Esophageal cancer metabolite biomarkers detected by LC-MS and NMR methods

The Use of Metabolomics to Identify Biological Signatures of Manganese Exposure

Objectives

Manganese (Mn) is a known neurotoxicant, and given its health effects and ubiquitous nature in metal-working settings, identification of a valid and reproducible biomarker of Mn exposure is of interest. Here, global metabolomics is utilized to determine metabolites that differ between groups defined by Mn exposure status, with the goal being to help inform a potential metabolite biomarker of Mn exposure.

Methods

Mn exposed subjects were recruited from a Mn steel foundry and Mn unexposed subjects were recruited from crane operators at a metal recycling facility. Over the course of a work day, each subject wore a personal inhalable dust sampler (IOM), and provided an end of shift urine sample that underwent global metabolomics profiling. Both exposed and unexposed subjects were divided into a training set and demographically similar validation set. Using a two-sided adjusted t-test, relative abundances of all metabolites found were compared between Mn exposed and unexposed training sets, and those with a false discovery rates (FDR) <0.1 were further tested in the validation sets.

Results

Fifteen ions were found to be significantly different (FDR < 0.1) between the exposed and unexposed training sets, and nine of these ions remained significantly different between the exposed and unexposed validation set as well. When further dividing exposure status into 'lower exposure' and 'higher exposure', several of these nine ions exhibited an apparent exposure-response relationship.

Conclusions

This is the first time that metabolomics has been used to distinguish between Mn exposure status in an occupational cohort, though additional work should be done to replicate these findings with a larger cohort. With metabolite identification by name, empirical formula, or pathway, a better understanding of the relationship between Mn exposure and neurotoxic effects could be elucidated, and a potential metabolite biomarker of Mn exposure could be determined.

Disturbed tryptophan metabolism correlating to progression and metastasis of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most frequent malignancies worldwide. Lymph node metastasis is the leading cause of death in ESCC patients. To identify early diagnostic and prognostic biomarkers of ESCC and elucidate underlying pathogenesis of the disease, a targeted metabolomics strategy based on liquid chromatography combined with tandem mass spectrometry was applied to explore tryptophan metabolism between ESCC patients, metastatic ESCC patients (mESCC), and healthy controls. Statistical analysis on metabolite expression abundance and compound concentration ratio was conducted to discriminate patients from healthy controls. The concentration ratio of kynurenine, 5-hydroxytryptophan, 5-hydroxyindole-3-acetic acid, 5-hydroxytryptamine to their precursor tryptophan were identified as potential biomarkers, presenting high diagnostic capacity for distinguishing ESCC and mESCC patients from healthy controls. Moreover, a prognostic prediction model was also built on these ratios to distinguish metastasis patients from non-metastasis patients successfully. The high performance of ESCC prediction models suggest that concentration ratios of compounds may be used as biomarkers for early diagnosis and prognosis of the disease. In addition, concentration ratios of compounds show a progressively increased trend from non-metastasis to metastasis patients compared with healthy controls, which is in accordance with process of malignant transformation of ESCC. This interested finding suggests that disturbed tryptophan metabolism is correlated to progression and metastasis of ESCC since concentration ratios of compounds reflect activity of enzymes involved in tryptophan metabolism. This study reveals the impact of tryptophan metabolism to tumorigenesis and metastasis of ESCC, which help biologists investigate mechanism of the disease.

Plasma-free amino acid profiles are predictors of cancer and diabetes development

Type 2 diabetes (T2D) and cancers are two major causes of morbidity and mortality worldwide. Nowadays, there is convincing evidence of positive associations between T2D and the incidence or prognosis of a wide spectrum of cancers, for example, breast, colon, liver and pancreas. Many observational studies suggest that certain medications used to treat hyperglycemia (or T2D) may affect cancer cells directly or indirectly. The potential mechanisms of the direct T2D cancer links have been hypothesized to be hyperinsulinemia, hyperglycemia and chronic inflammation; however, the metabolic pathways that lead to T2D and cancers still remain elusive. Plasma-free amino acid (PFAA) profiles have been highlighted in their associations with the risks of developing T2D and cancers in individuals with different ethnic groups and degree of obesity. The alterations of PFAAs might be predominately caused by the metabolic shift resulted from insulin resistance. The underlying mechanisms have not been fully elucidated, in particular whether the amino acids are contributing to these diseases development in a causal manner. This review addresses the molecular and clinical associations between PFAA alterations and both T2D and cancers, and interprets possible mechanisms involved. Revealing these interactions and mechanisms may improve our understanding of the complex pathogenesis of diabetes and cancers and improve their treatment strategies.

