The plasma glutamate concentration as a complementary tool to differentiate benign PET-positive lung lesions from lung cancer

Metabolomics-based search for lung cancer markers among patients with different smoking status

Tobacco smoking is the main etiological factor of lung cancer (LC), which can also cause metabolome disruption. This study aimed to investigate whether the observed metabolic shift in LC patients was also associated with their smoking status. Untargeted metabolomics profiling was applied for the initial screening of changes in serum metabolic profile between LC and chronic obstructive pulmonary disease (COPD) patients, selected as a non-cancer group. Differences in metabolite profiles between current and former smokers were also tested. Then, targeted metabolomics methods were applied to verify and validate the proposed LC biomarkers. For untargeted metabolomics, a single extraction-dual separation workflow was applied. The samples were analyzed using a liquid chromatograph-high resolution quadrupole time-of-flight mass spectrometer. Next, the selected metabolites were quantified using liquid chromatography-triple-quadrupole mass spectrometry. The acquired data confirmed that patients' stratification based on smoking status impacted the discriminating ability of the identified LC marker candidates. Analyzing a validation set of samples enabled us to determine if the putative LC markers were truly robust. It demonstrated significant differences in the case of four metabolites: allantoin, glutamic acid, succinic acid, and sphingosine-1-phosphate. Our research showed that studying the influence of strong environmental factors, such as tobacco smoking, should be considered in cancer marker research since it reduces the risk of false positives and improves understanding of the metabolite shifts in cancer patients.

Integration of clinical phenoms and metabolomics facilitates precision medicine for lung cancer

Lung cancer is a common malignancy that is frequently associated with systemic metabolic disorders. Early detection is pivotal to survival improvement. Although blood biomarkers have been used in its early diagnosis, missed diagnosis and misdiagnosis still exist due to the heterogeneity of lung cancer. Integration of multiple biomarkers or trans-omics results can improve the accuracy and reliability for lung cancer diagnosis. As metabolic reprogramming is a hallmark of lung cancer, metabolites, specifically lipids might be useful for lung cancer detection, yet systematic characterizations of metabolites in lung cancer are still incipient. The present study profiled the polar metabolome and lipidome in the plasma of lung cancer patients to construct an inclusive metabolomic atlas of lung cancer. A comprehensive analysis of lung cancer was also conducted combining metabolomics with clinical phenotypes. Furthermore, the differences in plasma lipid metabolites were compared and analyzed among different lung cancer subtypes. Alcohols, amides, and peptide metabolites were significantly increased in lung cancer, while carboxylic acids, hydrocarbons, and fatty acids were remarkably decreased. Lipid profiling revealed a significant increase in plasma levels of CER, PE, SM, and TAG in individuals with lung cancer as compared to those in healthy controls. Correlation analysis confirmed the association between a panel of metabolites and TAGs. Clinical trans-omics studies elucidated the complex correlations between lipidomic data and clinical phenotypes. The present study emphasized the clinical importance of lipidomics in lung cancer, which involves the correlation between metabolites and the expressions of other omics, ultimately influencing clinical phenotypes. This novel trans-omics network approach would facilitate the development of precision therapy for lung cancer.

Plasma Metabolite Profiling in the Search for Early-Stage Biomarkers for Lung Cancer: Some Important Breakthroughs

Lung cancer is the leading cause of cancer-related mortality worldwide. In order to improve its overall survival, early diagnosis is required. Since current screening methods still face some pitfalls, such as high false positive rates for low-dose computed tomography, researchers are still looking for early biomarkers to complement existing screening techniques in order to provide a safe, faster, and more accurate diagnosis. Biomarkers are biological molecules found in body fluids, such as plasma, that can be used to diagnose a condition or disease. Metabolomics has already been shown to be a powerful tool in the search for cancer biomarkers since cancer cells are characterized by impaired metabolism, resulting in an adapted plasma metabolite profile. The metabolite profile can be determined using nuclear magnetic resonance, or NMR. Although metabolomics and NMR metabolite profiling of blood plasma are still under investigation, there is already evidence for its potential for early-stage lung cancer diagnosis, therapy response, and follow-up monitoring. This review highlights some key breakthroughs in this research field, where the most significant biomarkers will be discussed in relation to their metabolic pathways and in light of the altered cancer metabolism.

