Metabolomic markers of altered nucleotide metabolism in early stage adenocarcinoma

Decoding the mechanisms behind second primary cancers

Second Primary Cancers (SPCs) are defined as cancers that develop either simultaneously or metachronously in the same individual who has been diagnosed with and survived one primary cancer. SPCs exhibit a high incidence rate and represent the primary cause of mortality among survivors of first primary cancers. There is growing concern about the dangers of SPCs. This review summarizes recent studies on the mechanisms of SPCs, including the roles of genomic changes after first primary cancer (FPC) treatments, stromal cell phenotypic and metabolic changes, hormone levels and receptor expression, immunosuppression, aberrant gene methylation, EGFR signaling, and cell-free DNA in SPC development. This comprehensive analysis contributes to elucidating current research trends in SPC mechanisms and enhances our understanding of the underlying pathophysiology. Furthermore, potential applications of intratumoral microbes, single-cell multi-omics, and metabolomics in investigating SPC mechanisms are also discussed, providing new ideas for follow-up studies.

Comparison of two metabolomics-platforms to discover biomarkers in critically ill patients from serum analysis

Serum metabolome analysis is essential for identifying disease biomarkers and predicting patient outcomes in precision medicine. Thus, this study aims to compare Ultra-High Performance Liquid Chromatography-High-Resolution Mass Spectrometry (UHPLC-HRMS) with Fourier Transform Infrared (FTIR) spectroscopy in acquiring the serum metabolome of critically ill patients, associated with invasive mechanical ventilation (IMV), and predicting death. Three groups of 8 patients were considered. Group A did not require IMV and survived hospitalization, while Groups B and C required IMV. Group C patients died a median of 5 days after sample harvest. Good prediction models were achieved when comparing groups A to B and B to C using both platforms' data, with UHPLC-HRMS showing 8-17 % higher accuracies (≥83 %). However, developing predictive models using metabolite sets was not feasible when comparing unbalanced populations, i.e., Groups A and B combined to Group C. Alternatively, FTIR-spectroscopy enabled the development of a model with 83 % accuracy. Overall, UHPLC-HRMS data yields more robust prediction models when comparing homogenous populations, potentially enhancing understanding of metabolic mechanisms and improving patient therapy adjustments. FTIR-spectroscopy is more suitable for unbalanced populations. Its simplicity, speed, cost-effectiveness, and high-throughput operation make it ideal for large-scale studies and clinical translation in complex populations.

Plant Metabolomics: The Future of Anticancer Drug Discovery

Drug development from medicinal plants constitutes an important strategy for finding natural anticancer therapies. While several plant secondary metabolites with potential antitumor activities have been identified, well-defined mechanisms of action remained uncovered. In fact, studies of medicinal plants have often focused on the genome, transcriptome, and proteome, dismissing the relevance of the metabolome for discovering effective plant-based drugs. Metabolomics has gained huge interest in cancer research as it facilitates the identification of potential anticancer metabolites and uncovers the metabolomic alterations that occur in cancer cells in response to treatment. This holds great promise for investigating the mode of action of target metabolites. Although metabolomics has made significant contributions to drug discovery, research in this area is still ongoing. In this review, we emphasize the significance of plant metabolomics in anticancer research, which continues to be a potential technique for the development of anticancer drugs in spite of all the challenges encountered. As well, we provide insights into the essential elements required for performing effective metabolomics analyses.

Preliminary Metabolomics Study Suggests Favorable Metabolic Changes in the Plasma of Breast Cancer Patients after Surgery and Adjuvant Treatment

Background/Objectives: The management of early breast cancer (BC) includes surgery, followed by adjuvant radiotherapy, chemotherapy, hormone therapy, or immunotherapy. However, the influence of these interventions in metabolic reprogramming remains unknown. This study explored alterations in the plasma metabolome of BC patients following distinct treatments to deepen our understanding of BC pathophysiology, outcomes, and the identification of potential biomarkers. Methods: We included 52 women diagnosed with BC and candidates for surgery as primary oncological treatment. Blood samples were collected at diagnosis, two weeks post-surgery, and one month post-radiotherapy. Plasma samples from 49 healthy women served as controls. Targeted metabolomics assessed 74 metabolites spanning carbohydrates, amino acids, lipids, nucleotide pathways, energy metabolism, and xenobiotic biodegradation. Results: Before treatment, the BC patients exhibited notable changes in carbohydrate, nucleotide, lipid, and amino acid metabolism. We noticed a gradual restoration of specific metabolite levels (hypoxanthine, 3-phosphoglyceric acid, xylonic acid, and maltose) throughout different treatments, suggesting a normalization of the nucleotide and carbohydrate metabolic pathways. Moreover, we observed increased dodecanoic acid concentrations, a metabolite associated with cancer protection. These variations distinguished patients from controls with high specificity and sensitivity. Conclusions: Our preliminary study suggests that oncological treatments modify the metabolism of patients towards a favorable profile with a decrease in the pathways that favor cell proliferation and an increase in the levels of anticancer molecules. These findings emphasize the pivotal role of metabolomics in recognizing the biological pathways influenced by each cancer treatment and the resulting metabolic consequences. Furthermore, it aids in identifying potential biomarkers for disease onset and progression.

The relationship between metabolite mediated immune regulatory imbalance and the occurrence of malignant tumors of bone and articular cartilage: a Mendelian randomization study

Background

This study aims to assess the causal relationship between immune cell characteristics and malignant tumors of bone and articular cartilage, focusing on the mediating role of metabolites. Using Mendelian randomization, we evaluated these relationships based on genetic variations to identify potential biomarkers and therapeutic targets.

Methods

A two-sample Mendelian randomization analysis was conducted using GWAS data for immune cell features and 1,400 metabolites to investigate direct and mediating effects. Effective instrumental variables (IVs) were selected, and statistical analyses-including inverse variance weighting (IVW), weighted median, and mode-based methods-were performed using R software. This approach enabled the assessment of direct causal relationships as well as the potential mediating role of metabolites in the association between immune cell features and malignancies.

Results

Significant causal relationships were identified between 26 immune phenotypes and the risk of malignant tumors of bone and articular cartilage. Notably, the HLA DR+ NK cell phenotype SSC-A showed a positive correlation with the risk of these malignancies. Further analysis revealed causal relationships with 67 metabolites, 38 of which were positively correlated and 29 negatively correlated. Mediation analysis highlighted the role of immune surveillance and metabolic dysregulation in tumor development, as evidenced by the association between the immune phenotype SSC-A on HLA DR+ NK cells and the metabolite 5-hydroxyhexanoate.

Conclusion

The findings suggest significant causal relationships between immune phenotypes and malignant tumors of bone and articular cartilage, with metabolites potentially mediating these relationships. These insights lay the groundwork for further research and could contribute to the development of new biomarkers and treatment strategies.

