolites in different cancer types.

OBJECTIVES

The primary purpose of this review is to analyze the characteristic metabolome of three of the most frequent cancer types. We further want to identify the existence and success rate of metabolomics-based intervention in patients suffering from those cancer types. Our conclusions are based on the analysis of the methodological quality of the studies.

METHODS

We searched for studies that investigated the metabolomic characteristics in patients suffering from breast cancer, prostate cancer or colon cancer in clinical trials. The data were analyzed, as well as the effects of specific interventions based on identified metabolomics and one or more oncometabolites. The used databases were PubMed, Virtual Health Library, Web of Science, EBSCO and Cochrane Library. Only nine studies met the selection criteria. Study bias was analyzed using the Cochrane risk of bias tool. This systematic review protocol was registered at the International Prospective Register of Systematic Reviews (PROSPERO: CRD42023401474).

RESULTS

Only nine studies about clinical trials were included in this review and show a moderate quality of evidence. Metabolomics-based interventions related with disease outcome were conflictive with no or small changes in the metabolic characteristics of the different cancer types.

CONCLUSIONS

This systematic review shows some interesting results related with metabolomics-based interventions and their effects on changes in certain cancer oncometabolites. The small number of studies we identified which fulfilled our inclusion criteria in this systematic review does not allow us to draw definitive conclusions. Nevertheless, some results can be considered as promising although further research is needed. That research must focus not only on the presence of possible oncometabolites but also on possible metabolomics-based interventions and their influence on the outcome in patients suffering from breast cancer, prostate cancer or colon cancer.

Advances in proteomic phenotyping of microglia in neurodegeneration

Microglia are dynamic resident immune cells of the central nervous system (CNS) that sense, survey, and respond to changes in their environment. In disease states, microglia transform from homeostatic to diverse molecular phenotypic states that play complex and causal roles in neurologic disease pathogenesis, as evidenced by the identification of microglial genes as genetic risk factors for neurodegenerative disease. While advances in transcriptomic profiling of microglia from the CNS of humans and animal models have provided transformative insights, the transcriptome is only modestly reflective of the proteome. Proteomic profiling of microglia is therefore more likely to provide functionally and therapeutically relevant targets. In this review, we discuss molecular insights gained from transcriptomic studies of microglia in the context of Alzheimer's disease as a prototypic neurodegenerative disease, and highlight existing and emerging approaches for proteomic profiling of microglia derived from in vivo model systems and human brain.

A pilot study: Metabolic profiling of plasma and saliva samples from newly diagnosed glioblastoma patients

BACKGROUND

Despite aggressive treatment, more than 90% of glioblastoma (GBM) patients experience recurrences. GBM response to therapy is currently assessed by imaging techniques and tissue biopsy. However, difficulties with these methods may cause misinterpretation of treatment outcomes. Currently, no validated therapy response biomarkers are available for monitoring GBM progression. Metabolomics holds potential as a complementary tool to improve the interpretation of therapy responses to help in clinical interventions for GBM patients.

METHODS

Saliva and blood from GBM patients were collected pre and postoperatively. Patients were stratified conforming their progression-free survival (PFS) into favourable or unfavourable clinical outcomes (>9 months or PFS ≤ 9 months, respectively). Analysis of saliva (whole-mouth and oral rinse) and plasma samples was conducted utilising LC-QqQ-MS and LC-QTOF-MS to determine the metabolomic and lipidomic profiles. The data were investigated using univariate and multivariate statistical analyses and graphical LASSO-based graphic network analyses.

RESULTS

Altogether, 151 metabolites and 197 lipids were detected within all saliva and plasma samples. Among the patients with unfavourable outcomes, metabolites such as cyclic-AMP, 3-hydroxy-kynurenine, dihydroorotate, UDP and cis-aconitate were elevated, compared to patients with favourable outcomes during pre-and post-surgery. These metabolites showed to impact the pentose phosphate and Warburg effect pathways. The lipid profile of patients who experienced unfavourable outcomes revealed a higher heterogeneity in the abundance of lipids and fewer associations between markers in contrast to the favourable outcome group.

CONCLUSION

Our findings indicate that changes in salivary and plasma metabolites in GBM patients can potentially be employed as less invasive prognostic biomarkers/biomarker panel but validation with larger cohorts is required.

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.

Status of Metabolomic Measurement for Insights in Alzheimer's Disease Progression-What Is Missing?

Alzheimer's disease (AD) is an aging-related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Currently, the pathogenesis and etiology of AD remain poorly understood, while no efficient treatments are available to slow down the degenerative effects of AD. Metabolomics allows the study of biochemical alterations in pathological processes which may be involved in AD progression and to discover new therapeutic targets. In this review, we summarized and analyzed the results from studies on metabolomics analysis performed in biological samples of AD subjects and AD animal models. Then this information was analyzed by using MetaboAnalyst to find the disturbed pathways among different sample types in human and animal models at different disease stages. We discuss the underlying biochemical mechanisms involved, and the extent to which they could impact the specific hallmarks of AD. Then we identify gaps and challenges and provide recommendations for future metabolomics approaches to better understand AD pathogenesis.

Direct mass spectrometry analysis of biological tissue for diagnosis of thyroid cancer using wooden-tip electrospray ionization

Direct mass spectrometry (MS) analysis of human tissue at the molecular level could gain insight into biomarker discovery and disease diagnosis. Detecting metabolite profiles of tissue sample play an important role in understanding the pathological properties of disease development. Because the complex matrices in tissue samples, complicated and time-consuming sample preparation processes are usually required by conventional biological and clinical MS methods. Direct MS with ambient ionization technique is a new analytical strategy for direct sample analysis with little sample preparation, and has been proven to be a simple, rapid, and effective analytical tools for direct analysis of biological tissues. In this work, we applied a simple, low-cost, disposable wooden tip (WT) for loading tiny thyroid tissue, and then loading organic solvents to extract biomarkers under electrospray ionization (ESI) condition. Under such WT-ESI, the extract of thyroid was directly sprayed out from wooden tip to MS inlet. In this work, thyroid tissue from normal and cancer parts were analyzed by the established WT-ESI-MS, showing lipids were mainly detectable compounds in thyroid tissue. The MS data of lipids obtained from thyroid tissues were further analyzed with MS/MS experiment and multivariate variable analysis, and the biomarkers of thyroid cancer were also investigated.