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

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

Paraoxonase-1: How a xenobiotic detoxifying enzyme has become an actor in the pathophysiology of infectious diseases and cancer

Both infectious and non-infectious diseases can share common molecular mechanisms, including oxidative stress and inflammation. External factors, such as bacterial or viral infections, excessive calorie intake, inadequate nutrients, or environmental factors, can cause metabolic disorders, resulting in an imbalance between free radical production and natural antioxidant systems. These factors may lead to the production of free radicals that can oxidize lipids, proteins, and nucleic acids, causing metabolic alterations that influence the pathogenesis of the disease. The relationship between oxidation and inflammation is crucial, as they both contribute to the development of cellular pathology. Paraoxonase 1 (PON1) is a vital enzyme in regulating these processes. PON1 is an enzyme that is bound to high-density lipoproteins and protects the organism against oxidative stress and toxic substances. It breaks down lipid peroxides in lipoproteins and cells, enhances the protection of high-density lipoproteins against different infectious agents, and is a critical component of the innate immune system. Impaired PON1 function can affect cellular homeostasis pathways and cause metabolically driven chronic inflammatory states. Therefore, understanding these relationships can help to improve treatments and identify new therapeutic targets. This review also examines the advantages and disadvantages of measuring serum PON1 levels in clinical settings, providing insight into the potential clinical use of this enzyme.

Profiling of amines in biological samples using polythioester-functionalized magnetic nanoprobe

Introduction: The metabolic balance of amines is closely related to human health. It remains a great challenge to analyze amines with high-throughput and high-coverage. Methods: Polythioester-functionalized magnetic nanoprobes (PMPs) have been prepared under mild conditions and applied in chemoselective capture of amides. With the introduction of polythioester, PMPs demonstrate remarkably increased capture efficiency, leading to the dramatically improved sensitivity of mass spectrometry detection. Results: The analysis method with PMPs treatment has been applied in rapid detection of more than 100 amines in lung adenocarcinoma cell lines, mouse organ tissues, and 103 human serum samples with high-throughput and high-coverage. Statistical analysis shows that arginine biosynthesis differed between lung adenocarcinoma cell lines. Discussion: Phenylalanine, tyrosine and tryptophan biosynthesis differed between tissues. The combination indicators demonstrate a great diagnostic accuracy for distinguishing between health and lung disease subjects as well as differentiating the patients with benign lung disease and lung cancer. With powerful capture ability, low-cost preparation, and convenient separation, the PMPs demonstrate promising application in the intensive study of metabolic pathways and early diagnosis of disease.high-throughput and high-coverage. Here, polythioester-functionalized magnetic nanoprobes (PMPs) have been prepared under mild conditions and applied in chemoselective capture of amides. With the introduction of polythioester, PMPs demonstrate remarkably increased capture efficiency, leading to the dramatically improved sensitivity of mass spectrometry detection. The analysis method with PMPs treatment has been applied in rapid detection of more than 100 amines in lung adenocarcinoma cell lines, mouse organ tissues, and 103 human serum samples with high-throughput and high-coverage. Statistical analysis shows that arginine biosynthesis differed between lung adenocarcinoma cell lines. Phenylalanine, tyrosine and tryptophan biosynthesis differed between tissues. The combination indicators demonstrate a great diagnostic accuracy for distinguishing between health and lung disease subjects as well as differentiating the patients with benign lung disease and lung cancer. With powerful capture ability, low-cost preparation, and convenient separation, the PMPs demonstrate promising application in the intensive study of metabolic pathways and early diagnosis of disease.

