Urinary metabolomic signature of esophageal cancer and Barrett's esophagus

Microbiome and metabolic features of tissues and feces reveal diagnostic biomarkers for colorectal cancer

Microbiome and their metabolites are increasingly being recognized for their role in colorectal cancer (CRC) carcinogenesis. Towards revealing new CRC biomarkers, we compared 16S rRNA gene sequencing and liquid chromatography-mass spectrometry (LC-MS) metabolite analyses in 10 CRC (T_CRC) and normal paired tissues (T_HC) along with 10 matched fecal samples (F_CRC) and 10 healthy controls (F_HC). The highest microbial phyla abundance from T_HC and T_CRC were Firmicutes, while the dominant phyla from F_HC and F_CRC were Bacteroidetes, with 72 different microbial genera identified among four groups. No changes in Chao1 indices were detected between tissues or between fecal samples whereas non-metric multidimensional scaling (NMDS) analysis showed distinctive clusters among fecal samples but not tissues. LEfSe analyses indicated Caulobacterales and Brevundimonas were higher in T_HC than in T_CRC, while Burkholderialese, Sutterellaceaed, Tannerellaceaea, and Bacteroidaceae were higher in F_HC than in F_CRC. Microbial association networks indicated some genera had substantially different correlations. Tissue and fecal analyses indicated lipids and lipid-like molecules were the most abundant metabolites detected in fecal samples. Moreover, partial least squares discriminant analysis (PLS-DA) based on metabolic profiles showed distinct clusters for CRC and normal samples with a total of 102 differential metabolites between T_HC and T_CRC groups and 700 metabolites different between F_HC and F_CRC groups. However, only Myristic acid was detected amongst all four groups. Highly significant positive correlations were recorded between genus-level microbiome and metabolomics data in tissue and feces. And several metabolites were associated with paired microbes, suggesting a strong microbiota-metabolome coupling, indicating also that part of the CRC metabolomic signature was attributable to microbes. Suggesting utility as potential biomarkers, most such microbiome and metabolites showed directionally consistent changes in CRC patients. Nevertheless, further studies are needed to increase sample sizes towards verifying these findings.

Succinate at the Crossroad of Metabolism and Angiogenesis: Roles of SDH, HIF1α and SUCNR1

Angiogenesis is an essential process by which new blood vessels develop from existing ones. While adequate angiogenesis is a physiological process during, for example, tissue repair, insufficient and excessive angiogenesis stands on the pathological side. Fine balance between pro- and anti-angiogenic factors in the tissue environment regulates angiogenesis. Identification of these factors and how they function is a pressing topic to develop angiogenesis-targeted therapeutics. During the last decade, exciting data highlighted non-metabolic functions of intermediates of the mitochondrial Krebs cycle including succinate. Among these functions is the contribution of succinate to angiogenesis in various contexts and through different mechanisms. As the concept of targeting metabolism to treat a wide range of diseases is rising, in this review we summarize the mechanisms by which succinate regulates angiogenesis in normal and pathological settings. Gaining a comprehensive insight into how this metabolite functions as an angiogenic signal will provide a useful approach to understand diseases with aberrant or excessive angiogenic background, and may provide strategies to tackle them.

Untargeted serum metabolomics reveals potential biomarkers and metabolic pathways associated with esophageal cancer

Metabolomics has been reported as an efficient tool to screen biomarkers that are related to esophageal cancer. However, the metabolic biomarkers identifying malignant degrees and therapeutic efficacy are still largely unknown in the disease. Here, GC-MS-based metabolomics was used to understand metabolic alteration in 137 serum specimens from patients with esophageal cancer, which is approximately two- to fivefold as many plasma specimens as the previous reports. The elevated amino acid metabolism is in sharp contrast to the reduced carbohydrate as a characteristic feature of esophageal cancer. Comparative metabolomics showed that most metabolic differences were determined between the early stage (0–II) and the late stage (III and IV) among the 0–IV stages of esophageal cancer and between patients who received treatment and those who did not receive treatment. Glycine, serine, and threonine metabolism and glycine were identified as the potentially overlapped metabolic pathway and metabolite, respectively, in both disease progress and treatment effect. Glycine, fructose, ornithine, and threonine can be a potential array for the evaluation of disease prognosis and therapy in esophageal cancer. These results highlight the means of identifying previously unknown biomarkers related to esophageal cancer by a metabolomics approach.

NMR metabolomic and microarray-based transcriptomic data integration identifies unique molecular signatures of hypersensitivity pneumonitis

Hypersensitivity pneumonitis (HP) is an immune-mediated granulomatous interstitial lung disease (ILD) that results from repeated inhalation of certain antigens. Despite major advances in research, pathophysiology of the disease remains poorly understood. The present study combines metabolomic and transcriptomic data to determine alterations in HP subjects as compared with healthy controls. Metabolic signatures were identified in serum, exhaled breath condensate (EBC) and bronchoalveolar lavage fluid (BALF) of HP patients using proton nuclear magnetic resonance (NMR) metabolomics. The expression of three metabolites, i.e., lactate, pyruvate, and proline, was found to be significantly altered in all three biofluids. The potential of differential diagnosis based on these three metabolites was investigated by including a group of patients with sarcoidosis, which is another type of granulomatous ILD. In addition, differentially expressed transcriptomic fingerprints in blood samples were identified by analyzing a Gene Expression Omnibus (GEO) database. The transcriptomics analysis of these microarray-based data revealed 59 genes to be significantly dysregulated in patients with HP. Over representation analysis of the metabolites and genes of interest was performed using IMPaLA (Integrated Molecular Pathway Level Analysis) version 12. Integrated analysis of serum metabolite signatures and blood gene expression suggests dysregulation of PI3K-AKT signaling and TCA cycle pathways in these patients. This preliminary study is a step towards better understanding of the pathogenesis of HP by identification of differentially expressed metabolites and transcriptomic fingerprints. These molecular signatures may be explored as diagnostic markers for differentiating HP from other lung diseases.

A systematic review of metabolomic profiling of gastric cancer and esophageal cancer

Objective: Upper gastrointestinal (UGI) cancers, predominantly gastric cancer (GC) and esophageal cancer (EC), are malignant tumor types with high morbidity and mortality rates. Accumulating studies have focused on metabolomic profiling of UGI cancers in recent years. In this systematic review, we have provided a collective summary of previous findings on metabolites and metabolomic profiling associated with GC and EC.

Methods: A systematic search of three databases (Embase, PubMed, and Web of Science) for molecular epidemiologic studies on the metabolomic profiles of GC and EC was conducted. The Newcastle–Ottawa Scale (NOS) was used to assess the quality of the included articles.

Results: A total of 52 original studies were included for review. A number of metabolites were differentially distributed between GC and EC cases and non-cases, including those involved in glycolysis, anaerobic respiration, tricarboxylic acid cycle, and protein and lipid metabolism. Lactic acid, glucose, citrate, and fumaric acid were among the most frequently reported metabolites of cellular respiration while glutamine, glutamate, and valine were among the most commonly reported amino acids. The lipid metabolites identified previously included saturated and unsaturated free fatty acids, aldehydes, and ketones. However, the key findings across studies to date have been inconsistent, potentially due to limited sample sizes and the majority being hospital-based case-control analyses lacking an independent replication group.

Conclusions: Studies on metabolomics have thus far provided insights into etiological factors and biomarkers for UGI cancers, supporting the potential of applying metabolomic profiling in cancer prevention and management efforts.