Combining Metabolomics and Machine Learning to Identify Diagnostic and Prognostic Biomarkers in Patients with Non-Small Cell Lung Cancer Pre- and Post-Radiation Therapy

Lung cancer is the leading cause of cancer-related deaths globally, with non-small cell lung cancer (NSCLC) accounting for over 85% of cases and poor prognosis in advanced stages. This study explored shifts in circulating metabolite levels in NSCLC patients versus healthy controls and examined the effects of conventionally fractionated radiation therapy (CFRT) and stereotactic body radiation therapy (SBRT). We enrolled 91 NSCLC patients (38 CFRT and 53 SBRT) and 40 healthy controls. Plasma metabolite levels were assessed using semi-targeted metabolomics, revealing 32 elevated and 18 reduced metabolites in patients. Key discriminatory metabolites included ethylmalonic acid, maltose, 3-phosphoglyceric acid, taurine, glutamic acid, glycocolic acid, and d-arabinose, with a combined Receiver Operating Characteristics curve indicating perfect discrimination between patients and controls. CFRT and SBRT affected different metabolites, but both changes suggested a partial normalization of energy and amino acid metabolism pathways. In conclusion, metabolomics identified distinct metabolic signatures in NSCLC patients with potential as diagnostic biomarkers. The differing metabolic responses to CFRT and SBRT reflect their unique therapeutic impacts, underscoring the utility of this technique in enhancing NSCLC diagnosis and treatment monitoring.

A critical appraisal of the role of metabolomics in breast cancer research and diagnostics

Breast cancer (BC) remains the most prevalent cancer among women worldwide, despite significant advancements in its prevention and treatment. The escalating incidence of BC globally necessitates continued research into novel diagnostic and therapeutic strategies. Metabolomics, a burgeoning field, offers a comprehensive analysis of all metabolites within a cell, tissue, system, or organism, providing crucial insights into the dynamic changes occurring during cancer development and progression. This review focuses on the metabolic alterations associated with BC, highlighting the potential of metabolomics in identifying biomarkers for early detection, diagnosis, treatment and prognosis. Metabolomics studies have revealed distinct metabolic signatures in BC, including alterations in lipid metabolism, amino acid metabolism, and energy metabolism. These metabolic changes not only support the rapid proliferation of cancer cells but also influence the tumour microenvironment and therapeutic response. Furthermore, metabolomics holds great promise in personalized medicine, facilitating the development of tailored treatment strategies based on an individual's metabolic profile. By providing a holistic view of the metabolic changes in BC, metabolomics has the potential to revolutionize our understanding of the disease and improve patient outcomes.

Adenine is an anti-inflammatory metabolite found to be more abundant in M-CSF over GM-CSF-differentiated human macrophages

Immunometabolism has been unveiled in the last decade to play a major role in controlling macrophage metabolism and inflammation. There has been a constant effort to understand the immunomodulating properties of regulated metabolites during inflammation with the aim of controlling and re-wiring aberrant macrophages in inflammatory diseases. M-CSF and GM-CSF-differentiated macrophages play a key role in mounting successful innate immune responses. When a resolution phase is not achieved however, GM-CSF macrophages contribute substantially more towards an adverse inflammatory milieu than M-CSF macrophages, consequently driving disease progression. Whether there are specific immunometabolites that determine the homoeostatic or inflammatory nature of M-CSF and GM-CSF-differentiated macrophages is still unknown. As such, we performed metabolomics analysis on LPS and IL-4-stimulated M-CSF and GM-CSF-differentiated human macrophages to identify differentially accumulating metabolites. Adenine was distinguished as a metabolite significantly higher in M-CSF-differentiated macrophages after both LPS or IL-4 stimulation. Human macrophages treated with adenine before LPS stimulation showed a reduction in inflammatory gene expression, cytokine secretion and surface marker expression. Adenine caused macrophages to become more quiescent by lowering glycolysis and OXPHOS which resulted in reduced ATP production. Moreover, typical metabolite changes seen during LPS-induced macrophage metabolic reprogramming were absent in the presence of adenine. Phosphorylation of metabolic signalling proteins AMPK, p38 MAPK and AKT were not responsible for the suppressed metabolic activity of adenine-treated macrophages. Altogether, in this study we highlight the immunomodulating capacity of adenine in human macrophages and its function in driving cellular quiescence.

Impact of Different Treatment Regimens and Timeframes in the Plasmatic Metabolic Profiling of Patients with Lung Adenocarcinoma

In recent years, the treatment of advanced non-small cell lung cancer (NSCLC) has suffered a variety of alterations. Chemotherapy (CTX), immunotherapy (IT) and tyrosine kinase inhibitors (TKI) have shown remarkable results. However, not all patients with NSCLC respond to these drug treatments or receive durable benefits. In this framework, metabolomics has been applied to improve the diagnosis, treatment, and prognosis of lung cancer and particularly lung adenocarcinoma (AdC). In our study, metabolomics was used to analyze plasma samples from 18 patients with AdC treated with CTX or IT via 1H-NMR spectroscopy. Relevant clinical information was gathered, and several biochemical parameters were also evaluated throughout the treatments. During the follow-up of patients undergoing CTX or IT, imaging control is recommended in order to assess the effectiveness of the therapy. This evaluation is usually performed every three treatments. Based on this procedure, all the samples were collected before the beginning of the treatment and after three and six treatments. The identified and quantified metabolites in the analyzed plasma samples were the following: isoleucine, valine, alanine, acetate, lactate, glucose, tyrosine, and formate. Multivariate/univariate statistical analyses were performed. Our data are in accordance with previous published results, suggesting that the plasma glucose levels of patients under CTX become higher throughout the course of treatment, which we hypothesize could be related to the tumor response to the therapy. It was also found that alanine levels become lower during treatment with CTX regimens, a fact that could be associated with frailty. NMR spectra of long responders' profiles also showed similar results. Based on the results of the study, metabolomics can represent a potential option for future studies, in order to facilitate patient selection and the monitoring of therapy efficacy in treated patients with AdC. Further studies are needed to improve the prospective identification of predictive markers, particularly glucose and alanine levels, as well as confer guidance to NSCLC treatment and patient stratification, thus avoiding ineffective therapeutic strategies.

How do prolonged anchorage-free lifetimes strengthen non-small-cell lung cancer cells to evade anoikis? - A link with altered cellular metabolomics

BACKGROUND

Malignant cells adopt anoikis resistance to survive anchorage-free stresses and initiate cancer metastasis. It is still unknown how varying periods of anchorage loss contribute to anoikis resistance, cell migration, and metabolic reprogramming of cancerous cells.

RESULTS

Our study demonstrated that prolonging the anchorage-free lifetime of non-small-cell lung cancer NCI-H460 cells for 7 days strengthened anoikis resistance, as shown by higher half-life and capability to survive and grow without anchorage, compared to wild-type cells or those losing anchorage for 3 days. While the prolonged anchorage-free lifetime was responsible for the increased aggressive feature of survival cells to perform rapid 3-dimensional migration during the first 3 h of a transwell assay, no significant influence was observed with 2-dimensional surface migration detected at 12 and 24 h by a wound-healing method. Metabolomics analysis revealed significant alteration in the intracellular levels of six (oxalic acid, cholesterol, 1-ethylpyrrolidine, 1-(3-methylbutyl)-2,3,4,6-tetramethylbenzene, β-alanine, and putrescine) among all 37 identified metabolites during 7 days without anchorage. Based on significance values, enrichment ratios, and impact scores of all metabolites and their associated pathways, three principal metabolic activities (non-standard amino acid metabolism, cell membrane biosynthesis, and oxidative stress response) offered potential links with anoikis resistance.

CONCLUSIONS

These findings further our insights into the evolution of anoikis resistance in lung cancer cells and identify promising biomarkers for early lung cancer diagnosis.