Changes in Metabolism as a Diagnostic Tool for Lung Cancer: Systematic Review

Lung cancer is the leading cause of cancer-related mortality worldwide, with five-year survival rates varying from 3–62%. Screening aims at early detection, but half of the patients are diagnosed in advanced stages, limiting therapeutic possibilities. Positron emission tomography-computed tomography (PET-CT) is an essential technique in lung cancer detection and staging, with a sensitivity reaching 96%. However, since elevated 18F-fluorodeoxyglucose (¹⁸F-FDG) uptake is not cancer-specific, PET-CT often fails to discriminate between malignant and non-malignant PET-positive hypermetabolic lesions, with a specificity of only 23%. Furthermore, discrimination between lung cancer types is still impossible without invasive procedures. High mortality and morbidity, low survival rates, and difficulties in early detection, staging, and typing of lung cancer motivate the search for biomarkers to improve the diagnostic process and life expectancy. Metabolomics has emerged as a valuable technique for these pitfalls. Over 150 metabolites have been associated with lung cancer, and several are consistent in their findings of alterations in specific metabolite concentrations. However, there is still more variability than consistency due to the lack of standardized patient cohorts and measurement protocols. This review summarizes the identified metabolic biomarkers for early diagnosis, staging, and typing and reinforces the need for biomarkers to predict disease progression and survival and to support treatment follow-up.

Gut Microbiome Was Highly Related to the Regulation of Metabolism in Lung Adenocarcinoma Patients

Background

Lung adenocarcinoma (LUAD) is one of the most predominant subtypes of lung cancer. The gut microbiome plays a vital role in the pathophysiological processes of various diseases, including cancers.

Methods

In the study, 100 individuals were enrolled. In total 75 stool and blood samples were analyzed with 16s-rRNA gene sequencing and metabolomics (30 from healthy individuals (H); 45 from LUAD patients). In addition, 25 stool samples were analyzed with metagenomics (10 from H; 15 from LUAD). The linear discriminant analysis (LDA) effect size (LefSe) and logistic regression analysis were applied to identify biomarkers’ taxa and develop a diagnostic model. The diagnostic power of the model was estimated with the receiver operating characteristic curve (ROC) by comparing the area under the ROC (AUC). The correlation between biomarker’s taxa and metabolites was calculated using the Spearman analysis.

Results

The α and β diversity demonstrated the composition and structure of the gut microbiome in LUAD patients were different from those in healthy people. The top three abundance of genera were Bacteroides (25.06%), Faecalibacterium (11.00%), and Prevotella (5.94%). The LefSe and logistic regression analysis identified three biomarker taxa (Bacteroides, Pseudomonas, and Ruminococcus gnavus group) and constructed a diagnostic model. The AUCs of the diagnostic model in 16s-rRNA gene sequencing and metagenomics were 0.852 and 0.841, respectively. A total of 102 plasma metabolites were highly related to those three biomarkers’ taxa. Seven metabolic pathways were enriched by 102 plasma metabolites, including the Pentose phosphate pathway, Glutathione metabolism.

Conclusions

In LUAD patients, the gut microbiome profile has significantly changed. We used three biomarkers taxa to develop a diagnostic model, which was accurate and suitable for the diagnosis of LUAD. Gut microbes, especially those three biomarkers’ taxa, may participate in regulating metabolism-related pathways in LUAD patients, such as the pentose phosphate pathway and glutathione metabolism.

Unraveling the Rewired Metabolism in Lung Cancer Using Quantitative NMR Metabolomics

Lung cancer cells are well documented to rewire their metabolism and energy production networks to enable proliferation and survival in a nutrient-poor and hypoxic environment. Although metabolite profiling of blood plasma and tissue is still emerging in omics approaches, several techniques have shown potential in cancer diagnosis. In this paper, the authors describe the alterations in the metabolic phenotype of lung cancer patients. In addition, we focus on the metabolic cooperation between tumor cells and healthy tissue. Furthermore, the authors discuss how metabolomics could improve the management of lung cancer patients.

nNav1.5 expression is associated with glutamate level in breast cancer cells

Background

Glutamate and voltage-gated sodium channels, both have been the target of intense investigation for its involvement in carcinogenesis and progression of malignant disease. Breast cancer with increased level of glutamate often metastasize to other organs (especially bone), whilst re-expression of ‘neonatal’ Nav1.5, nNav1.5 in breast cancer is known to promote cell invasion in vitro, metastasis in vivo and positive lymph node metastasis in patients.