Biodiscovery of Potential Antibacterial Diagnostic Metabolites from the Endolichenic Fungus Xylaria venustula Using LC-MS-Based Metabolomics

Simple Summary

In this study, we determined the bioactivities and chemical natures of three species of lichen Usnea and their associated endolichenic fungi (ELF) through metabolomics. We found significant differences in the antibacterial activities and the metabolites produced by the host lichen and its ELF, with the latter targeting a wider scope of organisms. We also discovered potential key metabolites produced by ELF that are yet to be reported. This study shows the application of metabolomics in rapidly identifying bioactive metabolites that are of significance in the discovery of new drugs.

Abstract

Three species of the lichen Usnea (U. baileyi (Stirt.) Zahlbr., U. bismolliuscula Zahlbr. and U. pectinata Stirt.) and nine associated endolichenic fungi (ELF) were evaluated using a metabolomics approach. All investigated lichen crude extracts afforded antibacterial activity against Staphylococcus aureus (minimum inhibitory concentration (MIC): 0.0625 mg/mL), but none was observed against Escherichia coli, while the ELF extract Xylaria venustula was found to be the most active against S. aureus (MIC: 2.5 mg/mL) and E. coli (MIC: 5 mg/mL). X. venustula was fractionated and tested for to determine its antibacterial activity. Fractions XvFr1 to 5 displayed bioactivities against both test bacteria. Selected crude extracts and fractions were subjected to metabolomics analyses using high-resolution LC–MS. Multivariate analyses showed the presence of five secondary metabolites unique to bioactive fractions XvFr1 to 3, which were identified as responsible for the antibacterial activity of X. venustula. The p-values of these metabolites were at the margin of significance level, with methyl xylariate C (P_60) being the most significant. However, their high variable importance of projection (VIP) scores (>5) suggest these metabolites are potential diagnostic metabolites for X. venustula for “dual” bioactivity against S. aureus and E. coli. The statistical models also showed the distinctiveness of metabolites produced by lichens and ELF, thus supporting our hypotheses of ELF functionality similar to plant endophytes.

Metabolomic Profiling for Diagnosis and Prognostication in Surgery: A Scoping Review

OBJECTIVE

This review assimilates and critically evaluates available literature regarding the use of metabolomic profiling in surgical decision-making.

BACKGROUND

Metabolomic profiling is performed by nuclear magnetic resonance spectroscopy or mass spectrometry of biofluids and tissues to quantify biomarkers (ie, sugars, amino acids, and lipids), producing diagnostic and prognostic information that has been applied among patients with cardiovascular disease, inflammatory bowel disease, cancer, and solid organ transplants.

METHODS

PubMed was searched from 1995 to 2019 to identify studies investigating metabolomic profiling of surgical patients. Articles were included and assimilated into relevant categories per PRISMA-ScR guidelines. Results were summarized with descriptive analytical methods.

RESULTS

Forty-seven studies were included, most of which were retrospective studies with small sample sizes using various combinations of analytic techniques and types of biofluids and tissues. Results suggest that metabolomic profiling has the potential to effectively screen for surgical diseases, suggest diagnoses, and predict outcomes such as postoperative complications and disease recurrence. Major barriers to clinical adoption include a lack of high-level evidence from prospective studies, heterogeneity in study design regarding tissue and biofluid procurement and analytical methods, and the absence of large, multicenter metabolome databases to facilitate systematic investigation of the efficacy, reproducibility, and generalizability of metabolomic profiling diagnoses and prognoses.

CONCLUSIONS

Metabolomic profiling research would benefit from standardization of study design and analytic approaches. As technologies improve and knowledge garnered from research accumulates, metabolomic profiling has the potential to provide personalized diagnostic and prognostic information to support surgical decision-making from preoperative to postdischarge phases of care.

Study of the Relationship Between Serum Amino Acid Metabolism and Lymph Node Metastasis in Patients with Colorectal Cancer

Purpose

Lymph node metastasis is one of the important prognostic factors of colorectal cancer, and an important index of individualized treatment. The purpose of this study is to use metabonomics to identify potential molecular markers of lymph node metastasis in colorectal cancer (CRC).

Patients and Methods

Peripheral blood samples of 223 CRC patients were collected. The metabolic levels of amino acids and carnitine in peripheral blood of CRC patients, with and without lymph node metastasis, were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS).

Results

The results show that there were significant differences in the levels of serum amino acids and carnitine between lymph node metastatic patients and lymph node non-metastatic patients. The diagnostic model that was constructed by 9 types of serum metabolites has a high diagnostic ability.

Conclusion

LC-MS/MS is a detection method that has a broad application in predicting CRC prognosis, individualized treatment, and in studying the mechanism of lymph node metastasis.

Screening for Early Gastric Cancer Using a Noninvasive Urine Metabolomics Approach

Simple Summary

There are currently no effective specific biomarkers for the screening of early gastric cancer. Recently, metabolomics has been used to profile small endogenous metabolites, demonstrating significant potential in the diagnosis/screening of cancer, owing to its ability to conduct a noninvasive sample analysis. Here, we performed a urine metabolomics analysis in the context of an early diagnosis of gastric cancer. This approach showed very high diagnostic sensitivity and specificity and performed significantly better than the analysis of serum tumor markers modalities. An additional genomic data analysis revealed the up-regulation of several genes in gastric cancer. This metabolomics-based early diagnosis approach may have the potential for mass screening an average-risk population and may facilitate endoscopic examination through risk stratification.

Abstract

The early detection of gastric cancer (GC) could decrease its incidence and mortality. However, there are currently no accurate noninvasive markers for GC screening. Therefore, we developed a noninvasive diagnostic approach, employing urine nuclear magnetic resonance (NMR) metabolomics, to discover putative metabolic markers associated with GC. Changes in urine metabolite levels during oncogenesis were evaluated using samples from 103 patients with GC and 100 age- and sex-matched healthy controls. Approximately 70% of the patients with GC (n = 69) had stage I GC, with the majority (n = 56) having intramucosal cancer. A multivariate statistical analysis of the urine NMR data well discriminated between the patient and control groups and revealed nine metabolites, including alanine, citrate, creatine, creatinine, glycerol, hippurate, phenylalanine, taurine, and 3-hydroxybutyrate, that contributed to the difference. A diagnostic performance test with a separate validation set exhibited a sensitivity and specificity of more than 90%, even with the intramucosal cancer samples only. In conclusion, the NMR-based urine metabolomics approach may have potential as a convenient screening method for the early detection of GC and may facilitate consequent endoscopic examination through risk stratification.

Alteration of plasma metabolites associated with chemoradiosensitivity in esophageal squamous cell carcinoma via untargeted metabolomics approach

BACKGROUND

To investigate the differences in plasma metabolomic characteristics between pathological complete response (pCR) and non-pCR patients and identify biomarker candidates for predicting the response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC).

METHODS

A total of 46 ESCC patients were included in this study. Gas chromatography time-of- flight mass spectrometry (GC-TOF/MS) technology was applied to detect the plasma samples collected before nCRT via untargeted metabolomics analysis.

RESULTS

Five differentially expressed metabolites (out of 109) was found in plasma between pCR and non-pCR groups. Compared with non-pCR group, isocitric acid (p = 0.0129), linoleic acid (p = 0.0137), citric acid (p = 0.0473) were upregulated, while L-histidine (p = 0.0155), 3'4 dihydroxyhydrocinnamic acid (p = 0.0339) were downregulated in the pCR plasma samples. Pathway analyses unveiled that citrate cycle (TCA cycle), glyoxylate and dicarboxylate metabolic pathway were associated with ESCC chemoradiosensitivity.

CONCLUSION

The present study provided supporting evidence that GC-TOF/MS based metabolomics approach allowed identification of metabolite differences between pCR and non-pCR patients in plasma levels, and the systemic metabolic status of patients may reflect the response of ESCC patient to neoadjuvant chemoradiotherapy.