Non-invasive urinary metabolomic profiling discriminates prostate cancer from benign prostatic hyperplasia

INTRODUCTION

Prostate cancer (PCa) is one of the most common malignancies in men worldwide. Serum prostate specific antigen (PSA) level has been extensively used as a biomarker to detect PCa. However, PSA is not cancer-specific and various non-malignant conditions, including benign prostatic hyperplasia (BPH), can cause a rise in PSA blood levels, thus leading to many false positive results.

OBJECTIVES

In this study, we evaluated the potential of urinary metabolomic profiling for discriminating PCa from BPH.

METHODS

Urine samples from 64 PCa patients and 51 individuals diagnosed with BPH were analysed using 1H nuclear magnetic resonance (1H-NMR). Comparative analysis of urinary metabolomic profiles was carried out using multivariate and univariate statistical approaches.

RESULTS

The urine metabolomic profile of PCa patients is characterised by increased concentrations of branched-chain amino acids (BCAA), glutamate and pseudouridine, and decreased concentrations of glycine, dimethylglycine, fumarate and 4-imidazole-acetate compared with individuals diagnosed with BPH.

CONCLUSION

PCa patients have a specific urinary metabolomic profile. The results of our study underscore the clinical potential of metabolomic profiling to uncover metabolic changes that could be useful to discriminate PCa from BPH in a clinical context.

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.

From blood to breath: New horizons for esophageal cancer biomarkers

Esophageal cancer is a lethal cancer encompassing adenocarcinoma and squamous cell carcinoma sub-types. The global incidence of esophageal cancer is increasing world-wide, associated with the increased prevalence of associated risk factors. The asymptomatic nature of disease often leads to late diagnosis and five-year survival rates of less than 15%. Current diagnostic tools are restricted to invasive and costly endoscopy and biopsy for histopathology. Minimally and non-invasive biomarkers of esophageal cancer are needed to facilitate earlier detection and better clinical management of patients. This paper summarises recent insights into the development and clinical validation of esophageal cancer biomarkers, focussing on circulating markers in the blood, and the emerging area of breath and odorant biomarkers.

Plasma Phosphatidylcholines Fatty Acids in Men with Squamous Cell Esophageal Cancer: Chemoradiotherapy Improves Abnormal Profile

BACKGROUND Abnormal metabolism of fatty acids (FA) is considered to play a role in human cancers, including esophageal cancer (EC). Nevertheless, there have been only a few studies dealing with the influence of the chemotherapy or radiotherapy on the plasma FA profiles. In this work we compared FA in plasma phosphatidylcholine (PC) of the patients with squamous EC and healthy subjects and investigated changes in the FA spectrum during neoadjuvant chemoradiotherapy (CRT). MATERIAL AND METHODS Forty-two men with squamous EC were compared with age-matched healthy controls. The EC group was subjected to concurrent neoadjuvant CRT. We analyzed FA in plasma PC before and after CRT. RESULTS The EC group was characterized by increased levels of both saturated and monounsaturated FA, associated with an increased index of SCD1 (stearoyl-CoA desaturase-1). Moreover, decreased levels of linoleic acid and total polyunsaturated FA (PUFA) n-6 were found in EC patients. The CRT was accompanied by increased docosahexaenoic acid and total PUFA n-3 content in plasma PC, concurrently with the decrease of estimated activity of SCD1. CONCLUSIONS We found that patients with EC had altered FA profile in plasma PC, which could be related to abnormal FA metabolism in cancer (e.g., altered synthesis de novo, b-oxidation, desaturation, and elongation). The described changes in FA profiles during CRT could be involved in favorable functioning of CRT. Further studies investigating the plasma FA compositions and their changes due to CRT in EC patients are warranted.

Pathophysiologic processes have an impact on the plasma metabolomic signature of endometriosis patients

OBJECTIVE

To evaluate potential variations in the plasma metabolomic profile of endometriosis patients as a consequence of pathophysiologic alterations associated with this disorder.