Metabolic rewiring in MYC-driven medulloblastoma by BET-bromodomain inhibition

Medulloblastoma (MB) is the most common malignant brain tumour in children. High-risk MB patients harbouring MYC amplification or overexpression exhibit a very poor prognosis. Aberrant activation of MYC markedly reprograms cell metabolism to sustain tumorigenesis, yet how metabolism is dysregulated in MYC-driven MB is not well understood. Growing evidence unveiled the potential of BET-bromodomain inhibitors (BETis) as next generation agents for treating MYC-driven MB, but whether and how BETis may affect tumour cell metabolism to exert their anticancer activities remains unknown. In this study, we explore the metabolic features characterising MYC-driven MB and examine how these are altered by BET-bromodomain inhibition. To this end, we employed an NMR-based metabolomics approach applied to the MYC-driven MB D283 and D458 cell lines before and after the treatment with the BETi OTX-015. We found that OTX-015 triggers a metabolic shift in both cell lines resulting in increased levels of myo-inositol, glycerophosphocholine, UDP-N-acetylglucosamine, glycine, serine, pantothenate and phosphocholine. Moreover, we show that OTX-015 alters ascorbate and aldarate metabolism, inositol phosphate metabolism, phosphatidylinositol signalling system, glycerophospholipid metabolism, ether lipid metabolism, aminoacyl-tRNA biosynthesis, and glycine, serine and threonine metabolism pathways in both cell lines. These insights provide a metabolic characterisation of MYC-driven childhood MB cell lines, which could pave the way for the discovery of novel druggable pathways. Importantly, these findings will also contribute to understand the downstream effects of BETis on MYC-driven MB, potentially aiding the development of new therapeutic strategies to combat medulloblastoma.

PRMT3 regulates the progression of invasive micropapillary carcinoma of the breast

Invasive micropapillary carcinoma (IMPC) is a special histopathological subtype of breast cancer. Clinically, IMPC exhibits a higher incidence of lymphovascular invasion and lymph node metastasis compared with that of invasive ductal carcinoma (IDC), the most common type. However, the metabolic characteristics and related mechanisms underlying malignant IMPC biological behaviors are unknown. We performed large-scale targeted metabolomics analysis on resected tumors obtained from chemotherapy-naïve IMPC (n = 25) and IDC (n = 26) patients to investigate metabolic alterations, and we integrated mass spectrometry analysis, RNA sequencing, and ChIP-sequencing data to elucidate the potential molecular mechanisms. The metabolomics revealed distinct metabolic profiles between IMPC and IDC. For IMPC patients, the metabolomic profile was characterized by significantly high levels of arginine methylation marks, and protein arginine methyltransferase 3 (PRMT3) was identified as a critical regulator that catalyzed the formation of these arginine methylation marks. Notably, overexpression of PRMT3 was an independent risk factor for poor IMPC prognosis. Furthermore, we demonstrated that PRMT3 was a key regulator of breast cancer cell proliferation and metastasis both in vitro and in vivo, and treatment with a preclinical PRMT3 inhibitor decreased the xenograft tumorigenic capacity. Mechanistically, PRMT3 regulated the endoplasmic reticulum (ER) stress signaling pathway by facilitating histone H4 arginine 3 asymmetric dimethylation (H4R3me2a), which may endow breast cancer cells with great proliferative and metastatic capacity. Our findings highlight PRMT3 importance in regulating the malignant biological behavior of IMPC and suggest that small-molecule inhibitors of PRMT3 activity might be promising breast cancer treatments.

Skin Cancer Metabolic Profile Assessed by Different Analytical Platforms

Skin cancer, including malignant melanoma (MM) and keratinocyte carcinoma (KC), historically named non-melanoma skin cancers (NMSC), represents the most common type of cancer among the white skin population. Despite decades of clinical research, the incidence rate of melanoma is increasing globally. Therefore, a better understanding of disease pathogenesis and resistance mechanisms is considered vital to accomplish early diagnosis and satisfactory control. The "Omics" field has recently gained attention, as it can help in identifying and exploring metabolites and metabolic pathways that assist cancer cells in proliferation, which can be further utilized to improve the diagnosis and treatment of skin cancer. Although skin tissues contain diverse metabolic enzymes, it remains challenging to fully characterize these metabolites. Metabolomics is a powerful omics technique that allows us to measure and compare a vast array of metabolites in a biological sample. This technology enables us to study the dermal metabolic effects and get a clear explanation of the pathogenesis of skin diseases. The purpose of this literature review is to illustrate how metabolomics technology can be used to evaluate the metabolic profile of human skin cancer, using a variety of analytical platforms including gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and nuclear magnetic resonance (NMR). Data collection has not been based on any analytical method.

When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective

Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.

Metabonomic analysis of tumor microenvironments: a mini-review

Metabolomic analysis is a vital part of studying cancer progression. Metabonomic crosstalk, such as nutrient availability, physicochemical transformation, and intercellular interactions can affect tumor metabolism. Many original studies have demonstrated that metabolomics is important in some aspects of tumor metabolism. In this mini-review, we summarize the definition of metabolomics and how it can help change a tumor microenvironment, especially in pathways of three metabonomic tumors. Just as non-invasive biofluids have been identified as early biomarkers of tumor development, metabolomics can also predict differences in tumor drug response, drug resistance, and efficacy. Therefore, metabolomics is important for tumor metabolism and how it can affect oncology drugs in cancer therapy.