Intervention of Uncaria and Its Components on Liver Lipid Metabolism in Spontaneously Hypertensive Rats

Uncaria rhynchophylla (Miq.) Miq. ex Havil is widely used in the treatment of hypertension. The Uncaria extract and its bioactives, rhynchophylline and isorhynchophylline, reduced the blood pressure and fatty content in liver cells. In the present study, the antihypertensive effects of Uncaria ethanol extract (UET), rhynchophylline (RT) and isorhynchophylline (IT) were investigated in spontaneously hypertensive rats (SHR) using UPLC-Q-Orbitrap/MS based lipidomics approach. Histological changes in the liver were evaluated. Cytolysis and fatty degeneration in the liver tissues were observed in the SHR group. Lipid species in WKY, SHR treated with UET, RT, and IT were plotted to obtain the Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) score plots. Fifty-six endogenous metabolites in the liver such as glycerides, glycerophospholipids, unsaturated fatty acids, and sphingomyelins were selected as potential hypertension associated biomarkers. In order to further explore the metabolite targets of UET for antihypertensive, student's t test and correlation analysis were performed to recognize the pattern recognition and to select the significant metabolites. Similar and prolonged reduction in blood pressure was observed in all SHR groups treated with UET, RT, and IT, while the metabolite profiles were perturbed slightly compared to that of the untreated SHR. Further analysis showed that only a few common components were observed in both RT and IT, which showed similar antihypertensive effect in spite of the distinct metabolic pathways. These results help in understanding the mechanisms of isomeric ingredients in exhibiting the antihypertensive effect but with different targets.

Metabolomics technology and bioinformatics for precision medicine

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

Nuclear magnetic resonance-based metabolomics and metabolic pathway networks from patient-matched esophageal carcinoma, adjacent noncancerous tissues and urine

BACKGROUND

Several studies have demonstrated a correlation between esophageal cancer (EC) and perturbed urinary metabolomic profiles, but none has described the correlation between urine metabolite profiles and those of the tumor and adjacent esophageal mucosa in the same patient.

AIM

To investigate how urinary metabolic phenotypes were linked to the changes in the biochemical landscape of esophageal tumors.

METHODS

Nuclear magnetic resonance-based metabolomics were applied to esophageal tumor tissues and adjacent normal mucosal tissues alongside patient-matched urine samples.

RESULTS

Analysis revealed that specific metabolite changes overlapped across both metrics, including glucose, glutamate, citrate, glycine, creatinine and taurine, indicating that the networks for metabolic pathway perturbations in EC, potentially involved in but not limited to disruption of fatty acid metabolism, glucose and glycolytic metabolism, tricarboxylic acid cycle and glutaminolysis. Additionally, changes in most urinary biomarkers correlated with changes in biomarker candidates in EC tissues, implying enhanced energy production for rapid cell proliferation.

CONCLUSION

Overall, these associations provide evidence for distinct metabolic signatures and pathway disturbances between the tumor tissues and urine of EC patients, and changes in urinary metabolic signature could reflect reprogramming of the aforementioned metabolic pathways in EC tissues. Further investigation is needed to validate these initial findings using larger samples and to establish the underlying mechanism of EC progression.

Developing a blood-based gene mutation assay as a novel biomarker for oesophageal adenocarcinoma

The Phosphatidylinositol glycan class A (PIG-A) gene mutation assay phenotypically measures erythrocyte mutations, assessed here for their correlation to neoplastic progression in the gastro-oesophageal reflux disease (GORD)-Barrett’s metaplasia (BM)-oesophageal adenocarcinoma (OAC) model. Endoscopy patients underwent venipuncture and erythrocytes fluorescently stained for glycosyl phosphatidylinositol (GPI)–anchored proteins; CD55 and CD59. Using flow cytometry, GPI–anchor negative erythrocytes (mutants) were scored and compared amongst groups. The study enlisted 200 patients and 137 healthy volunteers. OAC patients had a three–fold increase in erythrocyte mutant frequency (EMF) compared to GORD patients (p < 0.001) and healthy volunteers (p < 0.001). In OAC patients, higher EMF was associated with worsening tumour staging (p = 0.014), nodal involvement (p = 0.019) and metastatic disease (p = 0.008). Chemotherapy patients demonstrated EMF’s over 19–times higher than GORD patients. Patients were further classified into groups containing those with non-neoplastic disease and those with high-grade dysplasia/cancer with 72.1% of cases correctly classified by high EMF. Within the non-neoplastic group, aspirin users had lower EMF (p = 0.001) and there was a positive correlation between body mass index (p = 0.03) and age (p < 0.001) and EMF. Smokers had EMF’s over double that of non-smokers (p = 0.011). Results suggest this test could help detect OAC and may be a useful predictor of disease progression.

Attenuated total reflection Fourier-transform infrared spectral discrimination in human bodily fluids of oesophageal transformation to adenocarcinoma

Attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) of biofluids was used to detect oesophageal stages through to oesophageal adenocarcinoma.

1H-NMR spectroscopy identifies potential biomarkers in serum metabolomic signatures for early stage esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is one of the most prevalent types of upper gastrointestinal malignancies. Here, we used ¹H nuclear magnetic resonance spectroscopy (¹H-NMR) to identify potential serum biomarkers in patients with early stage ESCC.

METHODS

Sixty-five serum samples from early stage ESCC patients (n = 25) and healthy controls (n = 40) were analysed using ¹H-NMR spectroscopy. We distinguished between different metabolites through principal component analysis, partial least squares-discriminant analysis, and orthogonal partial least squares-discriminant analysis (OPLS-DA) using SIMCA-P+ version 14.0 software. Receiver operating characteristic (ROC) analysis was conducted to verify potential biomarkers.

RESULTS

Using OPLS-DA, 31 altered serum metabolites were successfully identified between the groups. Based on the area under the ROC curve (AUROC), and the biomarker panel with AUROC of 0.969, six serum metabolites (α-glucose, choline, glutamine, glutamate, valine, and dihydrothymine) were selected as potential biomarkers for early stage ESCC. Dihydrothymine particularly was selected as a new feasible biomarker associated with tumor occurrence.

CONCLUSIONS

¹H-NMR spectroscopy may be a useful tumour detection approach in identifying useful metabolic ESCC biomarkers for early diagnosis and in the exploration of the molecular pathogenesis of ESCC.

Integrating Network Pharmacology and Metabolomics Study on Anti-rheumatic Mechanisms and Antagonistic Effects Against Methotrexate-Induced Toxicity of Qing-Luo-Yin

Qing-Luo-Yin (QLY) is a traditional Chinese medicine (TCM) formula used to treat Hot Syndrome-related rheumatoid arthritis (RA). Previously, we uncovered partial mechanisms involved in the therapeutic actions of QLY on RA. In this study, we further elucidated its anti-rheumatic mechanisms and investigated its possible interactions with methotrexate (MTX) in vivo using an integrating strategy coupled with network pharmacology and metabolomics techniques. Chemical composition of QLY was characterized by HPLC analysis. Collagen induced arthritis (CIA) was developed in male SD rats. The CIA rats were then assigned into different groups, and received QLY, MTX or QLY+MTX treatments according to the pre-arrangement. Therapeutic effects of QLY and its possible interactions with MTX in vivo were evaluated by clinical parameters, digital radiography assessment, histological/immunohistochemical examination, and serological biomarkers. Mechanisms underlying these actions were deciphered with network pharmacology methods, and further validated by metabolomics clues based on UPLC-Q-TOF/MS analysis of urines. Experimental evidences demonstrated that QLY notably alleviated the severity of CIA and protected joints from destruction. Re-balanced levels of hemoglobin and alanine transaminase in serum indicated reduced MTX-induced hepatic injury and myelosuppression under the co-treatment of QLY. Network-based target prediction found dozens of RA related proteins as potential targets of QLY. Upon the further biological function enrichment analysis, we found that a large amount of them were involved in nucleotide metabolism and immune functions. Metabolomics analysis showed that QLY restored amino acids, fatty acids, and energy metabolisms in CIA rats, which solidly supported its therapeutic effects on CIA. Consistently to findings from network pharmacology analysis, metabolomics study also found altered purine, pyrimidine, and pentose phosphate metabolisms in CIA rats receiving QLY treatment. All these clues suggested that inhibition on nucleic acid synthesis was essential to the immunosuppressive activity of QLY in vivo, and could contribute great importance to its therapeutic effects on CIA. Additionally, QLY induced significant antifolate resistance in rats, which would prevent folate from depletion during long-term MTX treatment, and should account for reduced side effects in combination regimen with MTX and QLY.