Metaboverse enables automated discovery and visualization of diverse metabolic regulatory patterns

Metabolism is intertwined with various cellular processes, including controlling cell fate, influencing tumorigenesis, participating in stress responses and more. Metabolism is a complex, interdependent network, and local perturbations can have indirect effects that are pervasive across the metabolic network. Current analytical and technical limitations have long created a bottleneck in metabolic data interpretation. To address these shortcomings, we developed Metaboverse, a user-friendly tool to facilitate data exploration and hypothesis generation. Here we introduce algorithms that leverage the metabolic network to extract complex reaction patterns from data. To minimize the impact of missing measurements within the network, we introduce methods that enable pattern recognition across multiple reactions. Using Metaboverse, we identify a previously undescribed metabolite signature that correlated with survival outcomes in early stage lung adenocarcinoma patients. Using a yeast model, we identify metabolic responses suggesting an adaptive role of citrate homeostasis during mitochondrial dysfunction facilitated by the citrate transporter, Ctp1. We demonstrate that Metaboverse augments the user's ability to extract meaningful patterns from multi-omics datasets to develop actionable hypotheses.

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.

Bayesian kinetic modeling for tracer-based metabolomic data

BACKGROUND

Stable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly [Formula: see text]-enriched glucose ([Formula: see text]-Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups.

RESULTS

We propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts' knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux .

CONCLUSIONS

Our new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data.

Normalization Approach by a Reference Material to Improve LC-MS-Based Metabolomic Data Comparability of Multibatch Samples

Large cohorts of samples from multiple batches are usually required for global metabolomic studies to characterize the metabolic state of human disease. As such, it is critical to eliminate systematic variation and truly reveal the biologically associated alterations. In this study, we proposed a reference material-based approach (Ref-M) for data correction by liquid chromatography-mass spectrometry and represented by an analysis of multibatch human serum samples. The reference material was generated by mixing serum from healthy donors and distributed to each extraction batch of subject samples. Pooled quality control samples and isotopic internal standards were then applied in each acquisition batch for data quality control. Finally, each metabolite in subject samples was normalized by its counterpart in the reference serum. We demonstrated that Ref-M significantly enhanced the numbers of efficient features and effectively eliminated the batch variation of 522 serum samples of healthy individuals, benign pulmonary nodules, and lung cancer patients. Twenty differential metabolites were identified to distinguish lung cancer from healthy controls in the training set. The discriminant model was validated in an independent data set with an area under the receiver operating characteristics (ROC) curve (AUC) of 0.853. Another 40 serum samples further tested with Ref-M were achieved an AUC of 0.843 by the established model. Our results showed that the reference material-based approach presents the potential to improve the data comparability and precision for biomarker discovery in large-scale metabolomic studies.

Xanthine dehydrogenase rewires metabolism and the survival of nutrient deprived lung adenocarcinoma cells by facilitating UPR and autophagic degradation

Xanthine dehydrogenase (XDH) is the rate-limiting enzyme in purine catabolism by converting hypoxanthine to xanthine and xanthine to uric acid. The altered expression and activity of XDH are associated with the development and prognosis of multiple types of cancer, while its role in lung adenocarcinoma (LUAD) remains unknown. Herein, we demonstrated that XDH was highly expressed in LUAD and was significantly correlated with poor prognosis. Though inhibition of XDH displayed moderate effect on the viability of LUAD cells cultured in the complete medium, it significantly attenuated the survival of starved cells. Similar results were obtained in XDH-knockout cells. Nucleosides supplementation rescued the survival of starved LUAD cells upon XDH inhibition, while inhibition of purine nucleoside phosphorylase abrogated the process, indicating that nucleoside degradation is required for the XDH-mediated survival of LUAD cells. Accordingly, metabolic flux revealed that ribose derived from nucleoside fueled key carbon metabolic pathways to sustain the survival of starved LUAD cells. Mechanistically, down-regulation of XDH suppressed unfolded protein response (UPR) and autophagic flux in starved LUAD cells. Inhibition of XDH decreased the level of amino acids produced by autophagic degradation, which was accompanied with down-regulation of mTORC1 signaling. Supplementation of amino acids including glutamine or glutamate rescued the survival of starved LUAD cells upon knockout or inhibition of XDH. Finally, XDH inhibitors potentiated the anti-cancer activity of 2-deoxy-D-glucose that induced UPR and/or autophagy in vitro and in vivo. In summary, XDH plays a crucial role in the survival of starved LUAD cells and targeting XDH may improve the efficacy of drugs that induce UPR and autophagy in the therapy of LUAD.

Probing Folate-Responsive and Stage-Sensitive Metabolomics and Transcriptional Co-Expression Network Markers to Predict Prognosis of Non-Small Cell Lung Cancer Patients

Tumour metabolomics and transcriptomics co-expression network as related to biological folate alteration and cancer malignancy remains unexplored in human non-small cell lung cancers (NSCLC). To probe the diagnostic biomarkers, tumour and pair lung tissue samples (n = 56) from 97 NSCLC patients were profiled for ultra-performance liquid chromatography tandem mass spectrometry (UPLC/MS/MS)-analysed metabolomics, targeted transcriptionomics, and clinical folate traits. Weighted Gene Co-expression Network Analysis (WGCNA) was performed. Tumour lactate was identified as the top VIP marker to predict advance NSCLC (AUC = 0.765, Sig = 0.017, CI 0.58-0.95). Low folate (LF)-tumours vs. adjacent lungs displayed higher glycolytic index of lactate and glutamine-associated amino acids in enriched biological pathways of amino sugar and glutathione metabolism specific to advance NSCLCs. WGCNA classified the green module for hub serine-navigated glutamine metabolites inversely associated with tumour and RBC folate, which module metabolites co-expressed with a predominant up-regulation of LF-responsive metabolic genes in glucose transport (GLUT1), de no serine synthesis (PHGDH, PSPH, and PSAT1), folate cycle (SHMT1/2 and PCFR), and down-regulation in glutaminolysis (SLC1A5, SLC7A5, GLS, and GLUD1). The LF-responsive WGCNA markers predicted poor survival rates in lung cancer patients, which could aid in optimizing folate intervention for better prognosis of NSCLCs susceptible to folate malnutrition.

Landscape and clinical impact of metabolic alterations in non-squamous non-small cell lung cancer

Background

Metabolomics studies to date have described widespread metabolic reprogramming events during the development of non-squamous non-small cell lung cancer (NSCLC). Extending far beyond the Warburg effect, not only is carbohydrate metabolism affected, but also metabolism of amino acids, cofactors, lipids, and nucleotides.

Methods

We evaluated the clinical impact of metabolic reprogramming. We performed comparative analysis of publicly available data on non-squamous NSCLC, to identify concensus altered metabolic pathways. We investigated whether alterations of metabolic genes controlling those consensus metabolic pathways impacted clinical outcome. Using the clinically annotated lung adenocarcinoma (LUAD) cohort from The Cancer Genome Atlas, we surveyed the distribution and frequency of function-altering mutations in metabolic genes and their impact on overall survival (OS).