Methods

In this study, the role of nNav1.5 in regulating glutamate level in human breast cancer cells was examined using pharmacological approach (VGSCs specific blocker, TTX, glutamate release inhibitor, riluzole and siRNA-nNav1.5). Effect of these agents were evaluated based on endogenous and exogenous glutamate concentration using glutamate fluorometric assay, mRNA expression of nNav1.5 using qPCR and finally, invasion using 3D culture assay.

Results

Endogenous and exogenous glutamate levels were significantly higher in aggressive human breast cancer cells, MDA-MB-231 cells compared to less aggressive human breast cancer cells, MCF-7 and non-cancerous human breast epithelial cells, MCF-10A. Treatment with TTX to MDA-MB-231 cells resulted in significant reduction of endogenous and exogenous glutamate levels corresponded with significant suppression of cell invasion. Subsequently, downregulation of nNav1.5 gene was observed in TTX-treated cells.

Conclusions

An interesting link between nNav1.5 expression and glutamate level in aggressive breast cancer cells was detected and requires further investigation.

Serum fingerprinting by slippery liquid-infused porous SERS for non-invasive lung cancer detection

Direct and label-free analysis of clinical serum samples using slippery liquid-infused porous-enhanced Raman spectroscopy (SLIPSERS) enables the rapid non-invasive identification of lung cancer.

Evolutionary metabolic landscape from preneoplasia to invasive lung adenocarcinoma

Metabolic reprogramming evolves during cancer initiation and progression. However, thorough understanding of metabolic evolution from preneoplasia to lung adenocarcinoma (LUAD) is still limited. Here, we perform large-scale targeted metabolomics on resected lesions and plasma obtained from invasive LUAD and its precursors, and decipher the metabolic trajectories from atypical adenomatous hyperplasia (AAH) to adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC), revealing that perturbed metabolic pathways emerge early in premalignant lesions. Furthermore, three panels of plasma metabolites are identified as non-invasive predictive biomarkers to distinguish IAC and its precursors with benign diseases. Strikingly, metabolomics clustering defines three metabolic subtypes of IAC patients with distinct clinical characteristics. We identify correlation between aberrant bile acid metabolism in subtype III with poor clinical features and demonstrate dysregulated bile acid metabolism promotes migration of LUAD, which could be exploited as potential targetable vulnerability and for stratifying patients. Collectively, the comprehensive landscape of the metabolic evolution along the development of LUAD will improve early detection and provide impactful therapeutic strategies.

Metabolic reprogramming occurs during tumor progression. Here the authors decipher metabolic trajectories from preneoplasia to lung adenocarcinoma in tumor samples and identify plasma metabolites as potential predictive biomarkers for early detection.

On the Role of Paraoxonase-1 and Chemokine Ligand 2 (C-C motif) in Metabolic Alterations Linked to Inflammation and Disease. A 2021 Update

Infectious and many non-infectious diseases share common molecular mechanisms. Among them, oxidative stress and the subsequent inflammatory reaction are of particular note. Metabolic disorders induced by external agents, be they bacterial or viral pathogens, excessive calorie intake, poor-quality nutrients, or environmental factors produce an imbalance between the production of free radicals and endogenous antioxidant systems; the consequence being the oxidation of lipids, proteins, and nucleic acids. Oxidation and inflammation are closely related, and whether oxidative stress and inflammation represent the causes or consequences of cellular pathology, both produce metabolic alterations that influence the pathogenesis of the disease. In this review, we highlight two key molecules in the regulation of these processes: Paraoxonase-1 (PON1) and chemokine (C-C motif) ligand 2 (CCL2). PON1 is an enzyme bound to high-density lipoproteins. It breaks down lipid peroxides in lipoproteins and cells, participates in the protection conferred by HDL against different infectious agents, and is considered part of the innate immune system. With PON1 deficiency, CCL2 production increases, inducing migration and infiltration of immune cells in target tissues and disturbing normal metabolic function. This disruption involves pathways controlling cellular homeostasis as well as metabolically-driven chronic inflammatory states. Hence, an understanding of these relationships would help improve treatments and, as well, identify new therapeutic targets.