Non-Cardiac Chest Pain: A Review of Environmental Exposure-Associated Comorbidities and Biomarkers

The prevalence of non-cardiac chest pain (NCCP) ranges from 13-33%. A majority of those presenting with a chief complaint of chest pain are found to have a diagnosis of NCCP. Aerodigestive diseases are a cause of NCCP, and billions of dollars are spent annually on the treatment of NCCP. Furthermore, NCCP can cause significant psychological stress. NCCP is commonly diagnosed when patients have chest pain despite a normal cardiac evaluation. The leading cause of NCCP is gastro-oesophageal reflux disease (GORD). GORD should be suspected in patients who report a history of acid regurgitation, cough, dysphagia, and bloating. Another common cause of NCCP is obstructive airway disease (OAD). A thorough history and review of the symptoms should be performed for those with suspected NCCP, especially because of the contributing end organs. It is known that environmental exposures can commonly cause GORD and OAD; however, NCCP has not been fully explored in the context of environmental exposure. Patients with a history of exposure to particulate matter can develop environmental-exposure-associated GORD and coexisting OAD. This narrative review aims to provide a practical overview of NCCP, its causes, their relation to environmental exposure, and associated biomarkers. The authors used a PubMed search that spanned 2003-2018 to accomplish this. Additionally, this review provides a broad overview of biomarkers of GORD-associated NCCP and OAD-associated NCCP due to environmental exposure.

Serum Metabolomics Analysis of Asthma in Different Inflammatory Phenotypes: A Cross-Sectional Study in Northeast China

BACKGROUND AND OBJECTIVE

Asthma as a chronic heterogeneous disease seriously affects the quality of life. Incorrect identification for its clinical phenotypes lead to a huge waste of medical resources. Metabolomic technique as a novel approach to explore the pathogenesis of diseases have not been used to study asthma based on their clear defined inflammatory phenotypes. This study is aimed to distinguish the divergent metabolic profile in different asthma phenotypes and clarify the pathogenesis of them.

METHODS

Participants including eosinophilic asthmatics (EA, n=13), noneosinophilic asthmatics (NEA, n=16), and healthy controls (HC, n=15) were enrolled. A global profile of untargeted serum metabolomics was identified with Ultra Performance Liquid Chromatography-Mass Spectrometry technique.

RESULTS

Multivariate analysis was performed and showed a clear distinction between EA, NEA, and HC. A total of 18 different metabolites were recognized between the three groups based on OPLS-DA model and involved in 10 perturbed metabolic pathways. Glycerophospholipid metabolism, retinol metabolism, and sphingolipid metabolism were identified as the most significant changed three pathways (impact > 0.1 and -log(P) > 4) between the phenotypes.

CONCLUSIONS

We showed that the different inflammatory phenotypes of asthma involve the immune regulation, energy, and nutrients metabolism. The clarified metabolic profile contributes to understanding the pathophysiology of asthma phenotypes and optimizing the therapeutic strategy against asthma heterogeneity.

Evaluation of Serum Glycoprotein Biomarker Candidates for Detection of Esophageal Adenocarcinoma and Surveillance of Barrett's Esophagus

Esophageal adenocarcinoma (EAC) is thought to develop from asymptomatic Barrett's esophagus (BE) with a low annual rate of conversion. Current endoscopy surveillance of BE patients is probably not cost-effective. Previously, we discovered serum glycoprotein biomarker candidates which could discriminate BE patients from EAC. Here, we aimed to validate candidate serum glycoprotein biomarkers in independent cohorts, and to develop a biomarker candidate panel for BE surveillance. Serum glycoprotein biomarker candidates were measured in 301 serum samples collected from Australia (4 states) and the United States (1 clinic) using previously established lectin magnetic bead array (LeMBA) coupled multiple reaction monitoring mass spectrometry (MRM-MS) tier 3 assay. The area under receiver operating characteristic curve (AUROC) was calculated as a measure of discrimination, and multivariate recursive partitioning was used to formulate a multi-marker panel for BE surveillance. Complement C9 (C9), gelsolin (GSN), serum paraoxonase/arylesterase 1 (PON1) and serum paraoxonase/lactonase 3 (PON3) were validated as diagnostic glycoprotein biomarkers in lectin pull-down samples for EAC across both cohorts. A panel of 10 serum glycoprotein biomarker candidates discriminated BE patients not requiring intervention (BE± low grade dysplasia) from those requiring intervention (BE with high grade dysplasia (BE-HGD) or EAC) with an AUROC value of 0.93. Tissue expression of C9 was found to be induced in BE, dysplastic BE and EAC. In longitudinal samples from subjects that have progressed toward EAC, levels of serum C9 were significantly (p < 0.05) increased with disease progression in EPHA (erythroagglutinin from Phaseolus vulgaris) and NPL (Narcissus pseudonarcissus lectin) pull-down samples. The results confirm alteration of complement pathway glycoproteins during BE-EAC pathogenesis. Further prospective clinical validation of the confirmed biomarker candidates in a large cohort is warranted, prior to development of a first-line BE surveillance blood test.

NMR-based metabolomics of biofluids in cancer

This review describes the current status of NMR-based metabolomics of biofluids with respect to cancer risk assessment, detection, disease characterization, prognosis, and treatment monitoring. While the metabolism of cancer cells is altered compared with that of non-proliferating cells, the metabolome of blood and urine reflects the entire organism. We conclude that many studies show impressive associations between biofluid metabolomics and cancer progression, but translation to clinical practice is currently hindered by lack of validation, difficulties in biological interpretation, and non-standardized analytical procedures.

Feline urine metabolomic signature: characterization of low-molecular-weight substances in urine from domestic cats

Objectives This aim of this study was to characterize the composition and content of the feline urine metabolome. Methods Eight healthy domestic cats were acclimated at least 10 days before starting the study. Urine samples (~2 ml) were collected by ultrasound-guided cystocentesis. Samples were centrifuged at 1000 × g for 8 mins, and the supernatant was analyzed by gas chromatography/time-of-flight mass spectrometery. The urine metabolome was characterized using an untargeted metabolomics approach. Results Three hundred and eighteen metabolites were detected in the urine of the eight cats. These molecules are key components of at least 100 metabolic pathways. Feline urine appears to be dominated by carbohydrates, carbohydrate conjugates, organic acid and derivatives, and amino acids and analogs. The five most abundant molecules were phenaceturic acid, hippuric acid, pseudouridine phosphate and 3-(4-hydroxyphenyl) propionic acid. Conclusions and relevance This study is the first to characterize the feline urine metabolome. The results of this study revealed the presence of multiple low-molecular-weight substances that were not known to be present in feline urine. As expected, the origin of the metabolites detected in urine was diverse, including endogenous compounds and molecules biosynthesized by microbes. Also, the diet seemed to have had a relevant role on the urine metabolome. Further exploration of the urine metabolic phenotype will open a window for discovering unknown, or poorly understood, metabolic pathways. In turn, this will advance our understanding of feline biology and lead to new insights in feline physiology, nutrition and medicine.