Results

We identified 42 metabolic genes of clinical significance, the majority of which (37 of 42) clustered across three metabolic superpathways (carbohydrates, amino acids, and nucleotides) and most functions (40 of 42) were associated with shorter OS. Multivariate analyses showed that dysfunction of carbohydrate metabolism had the most profound impact on OS [hazard ratio (HR) =5.208; 95% confidence interval (CI): 3.272 to 8.291], false discovery rate (FDR)-P≤0.0001, followed by amino acid metabolism (HR =3.346; 95% CI: 2.129 to 5.258), FDR-P≤0.0001 and nucleotide metabolism (HR =2.578; 95% CI: 1.598 to 4.159), FDR-P=0.0001. The deleterious effect of metabolic reprogramming on non-squamous NSCLC was observed independently of disease stage and across treatments groups.

Conclusions

By providing a detailed landscape of metabolic alterations in non-squamous NSCLC, our findings offer new insights in the biology of the disease and metabolic adaptation mechanisms of clinical significance.

Effects of (R)-ketamine on reduced bone mineral density in ovariectomized mice: A role of gut microbiota

Depression is a high risk for osteoporosis, suggesting an association between depression and low bone mineral density (BMD). We reported that the novel antidepressant (R)-ketamine could ameliorate the reduced BMD in the ovariectomized (OVX) mice which is an animal model of postmenopausal osteoporosis. Given the role of gut microbiota in depression and bone homeostasis, we examined whether gut microbiota plays a role in the beneficial effects of (R)-ketamine in the reduced BMD of OVX mice. OVX or sham was operated for female mice. Subsequently, saline (10 ml/kg/day, twice weekly) or (R)-ketamine (10 mg/kg/day, twice weekly) was administered intraperitoneally into OVX or sham mice for the six weeks. The reduction of cortical BMD and total BMD in the OVX mice was significantly ameliorated after subsequent repeated intermittent administration of (R)-ketamine. Furthermore, there were significant changes in the α- and β-diversity between OVX + saline group and OVX + (R)-ketamine group. There were correlations between several OTUs and cortical (or total) BMD. There were also positive correlations between the genera Turicibacter and cortical (or total) BMD. Moreover, there were correlations between several metabolites in blood and cortical (or total) BMD. These data suggest that (R)-ketamine may ameliorate the reduced cortical BMD and total BMD in OVX mice through anti-inflammatory actions via gut microbiota. Therefore, it is likely that (R)-ketamine would be a therapeutic drug for depressed patients with low BMD or patients with osteoporosis.

The double faced role of xanthine oxidoreductase in cancer

Xanthine oxidoreductase (XOR) is a critical, rate-limiting enzyme that controls the last two steps of purine catabolism by converting hypoxanthine to xanthine and xanthine to uric acid. It also produces reactive oxygen species (ROS) during the catalytic process. The enzyme is generally recognized as a drug target for the therapy of gout and hyperuricemia. The catalytic products uric acid and ROS act as antioxidants or oxidants, respectively, and are involved in pro/anti-inflammatory actions, which are associated with various disease manifestations, including metabolic syndrome, ischemia reperfusion injury, cardiovascular disorders, and cancer. Recently, extensive efforts have been devoted to understanding the paradoxical roles of XOR in tumor promotion. Here, we summarize the expression of XOR in different types of cancer and decipher the dual roles of XOR in cancer by its enzymatic or nonenzymatic activity to provide an updated understanding of the mechanistic function of XOR in cancer. We also discuss the potential to modulate XOR in cancer therapy.

Fast, Direct Dihydrouracil Quantitation in Human Saliva: Method Development, Validation, and Application

Background. Salivary metabolomics is garnering increasing attention in the health field because of easy, minimally invasive saliva sampling. Dihydrouracil (DHU) is a metabolite of pyrimidine metabolism present in urine, plasma, and saliva and of fluoropyrimidines-based chemotherapeutics. Its fast quantification would help in the identification of patients with higher risk of fluoropyrimidine-induced toxicity and inborn errors of pyrimidine metabolism. Few studies consider DHU as the main salivary metabolite, but reports of its concentration levels in saliva are scarce. We propose the direct determination of DHU in saliva by reversed-phase high-performance liquid chromatography (RP-HPLC-UV detector) as a simple, rapid procedure for non-invasive screening. Methods. The method used was validated and applied to 176 saliva samples collected from 21 nominally healthy volunteers and 4 saliva samples from metastatic colorectal cancer patients before and after receiving 5-fluorouracil chemotherapy. Results. DHU levels in all samples analyzed were in the μmol L-1 range or below proving that DHU is not the main metabolite in saliva and confirming the results found in the literature with LC-MS/MS instrumentation. Any increase of DHU due to metabolism dysfunctions can be suggestive of disease and easily monitored in saliva using common, low-cost instrumentation available also for population screening.

Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival

INTRODUCTION

Metabolomics has emerged as a powerful method to provide insight into cancer progression, including separating patients into low- and high-risk groups for overall (OS) and progression-free survival (PFS). However, survival prediction based mainly on metabolites obtained from biofluids remains elusive.

OBJECTIVES

This proof-of-concept study evaluates metabolites as biomarkers obtained directly from tumor core biopsies along with covariates age, sex, pathological stage at diagnosis (I/II vs. III/VI), histological subtype, and treatment vs. no treatment to risk stratify lung cancer patients in terms of OS and PFS.

METHODS

Tumor core biopsy samples obtained during routine lung cancer patient care at the University of Louisville Hospital and Norton Hospital were evaluated with high-resolution 2DLC-MS/MS, and the data were analyzed by Kaplan-Meier survival analysis and Cox proportional hazards regression. A linear equation was developed to stratify patients into low and high risk groups based on log-transformed intensities of key metabolites. Sparse partial least squares discriminant analysis (SPLS-DA) was performed to predict OS and PFS events.

RESULTS

Univariable Cox proportional hazards regression model coefficients divided by the standard errors were used as weight coefficients multiplied by log-transformed metabolite intensity, then summed to generate a risk score for each patient. Risk scores based on 10 metabolites for OS and 5 metabolites for PFS were significant predictors of survival. Risk scores were validated with SPLS-DA classification model (AUROC 0.868 for OS and AUROC 0.755 for PFS, when combined with covariates).

CONCLUSION

Metabolomic analysis of lung tumor core biopsies has the potential to differentiate patients into low- and high-risk groups based on OS and PFS events and probability.

The hexosamine pathway and coat complex II promote malignant adaptation to nutrient scarcity

We present adaptive mechanisms of resistance of lung adenocarcinoma to their harsh microenvironment, which typically contains a lower glucose concentration compared with normal tissue.

The glucose-requiring hexosamine biosynthetic pathway (HBP), which produces UDP-N-acetylglucosamine for glycosylation reactions, promotes lung adenocarcinoma (LUAD) progression. However, lung tumor cells often reside in low-nutrient microenvironments, and whether the HBP is involved in the adaptation of LUAD to nutrient stress is unknown. Here, we show that the HBP and the coat complex II (COPII) play a key role in cell survival during glucose shortage. HBP up-regulation withstood low glucose-induced production of proteins bearing truncated N-glycans, in the endoplasmic reticulum. This function for the HBP, alongside COPII up-regulation, rescued cell surface expression of a subset of glycoproteins. Those included the epidermal growth factor receptor (EGFR), allowing an EGFR-dependent cell survival under low glucose in anchorage-independent growth. Accordingly, high expression of the HBP rate-limiting enzyme GFAT1 was associated with wild-type EGFR activation in LUAD patient samples. Notably, HBP and COPII up-regulation distinguished LUAD from the lung squamous-cell carcinoma subtype, thus uncovering adaptive mechanisms of LUAD to their harsh microenvironment.