DESIGN

Prospective study. For each subject, a plasma sample was collected after overnight fasting and before surgery.

SETTING

University medical center.

PATIENT(S)

The clinical cohort included 50 endometriosis patients, diagnosed at early (n = 6) and advanced (n = 44) stages of the disease, and 23 healthy women. All volunteers underwent diagnostic laparoscopy to visually confirm the presence or absence of endometriotic lesions.

INTERVENTION(S)

Metabolomic profiling of plasma samples based on ¹H-nuclear magnetic resonance (NMR) spectroscopy in combination with statistical approaches.

MAIN OUTCOME MEASURE(S)

Comparative identification of metabolites present in plasma from endometriosis patients and healthy women.

RESULT(S)

The plasma metabolomic profile of endometriosis patients was characterized by increased concentration of valine, fucose, choline-containing metabolites, lysine/arginine, and lipoproteins and decreased concentration of creatinine compared with healthy women. Metabolic alterations identified in the plasma metabolomic profile of endometriosis patients correlate with pathophysiologic events previously described in the progression of this disease.

CONCLUSION(S)

The results highlight the potential of ¹H-NMR-based metabolomics to characterize metabolic alterations associated with endometriosis in plasma samples. This information could be useful to get a better understanding of the molecular mechanisms involved in the pathogenesis of endometriosis, thus facilitating the noninvasive diagnosis of this pathology at early stages.

Combining NMR and LC/MS Using Backward Variable Elimination: Metabolomics Analysis of Colorectal Cancer, Polyps, and Healthy Controls

Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies.

Changes of serum amino acid profiles by an epidermal growth factor receptor mutation and benzo[a]pyrene in mouse lung tumorigenesis

Studies suggest that gene mutation and carcinogen exposure contribute to lung tumorigenesis including a mutation of epidermal growth factor receptor (EGFR) and exposure to benzo[a]pyrene (BaP). However, the interaction between EGFR mutation and BaP exposure during lung tumorigenesis is unclear. Metabolomics has become an important tool in clinical research and has been utilized to help our understanding of mechanisms and to identify indicators of cancers. This study's aim was to identify the changes in metabolite profiles in mice associated with an EGFR exon 21 deletion and/or BaP treatment-induced lung tumorigenesis. While the EGFR mutation increased the incidence of lung adenoma in transgenic mice (EGFR mutant mice) at 32 weeks of age, exposure to BaP caused the onset of lung tumorigenesis in these mice as early as 16 weeks after exposure. Using a metabolomics strategy involving liquid chromatography-mass spectrometry in conjunction with principal component analysis and confirmation by liquid chromatography triple quadrupole tandem mass spectrometry, we demonstrated that the serum amino acid profiles of these mice were changed. A total of eight amino acid concentrations were lower in EGFR mutant mice than in wild-type mice at 32 weeks of age. Five amino acids were lower in tumor-bearing mice than in non-tumor-bearing EGFR mutant mice at 10th week post-treatment of BaP, namely phenylalanine, tyrosine, alanine, proline, and threonine. Our results suggest that gene mutation and carcinogen exposure-induced lung adenomas share some common mechanisms. Changes in serum amino acid profiles may be early indicators of lung tumorigenesis.

Utilization of metabonomics to identify serum biomarkers in murine H22 hepatocarcinoma and deduce antitumor mechanism of Rhizoma Paridis saponins