Barrett's esophagus network analysis revealed that arginine, alanine, aspartate, glutamate, valine, leucine and isoleucine can be biomarkers

AIM

Identification of crucial genes and possible biomarkers which are involved in Barrett's esophagus (BE) disease was aim of this study.

BACKGROUND

BE is diagnosed by endoscopy and biopsy and is characterized by esophageal columnar metaplastic epithelium. BE can convert into dysplasia that finally results cancer condition.

METHODS

Gene expression profiles of BE and normal gastric cardia which are characterized by GSE34619 and GPL6244 platform (1) were retrieved from gene expression omnibus (GEO). The significant differentially expressed genes (DEGs) were analyzed via protein-protein interaction network (PPI) analysis. The nodes of network were enriched via gene ontology (GO) to find biological terms. Action map of network elements was provided.

RESULTS

Among 250 top DEGs, 100 ones were included in PPI network and KIT, CFTR, IMPDH2, MYB, FLT1, ATP4A, and CPS1 were recognized as prominent genes related to BE. Seven amino acids including arginine, alanine, aspartate, glutamate, valine, leucine and isoleucine which are related to BE were highlighted.

CONCLUSION

In conclusion five central DEGs; KIT, CFTR, IMPDH2, MYB, and FLT1 were proposed as possible biomarkers for BE. However, validation and more experimental information is require to finalize the findings.

NMR-based metabolomic techniques identify potential urinary biomarkers for early colorectal cancer detection

Better early detection methods are needed to improve the outcomes of patients with colorectal cancer (CRC). Proton nuclear magnetic resonance spectroscopy (¹H-NMR), a potential non-invasive early tumor detection method, was used to profile urine metabolites from 55 CRC patients and 40 healthy controls (HCs). Pattern recognition through orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied to ¹H-NMR processed data. Model specificity was confirmed by comparison with esophageal cancers (EC, n=18). Unique metabolomic profiles distinguished all CRC stages from HC urine samples. A total of 16 potential biomarker metabolites were identified in stage I/II CRC, indicating amino acid metabolism, glycolysis, tricarboxylic acid (TCA) cycle, urea cycle, choline metabolism, and gut microflora metabolism pathway disruptions. Metabolite profiles from early stage CRC and EC patients were also clearly distinguishable, suggesting that upper and lower gastrointestinal cancers have different metabolomic profiles. Our study assessed important metabolomic variations in CRC patient urine samples, provided information complementary to that collected from other biofluid-based metabolomics analyses, and elucidated potential underlying metabolic mechanisms driving CRC. Our results support the utility of NMR-based urinary metabolomics fingerprinting in early diagnosis of CRC.

Circulating metabolite profiles to predict overall survival in advanced non-small cell lung cancer patients receiving first-line chemotherapy

OBJECTIVES

The prognosis for advanced-stage non-small cell lung cancer (NSCLC) is usually poor. However, survival may be variable and difficult to predict. In the current study, we aimed to identify circulating metabolites as potential predictive biomarkers for overall survival of advanced-stage (III/IV) NSCLC patients treated with first-line platinum-based chemotherapy.

MATERIALS AND METHODS

Using two-stage study design, we performed global metabolomic profiling in blood of 220 advanced-stage NSCLC patients, including 110 with poor survival and 110 with good survival. Metabolomic profiling was conducted using Metabolon platform. The association of each metabolite with survival was assessed by Cox proportional hazard regression model with adjustment for covariates.

RESULTS AND CONCLUSION

We found levels of 4 metabolites, caffeine, paraxanthine, stachydrine, and methyl glucopyranoside (alpha+beta), differed significantly between NSCLC patients with poor and good survival in both discovery and validation phases (P<0.05). Interestingly, majority of the identified metabolites are involved in caffeine metabolism, and 2 metabolites are related to coffee intake. In fact, caffeine metabolism pathway was the only significant pathway identified which significantly differed between NSCLC patients with poor and good survival (P=1.48E-07) in the pathway analysis. We also found 4 metabolites whose levels were significantly associated with good survival in both discovery and validation phases. Strong cumulative effects on overall survival were observed for these 4 metabolites. In conclusion, we identified a panel of metabolites including metabolites in caffeine metabolism pathway that may predict survival outcome in advanced-stage NSCLC patients. The identified small metabolites may be useful biomarker candidates to help identify patients who may benefit from platinum-based chemotherapy.

Short overview on metabolomics approach to study pathophysiology of oxidative stress in cancer

Association of oxidative stress with carcinogenesis is well known, but not understood well, as is pathophysiology of oxidative stress generated during different types of anti-cancer treatments. Moreover, recent findings indicate that cancer associated lipid peroxidation might eventually help defending adjacent nonmalignant cells from cancer invasion. Therefore, untargeted metabolomics studies designed for advanced translational and clinical studies are needed to understand the existing paradoxes in oncology, including those related to controversial usage of antioxidants aiming to prevent or treat cancer. In this short review we have tried to put emphasis on the importance of pathophysiology of oxidative stress and lipid peroxidation in cancer development in relation to metabolic adaptation of particular types of cancer allowing us to conclude that adaptation to oxidative stress is one of the main driving forces of cancer pathophysiology. With the help of metabolomics many novel findings are being achieved thus encouraging further scientific breakthroughs. Combined with targeted qualitative and quantitative methods, especially immunochemistry, further research might reveal bio-signatures of individual patients and respective malignant diseases, leading to individualized treatment approach, according to the concepts of modern integrative medicine.

Metabolomic signature of brain cancer

Despite advances in surgery and adjuvant therapy, brain tumors represent one of the leading causes of cancer-related mortality and morbidity in both adults and children. Gliomas constitute about 60% of all cerebral tumors, showing varying degrees of malignancy. They are difficult to treat due to dismal prognosis and limited therapeutics. Metabolomics is the untargeted and targeted analyses of endogenous and exogenous small molecules, which charact erizes the phenotype of an individual. This emerging "omics" science provides functional readouts of cellular activity that contribute greatly to the understanding of cancer biology including brain tumor biology. Metabolites are highly informative as a direct signature of biochemical activity; therefore, metabolite profiling has become a promising approach for clinical diagnostics and prognostics. The metabolic alterations are well-recognized as one of the key hallmarks in monitoring disease progression, therapy, and revealing new molecular targets for effective therapeutic intervention. Taking advantage of the latest high-throughput analytical technologies, that is, nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), metabolomics is now a promising field for precision medicine and drug discovery. In the present report, we review the application of metabolomics and in vivo metabolic profiling in the context of adult gliomas and paediatric brain tumors. Analytical platforms such as high-resolution (HR) NMR, in vivo magnetic resonance spectroscopic imaging and high- and low-resolution MS are discussed. Moreover, the relevance of metabolic studies in the development of new therapeutic strategies for treatment of gliomas are reviewed.