Application of Metabolomics in Childhood Leukemia Diagnostics

Metabolomics is a new field of science dealing with the study and analysis of metabolites formed in living cells. The biological fluids used in this test method are: blood, blood plasma, serum, cerebrospinal fluid, saliva and urine. The most popular methods of assessing the composition of metabolites include nuclear magnetic resonance spectroscopy and mass spectrometry (MS) in combination with gas chromatography–MS or liquid chromatography–MS. Metabolomics is used in many areas of medicine. The variability of biochemical processes in neoplastic cells in relation to healthy cells is the starting point for this type of research. The aim of the research currently being carried out is primarily to find biomarkers for quick diagnosis of the disease, assessment of its advancement and treatment effectiveness. The development of metabolomics may also contribute to the individualization of treatment of patients, adjusting drugs depending on the metabolic profile, and thus may improve the effectiveness of therapy, reduce side effects and help to improve the quality of life of patients. Here, we review the current and potential applications of metabolomics, focusing on its use as a biomarker method for childhood leukemia.

Graphic abstract

Structure-Guided Approach to Discover Tuberosin as a Potent Activator of Pyruvate Kinase M2, Targeting Cancer Therapy

Metabolic reprogramming is a key attribute of cancer progression. An altered expression of pyruvate kinase M2 (PKM2), a phosphotyrosine-binding protein is observed in many human cancers. PKM2 plays a vital role in metabolic reprogramming, transcription and cell cycle progression and thus is deliberated as an attractive target in anticancer drug development. The expression of PKM2 is essential for aerobic glycolysis and cell proliferation, especially in cancer cells, facilitating selective targeting of PKM2 in cell metabolism for cancer therapeutics. We have screened a virtual library of phytochemicals from the IMPPAT (Indian Medicinal Plants, Phytochemistry and Therapeutics) database of Indian medicinal plants to identify potential activators of PKM2. The initial screening was carried out for the physicochemical properties of the compounds, and then structure-based molecular docking was performed to select compounds based on their binding affinity towards PKM2. Subsequently, the ADMET (absorption, distribution, metabolism, excretion and toxicity) properties, PAINS (Pan-assay interference compounds) patterns, and PASS evaluation were carried out to find more potent hits against PKM2. Here, Tuberosin was identified from the screening process bearing appreciable binding affinity toward the PKM2-binding pocket and showed a worthy set of drug-like properties. Finally, molecular dynamics simulation for 100 ns was performed, which showed decent stability of the protein-ligand complex and relatival conformational dynamics throughout the trajectory. The study suggests that modulating PKM2 with natural compounds is an attractive approach in treating human malignancy after required validation.

Characterization of lipomatous tumors with high-resolution 1H MRS at 17.6T: Do benign lipomas, atypical lipomatous tumors and liposarcomas have a distinct metabolic signature?

Background

Distinguishing between some benign lipomas (BLs), atypical lipomatous tumors (ALTs), and dedifferentiated liposarcomas (DDLs) can be challenging due to overlapping magnetic resonance imaging characteristics, and poorly understood molecular mechanisms underlying the malignant transformation of liposarcomas.

Purpose

To identify metabolic biomarkers of the lipomatous tumor spectrum by examining human tissue specimens using high-resolution ¹H magnetic resonance spectroscopy (MRS).

Materials and methods

In this prospective study, human tissue specimens were obtained from participants who underwent surgical resection for radiologically-indeterminate lipomatous tumors between November 2016 and May 2019. Tissue specimens were obtained from normal subcutaneous fat (n=9), BLs (n=10), ALTs (n=7) and DDLs (n=8). Extracts from specimens were examined with high-resolution MRS at 17.6T. Computational modeling of pattern recognition-based cluster analysis was utilized to identify significant differences in metabolic signatures between the lipomatous tumor types.

Results

Significant differences between BLs and ALTs were observed for multiple metabolites, including leucine, valine, branched chain amino acids, alanine, acetate, glutamine, and formate. DDLs were distinguished from ALTs by increased glucose and lactate, and increased phosphatidylcholine. Multivariate principal component analysis showed clear clustering identifying distinct metabolic signatures of the tissue types.

Conclusion

Metabolic signatures identified in ¹H MR spectra of lipomatous tumors provide new insights into malignant progression and metabolic targeting. The metabolic patterns identified provide the foundation of developing noninvasive MRS or PET imaging biomarkers to distinguish between BLs, ALTs, and DDLs.

Metabolomics for exposure assessment and toxicity effects of occupational pollutants: current status and future perspectives

INTRODUCTION

Work-related exposures to harmful agents or factors are associated with an increase in incidence of occupational diseases. These exposures often represent a complex mixture of different stressors, challenging the ability to delineate the mechanisms and risk factors underlying exposure-disease relationships. The use of omics measurement approaches that enable characterization of biological marker patterns provide internal indicators of molecular alterations, which could be used to identify bioeffects following exposure to a toxicant. Metabolomics is the comprehensive analysis of small molecule present in biological samples, and allows identification of potential modes of action and altered pathways by systematic measurement of metabolites.

OBJECTIVES

The aim of this study is to review the application of metabolomics studies for use in occupational health, with a focus on applying metabolomics for exposure monitoring and its relationship to occupational diseases.

METHODS

PubMed, Web of Science, Embase and Scopus electronic databases were systematically searched for relevant studies published up to 2021.

RESULTS

Most of reviewed studies included worker populations exposed to heavy metals such as As, Cd, Pb, Cr, Ni, Mn and organic compounds such as tetrachlorodibenzo-p-dioxin, trichloroethylene, polyfluoroalkyl, acrylamide, polyvinyl chloride. Occupational exposures were associated with changes in metabolites and pathways, and provided novel insight into the relationship between exposure and disease outcomes. The reviewed studies demonstrate that metabolomics provides a powerful ability to identify metabolic phenotypes and bioeffect of occupational exposures.

CONCLUSION

Continued application to worker populations has the potential to enable characterization of thousands of chemical signals in biological samples, which could lead to discovery of new biomarkers of exposure for chemicals, identify possible toxicological mechanisms, and improved understanding of biological effects increasing disease risk associated with occupational exposure.