Clinical Significance of Screening Differential Metabolites in Ovarian Cancer Tissue and Ascites by LC/MS

Tumor cells not only show a vigorous metabolic state, but also reflect the disease progression and prognosis from their metabolites. To judge the progress and prognosis of ovarian cancer is generally based on the formation of ascites, or whether there is ascites recurrence during chemotherapy after ovarian cancer surgery. To explore the relationship between the production of ascites and ovarian cancer tissue, metabolomics was used to screen differential metabolites in this study. The significant markers leading to ascites formation and chemoresistance were screened by analyzing their correlation with the formation of ascites in ovarian cancer and the clinical indicators of patients, and then provided a theoretical basis. The results revealed that nine differential metabolites were screened out from 37 ovarian cancer tissues and their ascites, among which seven differential metabolites were screened from 22 self-paired samples. Sebacic acid and 20-COOH-leukotriene E4 were negatively correlated with the high expression of serum CA125. Carnosine was positively correlated with the high expression of serum uric acid. Hexadecanoic acid was negatively correlated with the high expression of serum γ-GGT and HBDH. 20a,22b-Dihydroxycholesterol was positively correlated with serum alkaline phosphatase and γ-GGT. In the chemotherapy-sensitive and chemotherapy-resistant ovarian cancer tissues, the differential metabolite dihydrothymine was significantly reduced in the chemotherapy-resistant group. In the ascites supernatant of the drug-resistant group, the differential metabolites, 1,25-dihydroxyvitamins D3-26, 23-lactonel and hexadecanoic acid were also significantly reduced. The results indicated that the nine differential metabolites could reflect the prognosis and the extent of liver and kidney damage in patients with ovarian cancer. Three differential metabolites with low expression in the drug-resistant group were proposed as new markers of chemotherapy efficacy in ovarian cancer patients with ascites.

Detection and verification of coexisting diagnostic markers in plasma and serum of patients with non-small-cell lung cancer

Aim: To screen and identify the potential biomarkers co-existing in plasma and serum of patients with non-small-cell lung cancer (NSCLC), and establish appropriate diagnostic models. Methods: A cohort of 195 plasma samples and 180 serum samples were obtained from healthy controls (HCs), adenocarcinoma (AdC) and squamous cell carcinoma (SqCC) patients enrolled from the First Affiliated Hospital of Nanjing Medical University. Metabolites in plasma and serum were analyzed by GC-MS. Results: Hypoxanthine was found to have good performance in the differential diagnosis of NSCLC (including AdC and SqCC) and HC (area under the receiver operating characteristic [AUROC] ≥0.85). Combinations of metabolites could be used for differential diagnosis of NSCLC and HC (AUROC >0.93), AdC and HC (AUROC >0.91), SqCC and HC (AUROC >0.95), AdC and SqCC (AUROC >0.72). Conclusions: Metabolomics based on GC-MS can screen and identify the differential metabolites coexisting in plasma and serum of patients with NSCLC, and prediction models established by this method can be used for the differential diagnosis of patients with NSCLC.

Metabolomic Profiling in Lung Cancer: A Systematic Review

Lung cancer continues to be a significant burden worldwide and remains the leading cause of cancer-associated mortality. Two considerable challenges posed by this disease are the diagnosis of 61% of patients in advanced stages and the reduced five-year survival rate of around 4%. Noninvasively collected samples are gaining significant interest as new areas of knowledge are being sought and opened up. Metabolomics is one of these growing areas. In recent years, the use of metabolomics as a resource for the study of lung cancer has been growing. We conducted a systematic review of the literature from the past 10 years in order to identify some metabolites associated with lung cancer. More than 150 metabolites have been associated with lung cancer-altered metabolism. These were detected in different biological samples by different metabolomic analytical platforms. Some of the published results have been consistent, showing the presence/alteration of specific metabolites. However, there is a clear variability due to lack of a full clinical characterization of patients or standardized patients selection. In addition, few published studies have focused on the added value of the metabolomic profile as a means of predicting treatment response for lung cancer. This review reinforces the need for consistent and systematized studies, which will help make it possible to identify metabolic biomarkers and metabolic pathways responsible for the mechanisms that promote tumor progression, relapse and eventually resistance to therapy.

Tryptophan and Its Metabolites in Lung Cancer: Basic Functions and Clinical Significance

Lung cancer is the most lethal malignancy worldwide. Recently, it has been recognized that metabolic reprogramming is a complex and multifaceted factor, contributing to the process of lung cancer. Tryptophan (Try) is an essential amino acid, and Try and its metabolites can regulate the progression of lung cancer. Here, we review the pleiotropic functions of the Try metabolic pathway, its metabolites, and key enzymes in the pathogenic process of lung cancer, including modulating the tumor environment, promoting immune suppression, and drug resistance. We summarize the recent advance in therapeutic drugs targeting the Try metabolism and kynurenine pathway and their clinical trials.

High-Throughput Salivary Metabolite Profiling on an Ultralow Noise Tip-Enhanced Laser Desorption Ionization Mass Spectrometry Platform for Noninvasive Diagnosis of Early Lung Cancer

Lung cancer (LC) is a widespread cancer that is the cause of the highest mortality rate accounting for 25% of all cancer deaths. To date, most LC patients are diagnosed at the advanced stage owing to the lack of obvious symptoms in the early stage and the limitations of current clinical diagnostic techniques. Therefore, developing a high throughput technique for early screening is of great importance. In this work, we established an effective and rapid salivary metabolic analysis platform for early LC diagnosis and combined metabolomics and transcriptomics to reveal the metabolic fluctuations correlated to LC. Saliva samples were collected from a total of 150 volunteers including 89 patients with early LC, 11 patients with advanced LC, and 50 healthy controls. The metabolic profiling of noninvasive samples was investigated on an ultralow noise TELDI-MS platform. In addition, data normalization methods were screened and assessed to overcome the MS signal variation caused by individual difference for biomarker mining. For untargeted metabolic profiling of saliva samples, around 264 peaks could be reliably detected in each sample. After multivariate analysis, 23 metabolites were sorted out and verified to be related to the dysfunction of the amino acid and nucleotide metabolism in early LC. Notably, transcriptomic data from online TCGA repository were utilized to support findings from the salivary metabolomics experiment, including the disorder of amino acid biosynthesis and amino acid metabolism. Based on the verified differential metabolites, early LC patients could be clearly distinguished from healthy controls with a sensitivity of 97.2% and a specificity of 92%. The ultralow noise TELDI-MS platform displayed satisfactory ability to explore salivary metabolite information and discover potential biomarkers that may help develop a noninvasive screening tool for early LC.