Metabolic Perturbation and Potential Markers in Patients with Esophageal Cancer

Clinical diagnosis of esophageal cancer (EC) at early stage is rather difficult. This study aimed to profile the molecules in serum and tissue and identify potential biomarkers in patients with EC. A total of 64 volunteers were recruited, and 83 samples (24 EC serum samples, 21 serum controls, 19 paired EC tissues, and corresponding tumor-adjacent tissues) were analyzed. The gas chromatography time-of-flight mass spectrometry (GC/TOF-MS) was employed, and principal component analysis was used to reveal the discriminatory metabolites and identify the candidate markers of EC. A total of 41 in serum and 36 identified compounds in tissues were relevant to the malignant prognosis. A marked metabolic reprogramming of EC was observed, including enhanced anaerobic glycolysis and glutaminolysis, inhibited tricarboxylic acid (TCA) cycle, and altered lipid metabolism and amino acid turnover. Based on the potential markers of glucose, glutamic acid, lactic acid, and cholesterol, the receiver operating characteristic (ROC) curves indicated good diagnosis and prognosis of EC. EC patients showed distinct reprogrammed metabolism involved in glycolysis, TCA cycle, glutaminolysis, and fatty acid metabolism. The pivotal molecules in the metabolic pathways were suggested as the potential markers to facilitate the early diagnosis of human EC.

Candidate serum metabolite biomarkers for differentiating gastroesophageal reflux disease, Barrett's esophagus, and high-grade dysplasia/esophageal adenocarcinoma

INTRODUCTION/OBJECTIVES

Incidence of esophageal adenocarcinoma (EA), an often fatal cancer, has increased sharply over recent decades. Several important risk factors (reflux, obesity, smoking) have been identified for EA and its precursor, Barrett's esophagus (BE), but a key challenge remains identifying individuals at highest risk, since most with reflux do not develop BE, and most with BE do not progress to cancer. Metabolomics represents an emerging approach for identifying novel biomarkers associated with cancer development.

METHODS

We used targeted liquid chromatography-mass spectrometry (LC-MS) to profile 57 metabolites in 322 serum specimens derived from individuals with gastroesophageal reflux disease (GERD), BE, high-grade dysplasia (HGD), or EA, drawn from two well-annotated epidemiologic parent studies.

RESULTS

Multiple metabolites differed significantly (P<0.05) between BE versus GERD (n=9), and between HGD/EA versus BE (n=4). Several top candidates (FDR q≤0.15), including urate, homocysteine, and 3-nitrotyrosine, are linked to inflammatory processes, which may contribute to BE/EA pathogenesis. Multivariate modeling achieved moderate discrimination between HGD/EA and BE (AUC=0.75), with less pronounced separation for BE versus GERD (AUC=0.64).

CONCLUSION

Serum metabolite differences can be detected between individuals with GERD versus BE, and between those with BE versus HGD/EA, and may help differentiate patients at different stages of progression to EA.

Metabolomic Evidence for a Field Effect in Histologically Normal and Metaplastic Tissues in Patients with Esophageal Adenocarcinoma

Patients with Barrett's esophagus (BO) are at increased risk of developing esophageal adenocarcinoma (EAC). Most Barrett's patients, however, do not develop EAC, and there is a need for markers that can identify those most at risk. This study aimed to see if a metabolic signature associated with the development of EAC existed. For this, tissue extracts from patients with EAC, BO, and normal esophagus were analyzed using ¹H nuclear magnetic resonance. Where possible, adjacent histologically normal tissues were sampled in those with EAC and BO. The study included 46 patients with EAC, 7 patients with BO, and 68 controls who underwent endoscopy for dyspeptic symptoms with normal appearances. Within the cancer cohort, 9 patients had nonneoplastic Barrett's adjacent to the cancer suitable for biopsy. It was possible to distinguish between histologically normal, BO, and EAC tissue in EAC patients [area under the receiver operator curve (AUROC) 1.00, 0.86, and 0.91] and between histologically benign BO in the presence and absence of EAC (AUROC 0.79). In both these cases, sample numbers limited the power of the models. Comparison of histologically normal tissue proximal to EAC versus that from controls (AUROC 1.00) suggests a strong field effect which may develop prior to overt EAC and hence be useful for identifying patients at high risk of developing EAC. Excellent sensitivity and specificity were found for this model to distinguish histologically normal squamous esophageal mucosa in EAC patients and healthy controls, with 8 metabolites being very significantly altered. This may have potential diagnostic value if a molecular signature can detect tissue from which neoplasms subsequently arise.

Global metabolomics reveals potential urinary biomarkers of esophageal squamous cell carcinoma for diagnosis and staging

We performed a metabolomics study using liquid chromatography-mass spectrometry (LC-MS) combined with multivariate data analysis (MVDA) to discriminate global urine profiles in urine samples from esophageal squamous cell carcinoma (ESCC) patients and healthy controls (NC). Our work evaluated the feasibility of employing urine metabolomics for the diagnosis and staging of ESCC. The satisfactory classification between the healthy controls and ESCC patients was obtained using the MVDA model, and obvious classification of early-stage and advanced-stage patients was also observed. The results suggest that the combination of LC-MS analysis and MVDA may have potential applications for ESCC diagnosis and staging. We then conducted LC-MS/MS experiments to identify the potential biomarkers with large contributions to the discrimination. A total of 83 potential diagnostic biomarkers for ESCC were screened out, and 19 potential biomarkers were identified; the variations between the differences in staging using these potential biomarkers were further analyzed. These biomarkers may not be unique to ESCCs, but instead result from any malignant disease. To further elucidate the pathophysiology of ESCC, we studied related metabolic pathways and found that ESCC is associated with perturbations of fatty acid β-oxidation and the metabolism of amino acids, purines, and pyrimidines.

Early Diagnosis of Brain Metastases Using a Biofluids-Metabolomics Approach in Mice

Over 20% of cancer patients will develop brain metastases. Prognosis is currently extremely poor, largely owing to late-stage diagnosis. We hypothesized that biofluid metabolomics could detect tumours at the micrometastatic stage, prior to the current clinical gold-standard of blood-brain barrier breakdown. Metastatic mammary carcinoma cells (4T1-GFP) were injected into BALB/c mice via intracerebral, intracardiac or intravenous routes to induce differing cerebral and systemic tumour burdens. B16F10 melanoma and MDA231BR-GFP human breast carcinoma cells were used for additional modelling. Urine metabolite composition was analysed by 1H NMR spectroscopy. Statistical pattern recognition and modelling was applied to identify differences or commonalities indicative of brain metastasis burden. Significant metabolic profile separations were found between control cohorts and animals with tumour burdens at all time-points for the intracerebral 4T1-GFP time-course. Models became stronger, with higher sensitivity and specificity, as the time-course progressed indicating a more severe tumour burden. Sensitivity and specificity for predicting a blinded testing set were 0.89 and 0.82, respectively, at day 5, both rising to 1.00 at day 35. Significant separations were also found between control and all 4T1-GFP injected mice irrespective of route. Likewise, significant separations were observed in B16F10 and MDA231BR-GFP cell line models. Metabolites underpinning each separation were identified. These findings demonstrate that brain metastases can be diagnosed in an animal model based on urinary metabolomics from micrometastatic stages. Furthermore, it is possible to separate differing systemic and CNS tumour burdens, suggesting a metabolite fingerprint specific to brain metastasis. This method has strong potential for clinical translation.

Software-assisted serum metabolite quantification using NMR

The goal of metabolomics is to analyze a whole metabolome under a given set of conditions, and accurate and reliable quantitation of metabolites is crucial. Absolute concentration is more valuable than relative concentration; however, the most commonly used method in NMR-based serum metabolic profiling, bin-based and full data point peak quantification, provides relative concentration levels of metabolites and are not reliable when metabolite peaks overlap in a spectrum. In this study, we present the software-assisted serum metabolite quantification (SASMeQ) method, which allows us to identify and quantify metabolites in NMR spectra using Chenomx software. This software uses the ERETIC2 utility from TopSpin to add a digitally synthesized peak to a spectrum. The SASMeQ method will advance NMR-based serum metabolic profiling by providing an accurate and reliable method for absolute quantification that is superior to bin-based quantification.