A Comprehensive Metabolomics Analysis of Fecal Samples from Advanced Adenoma and Colorectal Cancer Patients

Accurate diagnosis of colorectal cancer (CRC) still relies on invasive colonoscopy. Noninvasive methods are less sensitive in detecting the disease, particularly in the early stage. In the current work, a metabolomics analysis of fecal samples was carried out by ultra-high-performance liquid chromatography–tandem mass spectroscopy (UPLC-MS/MS). A total of 1380 metabolites were analyzed in a cohort of 120 fecal samples from patients with normal colonoscopy, advanced adenoma (AA) and CRC. Multivariate analysis revealed that metabolic profiles of CRC and AA patients were similar and could be clearly separated from control individuals. Among the 25 significant metabolites, sphingomyelins (SM), lactosylceramides (LacCer), secondary bile acids, polypeptides, formiminoglutamate, heme and cytidine-containing pyrimidines were found to be dysregulated in CRC patients. Supervised random forest (RF) and logistic regression algorithms were employed to build a CRC accurate predicted model consisting of the combination of hemoglobin (Hgb) and bilirubin E,E, lactosyl-N-palmitoyl-sphingosine, glycocholenate sulfate and STLVT with an accuracy, sensitivity and specificity of 91.67% (95% Confidence Interval (CI) 0.7753–0.9825), 0.7 and 1, respectively.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

NMR-based metabolomic profiling can differentiate follicular lymphoma from benign lymph node tissues and may be predictive of outcome

Follicular lymphoma (FL) is a cancer of B-cells, representing the second most common type of non-Hodgkin lymphoma and typically diagnosed at advanced stage in older adults. In contrast to the wide range of available molecular genetic data, limited data relating the metabolomic features of follicular lymphoma are known. Metabolomics is a promising analytical approach employing metabolites (molecules < 1 kDa in size) as potential biomarkers in cancer research. In this pilot study, we performed proton nuclear magnetic resonance spectroscopy (¹H-NMR) on 29 cases of FL and 11 control patient specimens. The resulting spectra were assessed by both unsupervised and supervised statistical methods. We report significantly discriminant metabolomic models of common metabolites distinguishing FL from control tissues. Within our FL case series, we also report discriminant metabolomic signatures predictive of progression-free survival.

Emerging Role for 7T MRI and Metabolic Imaging for Pancreatic and Liver Cancer

Advances in magnet technologies have led to next generation 7T magnetic resonance scanners which can fit in the footprint and price point of conventional hospital scanners (1.5−3T). It is therefore worth asking if there is a role for 7T magnetic resonance imaging and spectroscopy for the treatment of solid tumor cancers. Herein, we survey the medical literature to evaluate the unmet clinical needs for patients with pancreatic and hepatic cancer, and the potential of ultra-high field proton imaging and phosphorus spectroscopy to fulfil those needs. We draw on clinical literature, preclinical data, nuclear magnetic resonance spectroscopic data of human derived samples, and the efforts to date with 7T imaging and phosphorus spectroscopy. At 7T, the imaging capabilities approach histological resolution. The spectral and spatial resolution enhancements at high field for phospholipid spectroscopy have the potential to reduce the number of exploratory surgeries due to tumor boundaries undefined at conventional field strengths. Phosphorus metabolic imaging at 7T magnetic field strength, is already a mainstay in preclinical models for molecular phenotyping, energetic status evaluation, dosimetry, and assessing treatment response for both pancreatic and liver cancers. Metabolic imaging of primary tumors and lymph nodes may provide powerful metrics to aid staging and treatment response. As tumor tissues contain extreme levels of phospholipid metabolites compared to the background signal, even spectroscopic volumes containing less than 50% tumor can be detected and/or monitored. Phosphorus spectroscopy allows non-invasive pH measurements, indicating hypoxia, as a predictor of patients likely to recur. We conclude that 7T multiparametric approaches that include metabolic imaging with phosphorus spectroscopy have the potential to meet the unmet needs of non-invasive location-specific treatment monitoring, lymph node staging, and the reduction in unnecessary surgeries for patients undergoing resections for pancreatic cancer. There is also potential for the use of 7T phosphorous spectra for the phenotyping of tumor subtypes and even early diagnosis (<2 mL). Whether or not 7T can be used for all patients within the next decade, the technology is likely to speed up the translation of new therapeutics.

Breast cancer in the era of integrating “Omics” approaches

Worldwide, breast cancer is the leading cause of cancer-related deaths in women. Breast cancer is a heterogeneous disease characterized by different clinical outcomes in terms of pathological features, response to therapies, and long-term patient survival. Thus, the heterogeneity found in this cancer led to the concept that breast cancer is not a single disease, being very heterogeneous both at the molecular and clinical level, and rather represents a group of distinct neoplastic diseases of the breast and its cells. Indubitably, in the past decades we witnessed a significant development of innovative therapeutic approaches, including targeted and immunotherapies, leading to impressive results in terms of increased survival for breast cancer patients. However, these multimodal treatments fail to prevent recurrence and metastasis. Therefore, it is urgent to improve our understanding of breast tumor and metastasis biology. Over the past few years, high-throughput “omics” technologies through the identification of novel biomarkers and molecular profiling have shown their great potential in generating new insights in the study of breast cancer, also improving diagnosis, prognosis and prediction of response to treatment. In this review, we discuss how the implementation of “omics” strategies and their integration may lead to a better comprehension of the mechanisms underlying breast cancer. In particular, with the aim to investigate the correlation between different “omics” datasets and to define the new important key pathway and upstream regulators in breast cancer, we applied a new integrative meta-analysis method to combine the results obtained from genomics, proteomics and metabolomics approaches in different revised studies.

Metabolomic Approaches for Detection and Identification of Biomarkers and Altered Pathways in Bladder Cancer

Metabolomic analysis has proven to be a useful tool in biomarker discovery and the molecular classification of cancers. In order to find new biomarkers, and to better understand its pathological behavior, bladder cancer also has been studied using a metabolomics approach. In this article, we review the literature on metabolomic studies of bladder cancer, focusing on the different available samples (urine, blood, tissue samples) used to perform the studies and their relative findings. Moreover, the multi-omic approach in bladder cancer research has found novel insights into its metabolic behavior, providing excellent start-points for new diagnostic and therapeutic strategies. Metabolomics data analysis can lead to the discovery of a “signature pathway” associated with the progression of bladder cancer; this aspect could be potentially valuable in predictions of clinical outcomes and the introduction of new treatments. However, further studies are needed to give stronger evidence and to make these tools feasible for use in clinical practice.