Murine H22 hepatocarcinoma model is so popular to be used for the preclinical anticancer candidate's evaluation. However, the metabolic biomarkers of this model were not identified. Meanwhile, Rhizoma Paridis saponins (RPS) as natural products have been found to show strong antitumor activity, while its anti-cancer mechanism is not clear. To search for potential metabolite biomarkers of this model, serum metabonomics approach was applied to detect the variation of metabolite biomarkers and the related metabolism genes and signaling pathway were used to deduce the antitumor mechanisms of RPS. As a result, ten serum metabolites were identified in twenty-four mice including healthy mice, non-treated cancer mice, RPS-treated cancer mice and RPS-treated healthy mice. RPS significantly decreased tumor weight correlates to down-regulating lactate, acetate, N-acetyl amino acid and glutamine signals (p < 0.05), which were marked metabolites screened according to the very important person (VIP), loading plot and receiver operating characteristic curve (ROC) tests. For the analysis of metabolic enzyme related genes, RPS reversed the aerobic glycolysis through activating tumor suppressor p53 and PTEN, and suppressed FASN to inhibit lipogenesis. What's more, RPS repressed Myc and GLS expression and decreased glutamine level. The regulating PI3K/Akt/mTOR and HIF-1α/Myc/Ras networks also participated in these metabolic changes. Taken together, RPS suppressed ATP product made the tumor growth slow, which indicated a good anti-cancer effect and new angle for understanding the mechanism of RPS. In conclusion, this study demonstrated that the utility of (1)H NMR metabolic profiles taken together with tumor weight and viscera index was a promising screening tool for evaluating the antitumor effect of candidates. In addition, RPS was a potent anticancer agent through inhibiting cancer cellular metabolism to suppress proliferation in hepatoma H22 tumor murine, which promoted the application of RPS in the future.

(1)H NMR and MS based metabolomics study of the therapeutic effect of Cortex Fraxini on hyperuricemic rats

ETHNOPHARMACOLOGICAL RELEVANCE

Cortex Fraxini (CF) is an important traditional Chinese herbal medicine used for the treatment of gout and hyperuricemia.

AIM OF THE STUDY

The aim of this study was to evaluate the anti-hyperuricemic effect of CF on hyperuricemic rats and to investigate its mechanism of action.

MATERIALS AND METHODS

Metabolomics based on NMR and MS was used to study the therapeutic effect of CF on hyperuricemic rats. Plasma determination of uric acid (UA) showed that CF treatment markedly improved the UA level. Subsequently, metabolomics analysis was conducted using samples of plasma, kidney and urine, and orthogonal partial least squares-discriminant analysis (OPLS-DA) combined with principal component analysis (PCA) were used to detect potential biomarkers.

RESULTS

A total of 26 biomarkers were identified as being primarily involved in amino acid metabolism, lipid metabolism, purine metabolism, amino acid metabolism and carbohydrate metabolism, and hyperuricemia can disturb the balance of many of these metabolic pathways in vivo.

CONCLUSIONS

The variations in biomarkers revealed the therapeutic mechanism of CF, and a number of these biomarkers are not only significant for early diagnosis but also for predicting hyperuricemia.

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.

Temporal characterization of serum metabolite signatures in lung cancer patients undergoing treatment

Lung cancer causes more deaths in men and women than any other cancer related disease. Currently, few effective strategies exist to predict how patients will respond to treatment. We evaluated the serum metabolomic profiles of 25 lung cancer patients undergoing chemotherapy ± radiation to evaluate the feasibility of metabolites as temporal biomarkers of clinical outcomes. Serial serum specimens collected prospectively from lung cancer patients were analyzed using both nuclear magnetic resonance (¹H-NMR) spectroscopy and gas chromatography mass spectrometry (GC-MS). Multivariate statistical analysis consisted of unsupervised principal component analysis or orthogonal partial least squares discriminant analysis with significance assessed using a cross-validated ANOVA. The metabolite profiles were reflective of the temporal distinction between patient samples before during and after receiving therapy (¹H-NMR, p < 0.001: and GC-MS p < 0.01). Disease progression and survival were strongly correlative with the GC-MS metabolite data whereas stage and cancer type were associated with ¹H-NMR data. Metabolites such as hydroxylamine, tridecan-1-ol, octadecan-1-ol, were indicative of survival (GC-MS p < 0.05) and metabolites such as tagatose, hydroxylamine, glucopyranose, and threonine that were reflective of progression (GC-MS p < 0.05). Metabolite profiles have the potential to act as prognostic markers of clinical outcomes for lung cancer patients. Serial ¹H-NMR measurements appear to detect metabolites diagnostic of tumor pathology, while GC-MS provided data better related to prognostic clinical outcomes, possibility due to physiochemical bias related to specific biochemical pathways. These results warrant further study in a larger cohort and with various treatment options.