Metabolomics: Applications and Promise in Mycobacterial Disease

Until recently, the study of mycobacterial diseases was trapped in culture-based technology that is more than a century old. The use of nucleic acid amplification is changing this, and powerful new technologies are on the horizon. Metabolomics, which is the study of sets of metabolites of both the bacteria and host, is being used to clarify mechanisms of disease, and can identify changes leading to better diagnosis, treatment, and prognostication of mycobacterial diseases. Metabolomic profiles are arrays of biochemical products of genes in their environment. These complex patterns are biomarkers that can allow a more complete understanding of cell function, dysfunction, and perturbation than genomics or proteomics. Metabolomics could herald sweeping advances in personalized medicine and clinical trial design, but the challenges in metabolomics are also great. Measured metabolite concentrations vary with the timing within a condition, the intrinsic biology, the instruments, and the sample preparation. Metabolism profoundly changes with age, sex, variations in gut microbial flora, and lifestyle. Validation of biomarkers is complicated by measurement accuracy, selectivity, linearity, reproducibility, robustness, and limits of detection. The statistical challenges include analysis, interpretation, and description of the vast amount of data generated. Despite these drawbacks, metabolomics provides great opportunity and the potential to understand and manage mycobacterial diseases.

Urinary metabolites as noninvasive biomarkers of gastrointestinal diseases: A clinical review

The diagnosis of gastrointestinal (GI) disorders is usually based on invasive techniques such as endoscopy. A key important factor in GI cancer is early diagnosis which warrants development of non- or less-invasive diagnostic techniques. In addition, monitoring and surveillance are other important parts in the management of GI diseases. Metabolomics studies with nuclear magnetic resonance and mass spectrometry can measure the concentration of more than 3000 chemical compounds in the urine providing possible chemical signature in different diseases and during health. In this review, we discuss the urinary metabolomics signature of different GI diseases including GI cancer and elaborate on how these biomarkers could be used for the classification, early diagnosis and the monitoring of the patients. Moreover, we discuss future directions of this still evolving field of research.

Magnetic resonance imaging for quantitative staging and evaluation of chemoradiotherapeutic effect in esophageal carcinoma

Esophageal carcinoma is a common digestive malignant tumor. Evaluation of the stage and response to chemoradiotherapy of the carcinoma is very important for the treatment decision making and adjustment of therapeutic protocol. To date, a variety of imaging techniques have been used for staging and monitoring response to therapy, but most of the procedures are invasive or of radiation exposure. Moreover, most of the techniques evaluating esophageal cancer are based on morphologic changes. As a non-invasive and non-ionising examination technique, magnetic resonance imaging can quantitatively evaluate this cancer. Nowadays magnetic resonance quantitative technique has progressed greatly in staging and monitoring response to therapy of esophageal carcinoma. This paper focuses on the quantitative evaluation of stage and chemoradiotherapeutic effect in esophageal carcinoma using magnetic resonance imaging.

Pathway, in silico and tissue-specific expression quantitative analyses of oesophageal squamous cell carcinoma genome-wide association studies data

BACKGROUND

Oesophageal cancer is the fourth leading cause of cancer death in China where essentially all cases are histologically oesophageal squamous cell carcinoma (ESCC). Agnostic pathway-based analyses of genome-wide association study (GWAS) data combined with tissue-specific expression quantitative trait loci (eQTL) analysis and publicly available functional data can identify biological pathways and/or genes enriched with functionally-relevant disease-associated variants.

METHOD

We used the adaptive multilocus joint test to analyse 1827 pathways containing 6060 genes using GWAS data from 1942 ESCC cases and 2111 controls with Chinese ancestry. We examined the function of risk alleles using in silico and eQTL analyses in oesophageal tissues.

RESULTS

Associations with ESCC risk were observed for 36 pathways predominantly involved in apoptosis, cell cycle regulation and DNA repair and containing known GWAS-associated genes. After excluding genes with previous GWAS signals, candidate pathways (and genes) for ESCC risk included taste transduction (KEGG_hsa04742; TAS2R13, TAS2R42, TAS2R14, TAS2R46,TAS2R50), long-patch base excision repair (Reactome_pid; POLD2) and the metabolics pathway (KEGG_hsa01100; MTAP, GAPDH, DCTD, POLD2, AMDHD1). We identified and validated CASP8 rs13016963 and IDH2 rs11630814 as eQTLs, and CASP8 rs3769823 and IDH2 rs4561444 as the potential functional variants in high-linkage disequilibrium with these single nucleotide polymorphisms (SNPs), respectively. Further, IDH2 mRNA levels were down-regulated in ESCC (tumour:normal-fold change = 0.69, P =  .75E-14).

CONCLUSION

Agnostic pathway-based analyses and integration of multiple types of functional data provide new evidence for the contribution of genes in taste transduction and metabolism to ESCC susceptibility, and for the functionality of both established and new ESCC risk-related SNPs.

Recommendations and Standardization of Biomarker Quantification Using NMR-Based Metabolomics with Particular Focus on Urinary Analysis

NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to nondestructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Precise metabolite quantification is a prerequisite to move any chemical biomarker or biomarker panel from the lab to the clinic. Among the biofluids commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, and easily obtained, needs little sample preparation, and does not require invasive medical procedures for collection. Furthermore, urine captures and concentrates many "unwanted" or "undesirable" compounds throughout the body, providing a rich source of potentially useful disease biomarkers; however, incredible variation in urine chemical concentrations makes analysis of urine and identification of useful urinary biomarkers by NMR challenging. We discuss a number of the most significant issues regarding NMR-based urinary metabolomics with specific emphasis on metabolite quantification for disease biomarker applications and propose data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, sample preparation, and biomarker assessment.

Effects of Ketamine on Metabolomics of Serum and Urine in Cynomolgus Macaques (Macaca fascicularis)

In this study, a metabolomics approach based on nuclear magnetic resonance spectroscopy and pertinent multivariate data analyses was used to evaluate the effect of ketamine on metabolic markers in cynomolgus macaques. Principal component analysis and orthogonal projection to latent structure with discriminant analysis showed that ketamine (10 mg/kg) induced metabolic perturbations. Compared with the control group, ketamine-treated macaques had lower serum levels of α-glucose, myoinositol, lactate and succinate and lower urine levels of pyruvate and lactate. In contrast, the levels of leucine in serum and arginine in urine were significantly higher in the ketamine group. Our results also demonstrated that a single injection of ketamine influenced the major energy and amino acid metabolic pathways in cynomolgus macaques. Our study suggests that these influences should be considered in the design of experiments and the interpretation related blood and urine data from ketamine-sedated cynomolgus macaques.

Volatile Organic Metabolites Identify Patients with Mesangial Proliferative Glomerulonephritis, IgA Nephropathy and Normal Controls

Urinary volatile organic compounds (VOCs) analysis for kidney diseases has attracted a large amount of scientific interest recently, and urinary metabolite analysis has already been applied to many diseases. Urine was collected from 15 mesangial proliferative glomerulonephritis (MsPGN) patients, 21 IgA nephropathy (IgAN) patients and 15 healthy controls. Solid phase microextraction-chromatography- mass spectrometry (SPME-GC-MS) was used to analyse the urinary metabolites. The statistical methods principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLSDA) were performed to process the final data. Five metabolites were significantly greater in the group of MsPGN patients than in the normal control group (P < 0.05) while three metabolites were found at increased levels in the group of IgAN patients compared with the normal controls (P < 0.05). In addition, five metabolites were significantly increased in the group of IgAN patients compared with the MsPGN patients (P < 0.05). These five metabolites may be specific biomarkers for distinguishing between MsPGN and IgAN. The analysis of urinary VOCs appears to have potential clinical applications as a diagnostic tool.