Metabolic Profiling of Thymic Epithelial Tumors Hints to a Strong Warburg Effect, Glutaminolysis and Precarious Redox Homeostasis as Potential Therapeutic Targets

Simple Summary

Thymomas and thymic carcinomas (TCs) are malignant thymic epithelial tumors (TETs) with poor outcome, if non-resectable. Metabolic signatures of TETs have not yet been studied and may offer new therapeutic options. This is the first metabolomics investigation on thymic epithelial tumors employing nuclear magnetic resonance spectroscopy of tissue samples. We could detect and quantify up to 37 metabolites in the major tumor subtypes, including acetylcholine that was not previously detected in other non-endocrine cancers. A metabolite-based cluster analysis distinguished three clinically relevant tumor subgroups, namely indolent and aggressive thymomas, as well as TCs. A metabolite-based metabolic pathway analysis also gave hints to activated metabolic pathways shared between aggressive thymomas and TCs. This finding was largely backed by enrichment of these pathways at the transcriptomic level in a large, publicly available, independent TET dataset. Due to the differential expression of metabolites in thymic epithelial tumors versus normal thymus, pathways related to proline, cysteine, glutathione, lactate and glutamine appear as promising therapeutic targets. From these findings, inhibitors of glutaminolysis and of the downstream TCA cycle are anticipated to be rational therapeutic strategies. If our results can be confirmed in future, sufficiently powered studies, metabolic signatures may contribute to the identification of new therapeutic options for aggressive thymomas and TCs.

Abstract

Thymomas and thymic carcinomas (TC) are malignant thymic epithelial tumors (TETs) with poor outcome, if non-resectable. Metabolic signatures of TETs have not yet been studied and may offer new therapeutic options. Metabolic profiles of snap-frozen thymomas (WHO types A, AB, B1, B2, B3, n = 12) and TCs (n = 3) were determined by high resolution magic angle spinning 1H nuclear magnetic resonance (HRMAS 1H-NMR) spectroscopy. Metabolite-based prediction of active KEGG metabolic pathways was achieved with MetPA. In relation to metabolite-based metabolic pathways, gene expression signatures of TETs (n = 115) were investigated in the public “The Cancer Genome Atlas” (TCGA) dataset using gene set enrichment analysis. Overall, thirty-seven metabolites were quantified in TETs, including acetylcholine that was not previously detected in other non-endocrine cancers. Metabolite-based cluster analysis distinguished clinically indolent (A, AB, B1) and aggressive TETs (B2, B3, TCs). Using MetPA, six KEGG metabolic pathways were predicted to be activated, including proline/arginine, glycolysis and glutathione pathways. The activated pathways as predicted by metabolite-profiling were generally enriched transcriptionally in the independent TCGA dataset. Shared high lactic acid and glutamine levels, together with associated gene expression signatures suggested a strong “Warburg effect”, glutaminolysis and redox homeostasis as potential vulnerabilities that need validation in a large, independent cohort of aggressive TETs. If confirmed, targeting metabolic pathways may eventually prove as adjunct therapeutic options in TETs, since the metabolic features identified here are known to confer resistance to cisplatin-based chemotherapy, kinase inhibitors and immune checkpoint blockers, i.e., currently used therapies for non-resectable TETs.

Serum Metabolic Disturbances in Lung Cancer Investigated through an Elaborative NMR-Based Serum Metabolomics Approach

Detection of metabolic disturbances in lung cancer (LC) has the potential to aid early diagnosis/prognosis and hence improve disease management strategies through reliable grading, staging, and determination of neoadjuvant status in LC. However, a majority of previous metabolomics studies compare the normalized spectral features which not only provide ambiguous information but further limit the clinical translation of this information. Various such issues can be resolved by performing the concentration profiling of various metabolites with respect to formate as an internal reference using commercial software Chenomx. Continuing our efforts in this direction, the serum metabolic profiles were measured on 39 LC patients and 42 normal controls (NCs, comparable in age/sex) using high-field 800 MHz NMR spectroscopy and compared using multivariate statistical analysis tools to identify metabolic disturbances and metabolites of diagnostic potential. Partial least-squares discriminant analysis (PLS-DA) model revealed a distinct separation between LC and NC groups and resulted in excellent discriminatory ability with the area under the receiver-operating characteristic (AUROC) = 0.97 [95% CI = 0.89-1.00]. The metabolic features contributing to the differentiation of LC from NC samples were identified first using variable importance in projection (VIP) score analysis and then checked for their statistical significance (with p-value < 0.05) and diagnostic potential using the ROC curve analysis. The analysis revealed relevant metabolic disturbances associated with LC. Among various circulatory metabolites, six metabolites, including histidine, glutamine, glycine, threonine, alanine, and valine, were found to be of apposite diagnostic potential for clinical implications. These metabolic alterations indicated altered glucose metabolism, aberrant fatty acid synthesis, and augmented utilization of various amino acids including active glutaminolysis in LC.

Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis

Lung cancer is the leading cause of cancer mortality, and early detection is key to improving survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of different early-stage lung cancers and found that lipid metabolism was broadly dysregulated in different cell types, with glycerophospholipid metabolism as the most altered lipid metabolism-related pathway. Untargeted lipidomics was carried out in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum-based feature selection, we identified nine lipids (lysophosphatidylcholines 16:0, 18:0, and 20:4; phosphatidylcholines 16:0-18:1, 16:0-18:2, 18:0-18:1, 18:0-18:2, and 16:0-22:6; and triglycerides 16:0-18:1-18:1) as the features most important for early-stage cancer detection. Using these nine features, we developed a liquid chromatography-mass spectrometry (MS)-based targeted assay using multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low-dose computed tomography and a prospective clinical cohort containing 109 participants, the assay reached more than 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization MS imaging confirmed that the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. This method, designated as Lung Cancer Artificial Intelligence Detector, may be useful for early detection of lung cancer or large-scale screening of high-risk populations for cancer prevention.