The Ability of Metabolomics to Discriminate Non-Small-Cell Lung Cancer Subtypes Depends on the Stage of the Disease and the Type of Material Studied

Identification of the NSCLC subtype at an early stage is still quite sophisticated. Metabolomics analysis of tissue and plasma of NSCLC patients may indicate new, and yet unknown, metabolic pathways active in the NSCLC. Our research characterized the metabolomics profile of tissue and plasma of patients with early and advanced NSCLC stage. Samples were subjected to thorough metabolomics analyses using liquid chromatography-mass spectrometry (LC-MS) technique. Tissue and/or plasma samples from 137 NSCLC patients were analyzed. Based on the early stage tissue analysis, more than 200 metabolites differentiating adenocarcinoma (ADC) and squamous cell lung carcinoma (SCC) subtypes as well as normal tissue, were identified. Most of the identified metabolites were amino acids, fatty acids, carnitines, lysoglycerophospholipids, sphingomyelins, plasmalogens and glycerophospholipids. Moreover, metabolites related to N-acyl ethanolamine (NAE) biosynthesis, namely glycerophospho (N-acyl) ethanolamines (GP-NAE), which discriminated early-stage SCC from ADC, have also been identified. On the other hand, the analysis of plasma of chronic obstructive pulmonary disease (COPD) and NSCLC patients allowed exclusion of the metabolites related to the inflammatory state in lungs and the identification of compounds (lysoglycerophospholipids, glycerophospholipids and sphingomyelins) truly characteristic to cancer. Our results, among already known, showed novel, thus far not described, metabolites discriminating NSCLC subtypes, especially in the early stage of cancer. Moreover, the presented results also indicated the activity of new metabolic pathways in NSCLC. Further investigations on the role of NAE biosynthesis pathways in the early stage of NSCLC may reveal new prognostic and diagnostic targets.

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.

Metabolomic profiling for second primary lung cancer: A pilot case-control study

OBJECTIVES

Lung cancer survivors have a high risk of developing a second primary lung cancer (SPLC). While national screening guidelines have been established for initial primary lung cancer (IPLC), no consensus guidelines exist for SPLC. Furthermore, the factors that contribute to SPLC risk have not been established. This study examines the potential for using serum metabolomics to identify metabolite biomarkers that differ between SPLC cases and IPLC controls.

MATERIAL AND METHODS

In this pilot case-control study, we applied an untargeted metabolomics approach based on ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) to serum samples of 82 SPLC cases and 82 frequency matched IPLC controls enrolled in the Boston Lung Cancer Study. Random forest and unconditional logistic regression models identified metabolites associated with SPLC. Candidate metabolites were integrated into a SPLC risk prediction model and the model performance was evaluated through a risk stratification approach.

RESULTS

The untargeted analysis detected 1008 named and 316 unnamed metabolites among all study participants. Metabolites that were significantly associated with SPLC (False Discovery Rate q-value < 0.2) included 5-methylthioadenosine (odds ratio [OR] = 2.04, 95 % confidence interval [CI] 1.39-3.01; P = 2.8 × 10^-4) and phenylacetylglutamine (OR = 2.65, 95 % CI 1.56-4.51; P = 3.2 × 10^-4), each exhibiting approximately 1.5-fold increased levels among SPLC cases versus IPLC controls. In stratifying the study participants across quartiles of estimated SPLC risk, the risk prediction model identified a significantly higher proportion of SPLC cases in the fourth compared to the first quartile (68.3 % versus 39.0 %; P = 0.044).

CONCLUSION

SPLC cases may have distinct metabolomic profiles compared to those in IPLC patients without SPLC. A risk stratification approach integrating metabolomics may be useful for distinguishing patients based on SPLC risk. Prospective validation studies are needed to further evaluate the potential for leveraging metabolomics in SPLC surveillance and screening.

Long non-coding RNA FOXD1-AS1 promotes the progression and glycolysis of nasopharyngeal carcinoma by sustaining FOXD1 expression

Long non-coding RNAs (lncRNAs) play a vital role in the progression of several cancers, including nasopharyngeal carcinoma (NPC). However, the mechanism of lncRNA involvement in the progression of NPC remains to be elucidated. Hence, we conducted in vivo and in vitro experiments to determine the molecular mechanism of FOXD1-AS1. We found that FOXD1-AS1 was over-expressed in NPC cells and tissues, and was significantly associated with poor survival rate in patients with NPC. We also found that FOXD1-AS1 promotes cellular proliferation, migration, invasion, and glycolysis, and inhibits apoptosis by upregulating the expression of FOXD1. Furthermore, FOXD1 could transcriptionally up-regulate the expression of key glycolytic genes to promote the glycolysis levels of NPC. The identified FOXD1-AS1 may serve as a potential prognostic biomarker and therapeutic target for patients with NPC.

Comparison of the ischemic and non-ischemic lung cancer metabolome reveals hyper activity of the TCA cycle and autophagy

Recent advances in cancer biology reveal the importance of metabolic changes in cancer; however, less is known about how metabolic pathways in tumors are regulated in vivo. Here, we report analysis of the lung cancer metabolism based on different surgical procedures, namely lobectomy and partial resection. In lobectomy, but not in partial resection, pulmonary arteries and veins are ligated prior to removal of tissues, rendering tissues ischemic. We show that tumors indeed undergo ischemia upon lobectomy and that the tumor metabolome differs markedly from that of tumors removed by partial resection. Comparison of the responses to ischemia in tumor and normal lung tissues revealed that lung cancer tissue exhibits greater TCA cycle and autophagic activity than do normal lung tissues in vivo in patients. Finally, we report that deleting ATG7, which encodes a protein essential for autophagy, antagonizes growth of tumors derived from lung cancer cell lines, suggesting that autophagy confers metabolic advantages to lung cancer. Our findings shed light on divergent metabolic responses to ischemia seen in tumors and normal tissues.

Liquid Chromatography-Mass Spectrometry-Based Tissue Metabolic Profiling Reveals Major Metabolic Pathway Alterations and Potential Biomarkers of Lung Cancer

Unclarified molecular mechanism and lack of practical diagnosis biomarkers hinder the effective treatment of non-small-cell lung cancer. Herein, we performed liquid chromatography-mass spectrometry-based nontargeted metabolomics analysis in 131 patients with their lung tissue pairs to study the metabolic characteristics and disordered metabolic pathways in lung tumor. A total of 339 metabolites were identified in metabolic profiling. Also, 241 differential metabolites were found between lung carcinoma tissues (LCTs) and paired distal noncancerous tissues; amino acids, purine metabolites, fatty acids, phospholipids, and most of lysophospholipids significantly increased in LCTs, while 3-phosphoglyceric acid, phosphoenolpyruvate, 6-phosphogluconate, and citrate decreased. Additionally, pathway enrichment analysis revealed that energy, purine, amino acid, lipid, and glutathione metabolism are markedly disturbed in lung cancer (LCa). Using binary logistic regression, we further defined candidate biomarkers for different subtypes of lung tumor. Xanthine and PC 35:2 were selected as combinational biomarkers for distinguishing benign from malignant lung tumors with a 0.886 area under curve (AUC) value, and creatine, myoinositol and LPE 16:0 were defined as combinational biomarkers for discriminating adenocarcinoma from squamous cell lung carcinoma with a 0.934 AUC value. Overall, metabolic characterization and pathway disturbance demonstrated apparent metabolic reprogramming in LCa. The defined candidate metabolite marker panels are useful for subtyping of lung tumors to assist clinical diagnosis.