Metabolic differentiation and classification of abnormal Savda Munziq's pharmacodynamic role on rat models with different diseases by nuclear magnetic resonance-based metabonomics

Background:

Abnormal Savda Munziq (ASMq) is a traditional Uyghur herbal preparation used as a therapy for abnormal Savda-related diseases. In this study, we investigate ASMq's dynamic effects on abnormal Savda rat models under different disease conditions.

Materials and Methods:

Abnormal Savda rat models with hepatocellular carcinoma (HCC), type 2 diabetes mellitus (T2DM), and asthma dosed of ASMq. Serum samples of each animal tested by nuclear magnetic resonance spectroscopy and analyzed by orthogonal projection to latent structure with discriminant analysis.

Results:

Compared with healthy controls, HCC rats had higher concentrations of amino acids, fat-related metabolites, lactate, myoinositol, and citrate, but lower concentrations of α-glucose, β-glucose, and glutamine. Following ASMq treatment, the serum acetone very low-density lipoprotein (VLDL), LDL, unsaturated lipids, acetylcysteine, and pyruvate concentration decreased, but α-glucose, β-glucose, and glutamine concentration increased (P < 0.05). T2DM rats had higher concentrations of α- and β-glucose, but lower concentrations of isoleucine, leucine, valine, glutamine, glycoprotein, lactate, tyrosine, creatine, alanine, carnitine, and phenylalanine. After ASMq treated T2DM groups showed reduced α- and β-glucose and increased creatine levels (P < 0.05). Asthma rats had higher acetate, carnitine, formate, and phenylalanine levels, but lower concentrations of glutamine, glycoprotein, lactate, VLDL, LDL, and unsaturated lipids. ASMq treatment showed increased glutamine and reduced carnitine, glycoprotein, formate, and phenylalanine levels (P < 0.05).

Conclusion:

Low immune function, decreased oxidative defense, liver function abnormalities, amino acid deficiencies, and energy metabolism disorders are common characteristics of abnormal Savda-related diseases. ASMq may improve the abnormal metabolism and immune function of rat models with different diseases combined abnormal Savda.

Diagnostic Metabolomic Blood Tests for Endoluminal Gastrointestinal Cancer--A Systematic Review and Assessment of Quality

Advances in analytics have resulted in metabolomic blood tests being developed for the detection of cancer. This systematic review aims to assess the diagnostic accuracy of blood-based metabolomic biomarkers for endoluminal gastrointestinal (GI) cancer. Using endoscopic diagnosis as a reference standard, methodologic and reporting quality was assessed using validated tools, in addition to pathway-based informatics to biologically contextualize discriminant features. Twenty-nine studies (15 colorectal, 9 esophageal, 3 gastric, and 2 mixed) with data from 10,835 participants were included. All reported significant differences in hematologic metabolites. In pooled analysis, 246 metabolites were found to be significantly different after multiplicity correction. Incremental metabolic flux with disease progression was frequently reported. Two promising candidates have been validated in independent populations (both colorectal biomarkers), and one has been approved for clinical use. Networks analysis suggested modulation of elements of up to half of Edinburgh Human Metabolic Network subdivisions, and that the poor clinical applicability of commonly modulated metabolites could be due to extensive molecular interconnectivity. Methodologic and reporting quality was assessed as moderate-to-poor. Serum metabolomics holds promise for GI cancer diagnostics; however, future efforts must adhere to consensus standardization initiatives, utilize high-resolution discovery analytics, and compare candidate biomarkers with peer nonendoscopic alternatives.

Can NMR solve some significant challenges in metabolomics?

The field of metabolomics continues to witness rapid growth driven by fundamental studies, methods development, and applications in a number of disciplines that include biomedical science, plant and nutrition sciences, drug development, energy and environmental sciences, toxicology, etc. NMR spectroscopy is one of the two most widely used analytical platforms in the metabolomics field, along with mass spectrometry (MS). NMR's excellent reproducibility and quantitative accuracy, its ability to identify structures of unknown metabolites, its capacity to generate metabolite profiles using intact bio-specimens with no need for separation, and its capabilities for tracing metabolic pathways using isotope labeled substrates offer unique strengths for metabolomics applications. However, NMR's limited sensitivity and resolution continue to pose a major challenge and have restricted both the number and the quantitative accuracy of metabolites analyzed by NMR. Further, the analysis of highly complex biological samples has increased the demand for new methods with improved detection, better unknown identification, and more accurate quantitation of larger numbers of metabolites. Recent efforts have contributed significant improvements in these areas, and have thereby enhanced the pool of routinely quantifiable metabolites. Additionally, efforts focused on combining NMR and MS promise opportunities to exploit the combined strength of the two analytical platforms for direct comparison of the metabolite data, unknown identification and reliable biomarker discovery that continue to challenge the metabolomics field. This article presents our perspectives on the emerging trends in NMR-based metabolomics and NMR's continuing role in the field with an emphasis on recent and ongoing research from our laboratory.