Preclinical Evaluation of Dimethyl Itaconate Against Hepatocellular Carcinoma via Activation of the e/iNOS-Mediated NF-κB-Dependent Apoptotic Pathway

Hepatocellular carcinoma (HCC) is one of the most common tumors affecting a large population worldwide, with the fifth and seventh greatest mortality rates among men and women, respectively, and the third prime cause of mortality among cancer victims. Dimethyl itaconate (DI) has been reported to be efficacious in colorectal cancer by decreasing IL-1β release from intestinal epithelial cells. In this study, diethylnitrosamine (DEN)-induced HCC in male albino Wistar rats was treated with DI as an anticancer drug. The function and molecular mechanism of DI against HCC in vivo were assessed using histopathology, enzyme-linked immunosorbent assay (ELISA), and Western blot studies. Metabolomics using ¹H-NMR was used to investigate metabolic profiles. As per molecular insights, DI has the ability to trigger mitochondrial apoptosis through iNOS- and eNOS-induced activation of the NF-κB/Bcl-2 family of proteins, CytC, caspase-3, and caspase-9 signaling cascade. Serum metabolomics investigations using ¹H-NMR revealed that aberrant metabolites in DEN-induced HCC rats were restored to normal following DI therapy. Furthermore, our data revealed that the DI worked as an anti-HCC agent. The anticancer activity of DI was shown to be equivalent to that of the commercial chemotherapeutic drug 5-fluorouracil.

Heat Shock Proteins in Benign Prostatic Hyperplasia and Prostate Cancer

Two out of three diseases of the prostate gland affect aging men worldwide. Benign prostatic hyperplasia (BPH) is a noncancerous enlargement affecting millions of men. Prostate cancer (PCa) in turn is the second leading cause of cancer death. The factors influencing the occurrence of BPH and PCa are different; however, in the course of these two diseases, the overexpression of heat shock proteins is observed. Heat shock proteins (HSPs), chaperone proteins, are known to be one of the main proteins playing a role in maintaining cell homeostasis. HSPs take part in the process of the proper folding of newly formed proteins, and participate in the renaturation of damaged proteins. In addition, they are involved in the transport of specific proteins to the appropriate cell organelles and directing damaged proteins to proteasomes or lysosomes. Their function is to protect the proteins against degradation factors that are produced during cellular stress. HSPs are also involved in modulating the immune response and the process of apoptosis. One well-known factor affecting HSPs is the androgen receptor (AR)—a main player involved in the development of BPH and the progression of prostate cancer. HSPs play a cytoprotective role and determine the survival of cancer cells. These chaperones are often upregulated in malignancies and play an indispensable role in tumor progression. Therefore, HSPs are considered as one of the therapeutic targets in anti-cancer therapies. In this review article, we discuss the role of different HSPs in prostate diseases, and their potential as therapeutic targets.

A Metabonomic View on Wilms Tumor by High-Resolution Magic-Angle Spinning Nuclear Magnetic Resonance Spectroscopy

Pediatric cancer NMR-metabonomics might be a powerful tool to discover modified biochemical pathways in tumor development, improve cancer diagnosis, and, consequently, treatment. Wilms tumor (WT) is the most common kidney tumor in young children whose genetic and epigenetic abnormalities lead to cell metabolism alterations, but, so far, investigation of metabolic pathways in WT is scarce. We aimed to explore the high-resolution magic-angle spinning nuclear magnetic resonance (HR-MAS NMR) metabonomics of WT and normal kidney (NK) samples. For this study, 14 WT and 7 NK tissue samples were obtained from the same patients and analyzed. One-dimensional and two-dimensional HR-MAS NMR spectra were processed, and the one-dimensional NMR data were analyzed using chemometrics. Chemometrics enabled us to elucidate the most significant differences between the tumor and normal tissues and to discover intrinsic metabolite alterations in WT. The metabolic differences in WT tissues were revealed by a validated PLS-DA applied on HR-MAS T₂-edited ¹H-NMR and were assigned to 16 metabolites, such as lipids, glucose, and branched-chain amino acids (BCAAs), among others. The WT compared to NK samples showed 13 metabolites with increased concentrations and 3 metabolites with decreased concentrations. The relative BCAA concentrations were decreased in the WT while lipids, lactate, and glutamine/glutamate showed increased levels. Sixteen tissue metabolites distinguish the analyzed WT samples and point to altered glycolysis, glutaminolysis, TCA cycle, and lipid and BCAA metabolism in WT. Significant variation in the concentrations of metabolites, such as glutamine/glutamate, lipids, lactate, and BCAAs, was observed in WT and opened up a perspective for their further study and clinical validation.

Comparative NMR metabolomics of the responses of A2780 human ovarian cancer cells to clinically established Pt-based drugs

Pt-based drugs play a very important role in current cancer treatments; yet, their cellular and mechanistic aspects are not fully understood. NMR metabolomics provides a powerful tool to investigate the...

Two-photon excitable membrane targeting polyphenolic carbon dots for long-term imaging and pH-responsive chemotherapeutic drug delivery for synergistic tumor therapy

Long-term dynamic tracking of cells with theranostics properties remains challenging due to difficulty in preparing and delivering drugs by the probes. Herein, we developed a highly fluorescent one- and two-photon...

Integration of Transcriptomics Data and Metabolomic Data Using Biomedical Literature Mining and Pathway Analysis

Recent progress in omics technologies such as transcriptomics and metabolomics offers an unprecedented opportunity to understand the disease mechanisms and determines the associated biomedical entities using biomedical literature mining. Tremendous data available in the biomedical literature helps in addressing complex biomedical problems. Advancements in genomics and transcriptomics helps in decoding the genetic information obtained from various high throughput techniques for its use in personalized medicine and therapeutics. Integration of data from biomedical literature and data from large-scale genomic studies aids in the determination of the etiology of a disease and drug targets. This chapter addresses the perspectives of transcriptomics and metabolomics in biomedical literature mining and gives an overview of state-of-the-art techniques in this field.