Emerging Roles and Therapeutic Interventions of Aerobic Glycolysis in Glioma

Glioma is the most common type of intracranial malignant tumor, with a great recurrence rate due to its infiltrative growth, treatment resistance, intra- and intertumoral genetic heterogeneity. Recently, accumulating studies have illustrated that activated aerobic glycolysis participated in various cellular and clinical activities of glioma, thus influencing the efficacy of radiotherapy and chemotherapy. However, the glycolytic process is too complicated and ambiguous to serve as a novel therapy for glioma. In this review, we generalized the implication of key enzymes, glucose transporters (GLUTs), signalings and transcription factors in the glycolytic process of glioma. In addition, we summarized therapeutic interventions via the above aspects and discussed promising clinical applications for glioma.

Early diagnosis and screening in lung cancer

Lung cancer is the third most diagnosed cancer, but the first cause of cancer-related deaths worldwide. This rather high death rate is due mainly to the fact that most patients are diagnosed with advanced-stage cancer, for which the conventional treatment does not work. The most used screening method for lung cancer is a low-dose CT scan, but it is recommended for specific age populations and it also started different debates on its advantages for lung cancer diagnosis. Over the year, several new techniques have been developed that are less invasive, have lower side effect, and can be implemented at all types of populations. This article aimed to present the advantages and disadvantages of using several methods for lung cancer diagnosis, including analysis of volatile organic compounds, exhaled breath condensate analysis and specific genomic approaches.

Glycine metabolomic changes induced by anticancer agents in A549 cells

Glycine plays a key role in rapidly proliferating cancer cells such as A549 cells. Targeting glycine metabolism is considered as a potential means for cancer treatment. However, the drug-induced alterations in glycine metabolism have not yet been investigated. Herein, a total of 34 glycine metabolites were examined in A549 cells with or without anticancer drug treatment. This work showed all tested anticancer agents could alter glycine metabolism in A549 cells including inhibition of pyruvate metabolism and down-regulation of betaine aldehyde and 5'-phosphoribosylglycinamide. Principal component analysis and orthogonal partial least-squares discrimination analysis exhibited the difference between control and each drug-treated group. In general, cisplatin, camptothecin, and SAHA could induce the significant down-regulation of more metabolites, compared with afatinib, gefitinib, and targretin. Both glycine, serine and threonine metabolism, and purine metabolism were significantly disturbed by the treatment with afatinib, gefitinib, and targretin. However, the treatment using cisplatin, camptothecin, and SAHA was considered to be highly responsible for the perturbation of glycine, serine and threonine metabolism, and cysteine and methionine metabolism. Finally, multivariate analysis for control and all drug-treated groups revealed 11 altered metabolites with a significant difference. It implies anti-cancer agents with different mechanisms of action might induce different comprehensive changes of glycine metabolomics. The current study provides fundamental insights into the acquisition of the role of anti-cancer agents in glycine metabolism while suppressing cancer cell proliferation, and may aid the development of cancer treatment targeting glycine metabolism.

Non-coding RNAs: Key regulators of aerobic glycolysis in breast cancer

Although extensive research progress has been made in breast cancer in recent years, yet the morbidity and mortality rates of breast cancer are rising, making it the major disease that endangers women's health. Energy metabolism reprogramming is featured by a state termed "aerobic glycolysis" or the Warburg effect that glycolysis is preferred even under aerobic conditions in neoplastic diseases. Widely acknowledged as an emerging hallmark in cancers, this metabolic switch shows a sophisticated role in the pathogenesis of breast cancer. The regulating effect of non-coding RNAs (ncRNAs) composed of microRNAs, long non-coding RNAs and circular RNAs is closely related to the glycolysis in breast cancer. Therefore, understand the mechanisms of ncRNAs of aerobic glycolysis in breast cancer may provide new strategy for the disease.

Dihydropyrimidinase protects from DNA replication stress caused by cytotoxic metabolites

Imbalance in the level of the pyrimidine degradation products dihydrouracil and dihydrothymine is associated with cellular transformation and cancer progression. Dihydropyrimidines are degraded by dihydropyrimidinase (DHP), a zinc metalloenzyme that is upregulated in solid tumors but not in the corresponding normal tissues. How dihydropyrimidine metabolites affect cellular phenotypes remains elusive. Here we show that the accumulation of dihydropyrimidines induces the formation of DNA-protein crosslinks (DPCs) and causes DNA replication and transcriptional stress. We used Xenopus egg extracts to recapitulate DNA replication invitro. We found that dihydropyrimidines interfere directly with the replication of both plasmid and chromosomal DNA. Furthermore, we show that the plant flavonoid dihydromyricetin inhibits human DHP activity. Cellular exposure to dihydromyricetin triggered DPCs-dependent DNA replication stress in cancer cells. This study defines dihydropyrimidines as potentially cytotoxic metabolites that may offer an opportunity for therapeutic-targeting of DHP activity in solid tumors.

Emerging roles for UDP-glucuronosyltransferases in drug resistance and cancer progression

The best-known role of UDP-glucuronosyltransferase enzymes (UGTs) in cancer is the metabolic inactivation of drug therapies. By conjugating glucuronic acid to lipophilic drugs, UGTs impair the biological activity and enhance the water solubility of these agents, driving their elimination. Multiple clinical observations support an expanding role for UGTs as modulators of the drug response and in mediating drug resistance in numerous cancer types. However, accumulating evidence also suggests an influence of the UGT pathway on cancer progression. Dysregulation of the expression and activity of UGTs has been associated with the progression of several cancers, arguing for UGTs as possible mediators of oncogenic pathways and/or disease accelerators in a drug-naive context. The consequences of altered UGT activity on tumour biology are incompletely understood. They might be associated with perturbed levels of bioactive endogenous metabolites such as steroids and bioactive lipids that are inactivated by UGTs or through non-enzymatic mechanisms, thereby eliciting oncogenic signalling cascades. This review highlights the evidence supporting dual roles for the UGT pathway, affecting cancer progression and drug resistance. Pharmacogenomic testing of UGT profiles in patients and the development of therapeutic options that impair UGT actions could provide useful prognostic and predictive biomarkers and enhance the efficacy of anti-cancer drugs.

Compartment and hub definitions tune metabolic networks for metabolomic interpretations

BACKGROUND

Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism's cells. They offer biological context in which to integrate, analyze, and interpret omic measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments.

RESULTS

We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular, whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from 5 studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications.

CONCLUSIONS

Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in omic measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of individual genes, transcripts, and proteins.

De novo synthesis of serine and glycine fuels purine nucleotide biosynthesis in human lung cancer tissues

Nucleotide synthesis is essential to proliferating cells, but the preferred precursors for de novo biosynthesis are not defined in human cancer tissues. We have employed multiplexed stable isotope-resolved metabolomics to track the metabolism of [¹³C₆]glucose, D₂-glycine, [¹³C₂]glycine, and D₃-serine into purine nucleotides in freshly resected cancerous and matched noncancerous lung tissues from nonsmall cell lung cancer (NSCLC) patients, and we compared the metabolism with established NSCLC PC9 and A549 cell lines in vitro Surprisingly, [¹³C₆]glucose was the best carbon source for purine synthesis in human NSCLC tissues, in contrast to the noncancerous lung tissues from the same patient, which showed lower mitotic indices and MYC expression. We also observed that D₃-Ser was preferentially incorporated into purine rings over D₂-glycine in both tissues and cell lines. MYC suppression attenuated [¹³C₆]glucose, D₃-serine, and [¹³C₂]glycine incorporation into purines and reduced proliferation in PC9 but not in A549 cells. Using detailed kinetic modeling, we showed that the preferred use of glucose as a carbon source for purine ring synthesis in NSCLC tissues involves cytoplasmic activation/compartmentation of the glucose-to-serine pathway and enhanced reversed one-carbon fluxes that attenuate exogenous serine incorporation into purines. Our findings also indicate that the substrate for de novo nucleotide synthesis differs profoundly between cancer cell lines and fresh human lung cancer tissues; the latter preferred glucose to exogenous serine or glycine but not the former. This distinction in substrate utilization in purine synthesis in human cancer tissues should be considered when targeting one-carbon metabolism for cancer therapy.