Identification of novel candidate plasma metabolite biomarkers for distinguishing serous ovarian carcinoma and benign serous ovarian tumors

OBJECTIVE

Serous ovarian carcinoma (OC) represents a leading cause of cancer-related death among U.S. women. Non-invasive tools have recently emerged for discriminating benign from malignant ovarian masses, but evaluation remains ongoing, without widespread implementation. In the last decade, metabolomics has matured into a new avenue for cancer biomarker development. Here, we sought to identify novel plasma metabolite biomarkers to distinguish serous ovarian carcinoma and benign serous ovarian tumor.

METHODS

Using liquid chromatography-mass spectrometry, we conducted global and targeted metabolite profiling of plasma isolated at the time of surgery from 50 serous OC cases and 50 serous benign controls.

RESULTS

Global lipidomics analysis identified 34 metabolites (of 372 assessed) differing significantly (P<0.05) between cases and controls in both training and testing sets, with 17 candidates satisfying FDR q<0.05, and two reaching Bonferroni significance. Targeted profiling of ~150 aqueous metabolites identified a single amino acid, alanine, as differentially abundant (P<0.05). A multivariate classification model built using the top four lipid metabolites achieved an estimated AUC of 0.85 (SD=0.07) based on Monte Carlo cross validation. Evaluation of a hybrid model incorporating both CA125 and lipid metabolites was suggestive of increased classification accuracy (AUC=0.91, SD=0.05) relative to CA125 alone (AUC=0.87, SD=0.07), particularly at high fixed levels of sensitivity, without reaching significance.

CONCLUSIONS

Our results provide insight into metabolic changes potentially correlated with the presence of serous OC versus benign ovarian tumor and suggest that plasma metabolites may help differentiate these two conditions.

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.

Biomarkers in Barrett's Esophagus: Role in Diagnosis, Risk Stratification, and Prediction of Response to Therapy

Esophageal adenocarcinoma (EAC) has increased dramatically in the past 3 decades, making its precursor lesion Barrett's esophagus (BE) an important clinical problem. Effective interventions are available, but overall outcomes remain unchanged. Most of the BE population remains undiagnosed; most EACs are diagnosed late, and most BE patients will never progress to cancer. These epidemiologic factors make upper endoscopy an inefficient and ineffective strategy for BE diagnosis and risk stratification. In the current review, biomarkers for diagnosis, risk stratification, and predictors of response to therapy in BE are discussed.

Determination of proline in human serum by a robust LC-MS/MS method: application to identification of human metabolites as candidate biomarkers for esophageal cancer early detection and risk stratification

Altered serum proline levels are related to cancer metabolism. This study developed and validated a LC-MS/MS method to analyze proline in human serum. Surrogate blank serum, coupled with stable isotope l-proline-(13) C5 ,(15)  N as internal standard, was used for generating standard curves ranging from 2.5 to 100 μg/mL. Proline was extracted from serum samples using methanol. A Phenomenex Lux 5u Cellulose-1 column (250 × 4.6 mm) was used for chromatographic separation with 40% methanol in 0.05% formic acid aqueous solution as a mobile phase. Mass detection was performed under positive ionization electrospray. Intra- and inter-day accuracy and precision were <10%. The extraction recovery and matrix factor were 99.17 and 1.47%, respectively. Our study showed that the chiral column had high specificity and selectivity for separating proline from serum components. The assay was successfully applied for the quantification of human serum proline levels from 30 esophageal cancer patients and 30 healthy volunteers. Statistical analyses showed significantly lower levels of serum proline in the patients as compared with the healthy volunteers (p-value = 0.011). We report here a simple, specific and reproducible LC-MS/MS method for the quantification of proline in human serum as a potential screening biomarker for esophageal cancer.