Potential of nuclear magnetic resonance metabolomics in the study of prostate cancer

Nuclear magnetic resonance (NMR) metabolomics is a powerful analytical technique and a tool which has unique characteristics and capabilities for the evaluation of a number of biochemicals/metabolites of cancer and other disease processes that are present in biofluids (urine and blood) and tissues. The potential of NMR metabolomics in prostate cancer (PCa) has been explored by researchers and its usefulness has been documented. A large number of metabolites such as citrate, choline, and sarcosine were detected by NMR metabolomics from biofluids and tissues related to PCa and their levels were compared with controls and benign prostatic hyperplasia. The changes in the levels of these metabolites aid in the diagnosis and help to understand the dysregulated metabolic pathways in PCa. We review recent studies on in vitro and ex vivo NMR spectroscopy-based PCa metabolomics and its possible role as a diagnostic tool.

Metabolomics Work Flow and Analytics in Systems Biology

Metabolomics is an omics approach of systems biology that involves the development and assessment of large-scale, comprehensive biochemical analysis tools for metabolites in biological systems. This review describes the metabolomics workflow and provides an overview of current analytic tools used for the quantification of metabolic profiles. We explain analytic tools such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, ionization techniques, and approaches for data extraction and analysis.

Predictive serum biomarkers of patients with cerebral infarction

OBJECTIVES

Stroke is the third most common cause of death and also causes seizures and disability. Biomarkers are abnormal signal indicators at the biological level that are present before the organism is seriously affected and are more sensitive to early diagnosis than are traditional imaging methods. Early diagnosis of stroke can prevent the progression of the disease. However, there are currently no widely accepted biomarkers for stroke that have been applied clinically.

METHODS

A serum metabonomics method based on ultra-high-performance liquid chromatography-quadrupole-time of flight tandem mass spectrometry (UPLC-Q-TOF/MS) was used to identify potential biomarkers and metabolic pathways of cerebral infarction. The receiver-operating characteristic (ROC) curve was used to verify the diagnostic and classification abilities of the biomarkers, and a support vector machine (SVM) model was developed for the prediction of cerebral infarction.

RESULTS

Principal component analysis revealed a clear separation between the normal and cerebral infarction groups. A total of 13 potential serum biomarkers were identified, which were mainly involved in linoleic acid metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; tyrosine metabolism; arachidonic acid metabolism; and fatty acid biosynthesis. The ROC curve analysis showed that the potential biomarkers had high specificity and sensitivity for the diagnosis of cerebral infarction. The SVM model had good diagnostic ability and could accurately distinguish the control group from the cerebral infarction group.

DISCUSSION

The metabonomics approach may be a useful bioanalytical method for understanding the pathophysiology of cerebral infarction and may provide an experimental basis for the development of clinical biomarkers for stroke.

Detection of Nail Oncometabolite SAICAR in Oral Cancer Patients and Its Molecular Interactions with PKM2 Enzyme

Oncometabolites are known to drive metabolic adaptations in oral cancer. Several oncometabolites are known to be shared between cancer cells and non-cancer cells including microbiotas to modulate the tumor microenvironment. Among potential oncometabolites, succinylaminoimidazolecarboxamide ribose5′-phosphate (SAICAR) supports the growth and invasiveness of cancer cells by pyruvate kinase M2 (PKM2) enzyme in a glucose starved tumor microenvironment. There is a significant gap that shows the detection of SAICAR in biological samples including nails of oral cancer patients. Metabolite identification of SAICAR was investigated in the nails of oral cancer patients using novel vertical tube gel electrophoresis (VTGE) and LC-HRMS. Further molecular docking and molecular dynamics simulations (MDS) were employed to determine the nature of molecular interactions of SAICAR (CHEBI ID:18319) with PKM2 (PDB ID: 4G1N). Molecular docking of SAICAR (CHEBI ID:18319) was performed against pyruvate kinase M2 (PDB ID: 4G1N). Data suggest the presence of oncometabolite SAICAR in nails of oral cancer. Molecular docking of SAICAR with PKM2 showed appreciable binding affinity (−8.0 kcal/mol) with residues including ASP407, THR405, GLU410, ARG443, GLY321, ARG436, HIS439, LYS266, and TYR466. Furthermore, MDS confirmed the specific binding of SAICAR within the activator site of PKM2 and the stability of SAICAR and PKM2 molecular interactions. In conclusion, SAICAR is a promising oncometabolite biomarker present in the nails of oral cancer patients. A significant activation potential of SAICAR exists with the PKM2 enzyme.

Targeting Fatty Acid Synthase Modulates Metabolic Pathways and Inhibits Cholangiocarcinoma Cell Progression

An aberrant regulation of lipid metabolism is involved in the pathogenesis and progression of cancer. Up-regulation of lipid biosynthesis enzymes, including acetyl-CoA carboxylase (ACC), fatty acid synthase (FASN) and HMG-CoA reductase (HMGCR), has been reported in many cancers. Therefore, elucidating lipid metabolism changes in cancer is essential for the development of novel therapeutic targets for various human cancers. The current study aimed to identify the abnormal expression of lipid-metabolizing enzymes in cholangiocarcinoma (CCA) and to evaluate whether they can be used as the targets for CCA treatment. Our study demonstrated that a high expression of FASN was significantly correlated with the advanced stage in CCA patients. In addition, survival analysis showed that high expression of FASN and HMGCR was correlated with shorter survival of CCA patients. Furthermore, FASN knockdown inhibited the growth, migration and invasion in CCA cell lines, KKU055 and KKU213, as well as induced cell cycle arrest and apoptosis in the CCA cell lines. In addition, metabolomics study further revealed that purine metabolism was the most relevant pathway involved in FASN knockdown. Adenosine diphosphate (ADP), glutamine and guanine levels significantly increased in KKU213 cells while guanine and xanthine levels remarkably increased in KKU055 cells showing a marked difference between the control and FASN knockdown groups. These findings provide new insights into the mechanisms associated with FASN knockdown in CCA cell lines and suggest that targeting FASN may serve as a novel CCA therapeutic strategy.