Cheminformatics approach to exploring and modeling trait-associated metabolite profiles

Developing predictive and transparent approaches to the analysis of metabolite profiles across patient cohorts is of critical importance for understanding the events that trigger or modulate traits of interest (e.g., disease progression, drug metabolism, chemical risk assessment). However, metabolites’ chemical structures are still rarely used in the statistical modeling workflows that establish these trait-metabolite relationships. Herein, we present a novel cheminformatics-based approach capable of identifying predictive, interpretable, and reproducible trait-metabolite relationships. As a proof-of-concept, we utilize a previously published case study consisting of metabolite profiles from non-small-cell lung cancer (NSCLC) adenocarcinoma patients and healthy controls. By characterizing each structurally annotated metabolite using both computed molecular descriptors and patient metabolite concentration profiles, we show that these complementary features enhance the identification and understanding of key metabolites associated with cancer. Ultimately, we built multi-metabolite classification models for assessing patients’ cancer status using specific groups of metabolites identified based on high structural similarity through chemical clustering. We subsequently performed a metabolic pathway enrichment analysis to identify potential mechanistic relationships between metabolites and NSCLC adenocarcinoma. This cheminformatics-inspired approach relies on the metabolites’ structural features and chemical properties to provide critical information about metabolite-trait associations. This method could ultimately facilitate biological understanding and advance research based on metabolomics data, especially with respect to the identification of novel biomarkers.

Accounting for biological variation with linear mixed-effects modelling improves the quality of clinical metabolomics data

Metabolite profiles from biological samples suffer from both technical variations and subject-specific variants. To improve the quality of metabolomics data, conventional data processing methods can be employed to remove technical variations. These methods do not consider sources of subject variation as separate factors from biological factors of interest. This can be a significant issue when performing quantitative metabolomics in clinical trials or screening for a potential biomarker in early-stage disease, because changes in metabolism or a desired-metabolite signal are small compared to the total metabolite signals. As a result, inter-individual variability can interfere subsequent statistical analyses. Here, we propose an additional data processing step using linear mixed-effects modelling to readjust an individual metabolite signal prior to multivariate analyses. Published clinical metabolomics data was used to demonstrate and evaluate the proposed method. We observed a substantial reduction in variation of each metabolite signal after model fitting. A comparison with other strategies showed that our proposed method contributed to improved classification accuracy, precision, sensitivity and specificity. Moreover, we highlight the importance of patient metadata as it contains rich information of subject characteristics, which can be used to model and normalize metabolite abundances. The proposed method is available as an R package lmm2met.

Are There New Biomarkers in Tissue and Liquid Biopsies for the Early Detection of Non-Small Cell Lung Cancer?

Lung cancer is one of the most lethal malignancies worldwide, mainly due to its late diagnoses. The detection of molecular markers on samples provided from routine bronchoscopy including several liquid-based cytology tests (e.g., bronchoaspirate, bronchoalveolar lavage) and/or on easily obtained specimens such as sputum could represent a new approach to improve the sensitivity in lung cancer diagnoses. Recently growing interest has been reported for "noninvasive" liquid biopsy as a valuable source for molecular profiling. Unfortunately, a biomarker and/or composition of biomarkers capable of detecting early-stage lung cancer has yet to be discovered even if in the last few years there has been, through the use of revolutionary new technologies, an explosion of lung cancer biomarkers. Assay sensitivity and specificity need to be improved particularly when new approaches and/or tools are used. We have focused on the most important markers detected in tissue, and on several cytological specimens and liquid biopsies overall.

Human-like hyperplastic prostate with low ZIP1 induced solely by Zn deficiency in rats

Prostate cancer is a leading cause of cancer death in men over 50 years of age, and there is a characteristic marked decrease in Zn content in the malignant prostate cells. The cause and consequences of this loss have thus far been unknown. We found that in middle-aged rats a Zn-deficient diet reduces prostatic Zn levels (P = 0.025), increases cellular proliferation, and induces an inflammatory phenotype with COX-2 overexpression. This hyperplastic/inflammatory prostate has a human prostate cancer-like microRNA profile, with up-regulation of the Zn-homeostasis-regulating miR-183-96-182 cluster (fold change = 1.41-2.38; P = 0.029-0.0003) and down-regulation of the Zn importer ZIP1 (target of miR-182), leading to a reduction of prostatic Zn. This inverse relationship between miR-182 and ZIP1 also occurs in human prostate cancer tissue, which is known for Zn loss. The discovery that the Zn-depleted middle-aged rat prostate has a metabolic phenotype resembling that of human prostate cancer, with a 10-fold down-regulation of citric acid (P = 0.0003), links citrate reduction directly to prostatic Zn loss, providing the underlying mechanism linking dietary Zn deficiency with miR-183-96-182 overexpression, ZIP1 down-regulation, prostatic Zn loss, and the resultant citrate down-regulation, changes mimicking features of human prostate cancer. Thus, dietary Zn deficiency during rat middle age produces changes that mimic those of human prostate carcinoma and may increase the risk for prostate cancer, supporting the need for assessment of Zn supplementation in its prevention.

Umbilical cord blood metabolomics reveal distinct signatures of dyslipidemia prior to bronchopulmonary dysplasia and pulmonary hypertension

Pulmonary hypertension (PH) is a common consequence of bronchopulmonary dysplasia (BPD) and remains a primary contributor to increased morbidity and mortality among preterm infants. Unfortunately, at the present time, there are no reliable early predictive markers for BPD-associated PH. Considering its health consequences, understanding in utero perturbations that lead to the development of BPD and BPD-associated PH and identifying early predictive markers is of utmost importance. As part of the discovery phase, we applied a multiplatform metabolomics approach consisting of untargeted and targeted methodologies to screen for metabolic perturbations in umbilical cord blood (UCB) plasma from preterm infants that did ( n = 21; cases) or did not ( n = 21; controls) develop subsequent PH. A total of 1,656 features were detected, of which 407 were annotated by metabolite structures. PH-associated metabolic perturbations were characterized by reductions in major choline-containing phospholipids, such as phosphatidylcholines and sphingomyelins, indicating altered lipid metabolism. The reduction in UCB abundances of major choline-containing phospholipids was confirmed in an independent validation cohort consisting of UCB plasmas from 10 cases and 10 controls matched for gestational age and BPD status. Subanalyses in the discovery cohort indicated that elevations in the oxylipins PGE1, PGE2, PGF2a, 9- and 13-HOTE, 9- and 13-HODE, and 9- and 13-KODE were positively associated with BPD presence and severity. This expansive evaluation of cord blood plasma identifies compounds reflecting dyslipidemia and suggests altered metabolite provision associated with metabolic immaturity that differentiate subjects, both by BPD severity and PH development.