Precision prevention of oesophageal adenocarcinoma

The incidence of oesophageal adenocarcinoma has risen rapidly over the past four decades. Unfortunately, treatments have not kept pace; unless their cancer is identified at a very early stage, most patients will not survive a year after diagnosis. The beginnings of this widespread problem were first recognized over 25 years ago, yet rates have continued to rise against a backdrop of much improved understanding and management of oesophageal adenocarcinoma. We estimate that only ∼7% of the 10,000 cases of oesophageal adenocarcinoma diagnosed annually in the USA are identified through current approaches to cancer control, and trace pathways by which the remaining 93% are 'lost'. On the basis of emerging data on aetiology and predictive factors, together with new diagnostic tools, we suggest a five-tier strategy for prevention and control that begins with a wide population base and triages individuals into progressively higher risk strata, each with risk-appropriate prevention, screening and treatment options.

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.

Haystack, a web-based tool for metabolomics research

Background

Liquid chromatography coupled to mass spectrometry (LCMS) has become a widely used technique in metabolomics research for differential profiling, the broad screening of biomolecular constituents across multiple samples to diagnose phenotypic differences and elucidate relevant features. However, a significant limitation in LCMS-based metabolomics is the high-throughput data processing required for robust statistical analysis and data modeling for large numbers of samples with hundreds of unique chemical species.

Results

To address this problem, we developed Haystack, a web-based tool designed to visualize, parse, filter, and extract significant features from LCMS datasets rapidly and efficiently. Haystack runs in a browser environment with an intuitive graphical user interface that provides both display and data processing options. Total ion chromatograms (TICs) and base peak chromatograms (BPCs) are automatically displayed, along with time-resolved mass spectra and extracted ion chromatograms (EICs) over any mass range. Output files in the common .csv format can be saved for further statistical analysis or customized graphing. Haystack's core function is a flexible binning procedure that converts the mass dimension of the chromatogram into a set of interval variables that can uniquely identify a sample. Binned mass data can be analyzed by exploratory methods such as principal component analysis (PCA) to model class assignment and identify discriminatory features. The validity of this approach is demonstrated by comparison of a dataset from plants grown at two light conditions with manual and automated peak detection methods. Haystack successfully predicted class assignment based on PCA and cluster analysis, and identified discriminatory features based on analysis of EICs of significant bins.

Conclusion

Haystack, a new online tool for rapid processing and analysis of LCMS-based metabolomics data is described. It offers users a range of data visualization options and supports non-biased differential profiling studies through a unique and flexible binning function that provides an alternative to conventional peak deconvolution analysis methods.

Loss of HSulf-1 promotes altered lipid metabolism in ovarian cancer

Background

Loss of the endosulfatase HSulf-1 is common in ovarian cancer, upregulates heparin binding growth factor signaling and potentiates tumorigenesis and angiogenesis. However, metabolic differences between isogenic cells with and without HSulf-1 have not been characterized upon HSulf-1 suppression in vitro. Since growth factor signaling is closely tied to metabolic alterations, we determined the extent to which HSulf-1 loss affects cancer cell metabolism.

Results

Ingenuity pathway analysis of gene expression in HSulf-1 shRNA-silenced cells (Sh1 and Sh2 cells) compared to non-targeted control shRNA cells (NTC cells) and subsequent Kyoto Encyclopedia of Genes and Genomics (KEGG) database analysis showed altered metabolic pathways with changes in the lipid metabolism as one of the major pathways altered inSh1 and 2 cells. Untargeted global metabolomic profiling in these isogenic cell lines identified approximately 338 metabolites using GC/MS and LC/MS/MS platforms. Knockdown of HSulf-1 in OV202 cells induced significant changes in 156 metabolites associated with several metabolic pathways including amino acid, lipids, and nucleotides. Loss of HSulf-1 promoted overall fatty acid synthesis leading to enhance the metabolite levels of long chain, branched, and essential fatty acids along with sphingolipids. Furthermore, HSulf-1 loss induced the expression of lipogenic genes including FASN, SREBF1, PPARγ, and PLA2G3 stimulated lipid droplet accumulation. Conversely, re-expression of HSulf-1 in Sh1 cells reduced the lipid droplet formation. Additionally, HSulf-1 also enhanced CPT1A and fatty acid oxidation and augmented the protein expression of key lipolytic enzymes such as MAGL, DAGLA, HSL, and ASCL1. Overall, these findings suggest that loss of HSulf-1 by concomitantly enhancing fatty acid synthesis and oxidation confers a lipogenic phenotype leading to the metabolic alterations associated with the progression of ovarian cancer.