Integrative Metabolomic and Lipidomic Profiling of Lung Squamous Cell Carcinoma for Characterization of Metabolites and Intact Lipid Species Related to the Metastatic Potential

SQCC is a major type of NSCLC, which is a major cause of cancer-related deaths, and there were no reports regarding the prediction of metastatic potential of lung SQCC by metabolomic and lipidomic profiling. In this study, metabolomic and lipidomic profiling of lung SQCC were performed to predict its metastatic potential and to suggest potential therapeutic targets for the inhibition of lung SQCC metastasis. Human bronchial epithelial cells and four lung SQCC cell lines with different metastatic potentials were analyzed using gas chromatography-mass spectrometry and direct infusion-mass spectrometry. Based on the obtained metabolic and lipidomic profiles, we constructed models to predict the metastatic potential of lung SQCC; glycerol, putrescine, β-alanine, hypoxanthine, inosine, myo-inositol, phosphatidylinositol (PI) 18:1/18:1, and PI 18:1/20:4 were suggested as characteristic metabolites and intact lipid species associated with lung SQCC metastatic potential. In this study, we established predictive models for the metastatic potential of lung SQCC; furthermore, we identified metabolites and intact lipid species relevant to lung SQCC metastatic potential that may serve as potential therapeutic targets for the inhibition of lung SQCC metastasis.

Metabolomics profiling for identification of potential biomarkers in chickens infected with avian leukosis virus subgroup J (ALV-J)

There are currently no vaccines or effective drugs to prevent the disorders caused by avian leukosis virus subgroup J (ALV-J). Hence, it is critical to identify potential biomarkers in ALV-J-infected chickens to prevent ALV-J-induced disorders. We hypothesized that ALV-J infection alters metabolic profile in chickens. In the present study, a nontargeted metabolomics approach based on liquid chromatography coupled with mass spectrometry (LC-MS) was used to find differential metabolites in plasma samples from ALV-J-infected chickens and healthy controls. The parametric statistical test (Student's t-test) and fold change analysis were used for univariate analysis. Multivariate statistical analyses included principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The levels of methyl bromide, pyraclonil, hexaflumuron, lythidathion, 3-phosphoglycerol-glutathione, bis-4-nitrophenyl phosphate, 4-ketocyclophosphamide, oxidized photinus luciferin, phenyl sulfate, and aryl sulfate significantly decreased, whereas the levels of 2-methylthiobenzothiazole, irinotecan, methadone, 3-o-ethyl-l-ascorbic acid, and o-acetylneuraminic acid markedly increased in ALV-J-infected chickens as compared to those in healthy controls. These data provide metabolic evidence and potential biomarkers for ALV-J-induced alterations in plasma metabolism.

Regulation and Functions of Protumoral Unconventional T Cells in Solid Tumors

The vast majority of studies on T cell biology in tumor immunity have focused on peptide-reactive conventional T cells that are restricted to polymorphic major histocompatibility complex molecules. However, emerging evidence indicated that unconventional T cells, including γδ T cells, natural killer T (NKT) cells and mucosal-associated invariant T (MAIT) cells are also involved in tumor immunity. Unconventional T cells span the innate-adaptive continuum and possess the unique ability to rapidly react to nonpeptide antigens via their conserved T cell receptors (TCRs) and/or to activating cytokines to orchestrate many aspects of the immune response. Since unconventional T cell lineages comprise discrete functional subsets, they can mediate both anti- and protumoral activities. Here, we review the current understanding of the functions and regulatory mechanisms of protumoral unconventional T cell subsets in the tumor environment. We also discuss the therapeutic potential of these deleterious subsets in solid cancers and why further feasibility studies are warranted.

A Metabolic Model of Intestinal Secretions: The Link between Human Microbiota and Colorectal Cancer Progression

The human gut microbiota plays a dual key role in maintaining human health or inducing disorders, for example, obesity, type 2 diabetes, and cancers such as colorectal cancer (CRC). High-throughput data analysis, such as metagenomics and metabolomics, have shown the diverse effects of alterations in dynamic bacterial populations on the initiation and progression of colorectal cancer. However, it is well established that microbiome and human cells constantly influence each other, so it is not appropriate to study them independently. Genome-scale metabolic modeling is a well-established mathematical framework that describes the dynamic behavior of these two axes at the system level. In this study, we created community microbiome models of three conditions during colorectal cancer progression, including carcinoma, adenoma and health status, and showed how changes in the microbial population influence intestinal secretions. Conclusively, our findings showed that alterations in the gut microbiome might provoke mutations and transform adenomas into carcinomas. These alterations include the secretion of mutagenic metabolites such as H₂S, NO compounds, spermidine and TMA (trimethylamine), as well as the reduction of butyrate. Furthermore, we found that the colorectal cancer microbiome can promote inflammation, cancer progression (e.g., angiogenesis) and cancer prevention (e.g., apoptosis) by increasing and decreasing certain metabolites such as histamine, glutamine and pyruvate. Thus, modulating the gut microbiome could be a promising strategy for the prevention and treatment of CRC.

Metabolomics in cancer research and emerging applications in clinical oncology

Cancer has myriad effects on metabolism that include both rewiring of intracellular metabolism to enable cancer cells to proliferate inappropriately and adapt to the tumor microenvironment, and changes in normal tissue metabolism. With the recognition that fluorodeoxyglucose-positron emission tomography imaging is an important tool for the management of many cancers, other metabolites in biological samples have been in the spotlight for cancer diagnosis, monitoring, and therapy. Metabolomics is the global analysis of small molecule metabolites that like other -omics technologies can provide critical information about the cancer state that are otherwise not apparent. Here, the authors review how cancer and cancer therapies interact with metabolism at the cellular and systemic levels. An overview of metabolomics is provided with a focus on currently available technologies and how they have been applied in the clinical and translational research setting. The authors also discuss how metabolomics could be further leveraged in the future to improve the management of patients with cancer.