Identification of Serum Markers of Esophageal Adenocarcinoma by Global and Targeted Metabolic Profiling

BACKGROUND & AIMS

We aimed to identify new serum biomarkers of esophageal adenocarcinoma (EAC).

METHODS

We performed metabolomic analyses of serum samples from 2 sets of case-control pairs in the discovery phase, each consisting of 30 patients with histologically confirmed EAC (cases) from the University of Texas MD Anderson Cancer Center and 30 matched subjects without EAC (controls). We identified metabolites whose levels differed significantly between cases and controls and validated those with the greatest difference in an analysis of 321 EAC cases and 331 controls. We generated a metabolite risk score (MRS) for the metabolites.

RESULTS

The levels of 64 metabolites differed significantly between EAC cases and controls in the discovery phase. The metabolites with the greatest difference were amino acid L-proline (LP), ketone body 3-hydroxybutyrate (BHBA), and carbohydrate D-mannose (DM); these differences were confirmed in the validation set. Cases had lower mean levels of LP than controls (22.78 ± 6.79 μg/mL vs 28.24 ± 8.64 μg/mL; P < .001) and higher levels of BHBA (18.06 ± 17.84 μg/mL vs 7.73 ± 9.92 μg/mL; P < .001) and DM (9.87 ± 4.28 μg/mL vs 6.28 ± 3.61 μg/mL; P < .001). Levels of DM were significantly higher in patients with late-stage EAC than early-stage EAC (10.61 ± 4.79 μg/mL vs 8.97 ± 3.36 μg/mL; P = .005). Higher levels of LP were associated with significant decrease in risk of EAC (odds ratio [OR], 0.26; 95% confidence interval [CI], 0.18-0.38). A significant increase in risk of EAC was associated with higher levels of BHBA (OR, 4.05; 95% CI, 2.84-5.78) and DM (OR, 7.04; 95% CI, 4.79-10.34). Levels of all 3 metabolites associated with EAC risk in a dose response manner; the level of risk conferred by the metabolites increased jointly with smoking status and body mass index. Individuals with high MRS had significant (7.76-fold) increase in risk of EAC vs those with low MRS. Smokers with high MRS had the greatest risk of EAC (OR, 23.40; 95% CI, 10.95-50.00), compared with never smokers with low MRS.

CONCLUSIONS

On the basis of a case vs control metabolic profile analysis, levels of LP, BHBA, and DM are associated with risk of EAC. These markers might be used as risk and prognostic factors for patients with EAC.

Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review

The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.

Metabolomic profiling in cattle experimentally infected with Mycobacterium avium subsp. paratuberculosis

The sensitivity of current diagnostics for Johne's disease, a slow, progressing enteritis in ruminants caused by Mycobacterium avium subsp. paratuberculosis (MAP), is too low to reliably detect all infected animals in the subclinical stage. The objective was to identify individual metabolites or metabolite profiles that could be used as biomarkers of early MAP infection in ruminants. In a monthly follow-up for 17 months, calves infected at 2 weeks of age were compared with aged-matched controls. Sera from all animals were analyzed by 1H nuclear magnetic resonance spectrometry. Spectra were acquired, processed, and quantified for analysis. The concentration of many metabolites changed over time in all calves, but some metabolites only changed over time in either infected or non-infected groups and the change in others was impacted by the infection. Hierarchical multivariate statistical analysis achieved best separation between groups between 300 and 400 days after infection. Therefore, a cross-sectional comparison between 1-year-old calves experimentally infected at various ages with either a high- or a low-dose and age-matched non-infected controls was performed. Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS DA) yielded distinct separation of non-infected from infected cattle, regardless of dose and time (3, 6, 9 or 12 months) after infection. Receiver Operating Curves demonstrated that constructed models were high quality. Increased isobutyrate in the infected cattle was the most important agreement between the longitudinal and cross-sectional analysis. In general, high- and low-dose cattle responded similarly to infection. Differences in acetone, citrate, glycerol and iso-butyrate concentrations indicated energy shortages and increased fat metabolism in infected cattle, whereas changes in urea and several amino acids (AA), including the branched chain AA, indicated increased protein turnover. In conclusion, metabolomics was a sensitive method for detecting MAP infection much sooner than with current diagnostic methods, with individual metabolites significantly distinguishing infected from non-infected cattle.

Tyrosine, phenylalanine, and tryptophan in gastroesophageal malignancy: a systematic review

Gastroesophageal cancer has a rapidly increasing incidence worldwide and reliable biomarkers are urgently required to facilitate earlier diagnosis and improve survival. The aromatic amino acids tyrosine, phenylalanine, and tryptophan represent potential biomarkers and their relation to gastroesophageal cancer will be evaluated in this review. An electronic literature search was performed to identify all published research relating to the measurement of tyrosine, phenylalanine, or tryptophan in the biofluids or tissues of patients with gastroesophageal cancer. Sixteen studies were included in this systematic review. Six studies investigated serum concentrations, which all found decreased concentrations of these aromatic amino acids, except one study that found increased phenylalanine. Five studies reported increased concentrations within gastric content of these patients and two reported increased urinary concentrations. Tissue concentrations of these aromatic amino acids were increased in three studies. Tyrosine, phenylalanine, and tryptophan represent potential biomarkers of gastroesophageal cancer, and further research is necessary to definitively establish the mechanism responsible for altered concentrations of these compounds in patients with gastroesophageal cancer.

Potential role of metabolomics in diagnosis and surveillance of gastric cancer

Gastric cancer is one of the deadliest cancers worldwide, and is especially prevalent in Asian countries. With such high morbidity and mortality, early diagnosis is essential to achieving curative intent treatment and long term survival. Metabolomics is a new field of study that analyzes metabolites from biofluids and tissue samples. While metabolomics is still in its infancy, there are numerous potential applications in oncology, specifically early diagnosis. Only a few studies in the literature have examined metabolomics’ role in gastric cancer. Various fatty acid, carbohydrate, nucleic acid, and amino acid metabolites have been identified that distinguish gastric cancer from normal tissue and benign gastric disease. However, findings from these few studies are at times conflicting. Most studies demonstrate some relationship of cancer cells to the Warburg Effect, in that glycolysis predominates with conversion of pyruvate to lactate. This is one of the most consistent findings across the literature. There is less consistency in metabolomic signature with respect to nucleic acids, lipids and amino acids. In spite of this, metabolomics holds some promise for cancer surveillance but further studies are necessary to achieve consistency and validation before it can be widely employed as a clinical tool.

Applying metabolomics to understand the aggressive phenotype and identify novel therapeutic targets in glioblastoma

Glioblastoma continues to be an invariably fatal malignancy. The established approach for understanding the biology of these aggressive tumors in an effort to identify novel molecular targets has largely been genotype-based. Unfortunately, clinical gains offered by this level of understanding have been limited, largely based on the complex nature of signaling networks associated with tumorigenesis and the inability to delineate the key “functional” signaling pathways actually driving growth in an individual tumor. Metabolomics is the global quantitative assessment of endogenous metabolites within a biological system, taking into account genetic regulation, altered kinetic activity of enzymes, and changes in metabolic reactions. Thus, compared to genomics and proteomics, metabolomics reflects changes in phenotype and therefore function. In this review, we highlight some of the key advancements that have been made in applying metabolomics to understand the aggressive phenotype of glioblastoma. Collectively, these studies have provided a previously unrecognized window into the underlying biology of these tumors. Current and future efforts are designed to determine how this technology may be applied to improve diagnosis and predict the aggressiveness of glioblastoma, and more importantly, identify novel, therapeutic strategies designed to improve clinical outcomes.