Metabolic Signaling Cascades Prompted by Glutaminolysis in Cancer

Simple Summary

Within the last few years, accumulating evidences suggest the involvement of altered metabolisms in human diseases including cancer. Metabolism is defined as the sum of biochemical processes in living organisms that produce and consume energy. Tumor growth requires restructuring of cellular metabolism to meet the increasing demand for building blocks to support the ever-increasing cancer cell numbers. The principle of perturbed metabolism in tumors is known for 50–60 years, it regains greater appreciation within the last few years with the realization that there is interdependency between metabolism and all aspects of cellular function including regulation and control of cell growth. Tumor cells do not need stimulation signals from the surrounding environment to promote cell proliferation; in some cases, the tumor cells can generate their own growth signals. In order to support the continuous tumor cell growth even under stressful conditions, a change in metabolism is necessary to fulfill the continuous demand for energy and building blocks. A better understanding of the relationship between tumor environment and altered cell metabolisms will provide valuable insights to design innovative approaches to limit the supply of energy and macromolecules for the treatment of cancer including melanoma.

Abstract

Aberrant glutamatergic signaling has been implicated in altered metabolic activity and the demand to synthesize biomass in several types of cancer including melanoma. In the last decade, there has been a significant contribution to our understanding of metabolic pathways. An increasing number of studies are now emphasizing the importance of glutamate functioning as a signaling molecule and a building block for cancer progression. To that end, our group has previously illustrated the role of glutamatergic signaling mediated by metabotropic glutamate receptor 1 (GRM1) in neoplastic transformation of melanocytes in vitro and spontaneous development of metastatic melanoma in vivo. Glutamate, the natural ligand of GRM1, is one of the most abundant amino acids in humans and the predominant excitatory neurotransmitter in the central nervous system. Elevated levels of glutaminolytic mitochondrial tricarboxylic acid (TCA) cycle intermediates, especially glutamate, have been reported in numerous cancer cells. Herein, we highlight and critically review metabolic bottlenecks that are prevalent during tumor evolution along with therapeutic implications of limiting glutamate bioavailability in tumors.

Alterations in plasma concentrations of energy-balance-related metabolites in patients with lung, or head & neck, cancers: Effects of radiotherapy

We investigated the alterations in the plasma concentrations of energy-balance-related metabolites in patients with lung (LC) or head & neck (HNC) cancer and the changes on these parameters induced by radiotherapy. The study was conducted in 33 patients with non-small cell LC and 28 patients with HNC. We analyzed the concentrations of 17 metabolites involved in glycolysis, citric acid cycle and amino acid metabolism using targeted gas chromatography coupled to quadrupole time-of-flight mass spectrometry. For comparison, a control group of 50 healthy individuals was included in the present study. Patients with LC or HNC had significant alterations in the plasma levels of several energy-balance-related metabolites. Radiotherapy partially normalized these alterations in patients with LC, but not in those with HNC. The measurement of plasma glutamate concentration was an excellent predictor of the presence of LC or HNC, with sensitivity >90% and specificity >80%. Also, associations with disease prognosis were observed with plasma glutamate, amino acids and β-hydroxybutyrate concentrations. SIGNIFICANCE: This study analyzed the changes produced in the plasma concentrations of energy-balance-related metabolites in patients with lung cancer or head and neck cancer. The results obtained identified glutamate as the parameter with the highest discrimination capacity between patients and the control group. The relationships between various metabolites and clinical outcomes were also analyzed. These results extend the knowledge of metabolic alterations in cancer, thus facilitating the search for biomarkers and therapeutic targets.