Conclusions

Taken together, these findings demonstrate that loss of HSulf-1 potentially contributes to the metabolic alterations associated with the progression of ovarian pathogenesis, specifically impacting the lipogenic phenotype of ovarian cancer cells that can be therapeutically targeted.

Metabolomics for biomarker discovery in gastroenterological cancer

The study of the omics cascade, which involves comprehensive investigations based on genomics, transcriptomics, proteomics, metabolomics, etc., has developed rapidly and now plays an important role in life science research. Among such analyses, metabolome analysis, in which the concentrations of low molecular weight metabolites are comprehensively analyzed, has rapidly developed along with improvements in analytical technology, and hence, has been applied to a variety of research fields including the clinical, cell biology, and plant/food science fields. The metabolome represents the endpoint of the omics cascade and is also the closest point in the cascade to the phenotype. Moreover, it is affected by variations in not only the expression but also the enzymatic activity of several proteins. Therefore, metabolome analysis can be a useful approach for finding effective diagnostic markers and examining unknown pathological conditions. The number of studies involving metabolome analysis has recently been increasing year-on-year. Here, we describe the findings of studies that used metabolome analysis to attempt to discover biomarker candidates for gastroenterological cancer and discuss metabolome analysis-based disease diagnosis.

Global metabolite profiling of synovial fluid for the specific diagnosis of rheumatoid arthritis from other inflammatory arthritis

Currently, reliable biomarkers that can be used to distinguish rheumatoid arthritis (RA) from other inflammatory diseases are unavailable. To find possible distinctive metabolic patterns and biomarker candidates for RA, we performed global metabolite profiling of synovial fluid samples. Synovial fluid samples from 38 patients with RA, ankylosing spondylitis, Behçet's disease, and gout were analyzed by gas chromatography/time-of-flight mass spectrometry (GC/TOF MS). Orthogonal partial least-squares discriminant and hierarchical clustering analyses were performed for the discrimination of RA and non-RA groups. Variable importance for projection values were determined, and the Wilcoxon-Mann-Whitney test and the breakdown and one-way analysis of variance were conducted to identify potential biomarkers for RA. A total of 105 metabolites were identified from synovial fluid samples. The score plot of orthogonal partial least squares discriminant analysis showed significant discrimination between the RA and non-RA groups. The 20 metabolites, including citrulline, succinate, glutamine, octadecanol, isopalmitic acid, and glycerol, were identified as potential biomarkers for RA. These metabolites were found to be associated with the urea and TCA cycles as well as fatty acid and amino acid metabolism. The metabolomic analysis results demonstrated that global metabolite profiling by GC/TOF MS might be a useful tool for the effective diagnosis and further understanding of RA.

Metabolome analysis for discovering biomarkers of gastroenterological cancer

Improvements in analytical technologies have made it possible to rapidly determine the concentrations of thousands of metabolites in any biological sample, which has resulted in metabolome analysis being applied to various types of research, such as clinical, cell biology, and plant/food science studies. The metabolome represents all of the end products and by-products of the numerous complex metabolic pathways operating in a biological system. Thus, metabolome analysis allows one to survey the global changes in an organism's metabolic profile and gain a holistic understanding of the changes that occur in organisms during various biological processes, e.g., during disease development. In clinical metabolomic studies, there is a strong possibility that differences in the metabolic profiles of human specimens reflect disease-specific states. Recently, metabolome analysis of biofluids, e.g., blood, urine, or saliva, has been increasingly used for biomarker discovery and disease diagnosis. Mass spectrometry-based techniques have been extensively used for metabolome analysis because they exhibit high selectivity and sensitivity during the identification and quantification of metabolites. Here, we describe metabolome analysis using liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and capillary electrophoresis-mass spectrometry. Furthermore, the findings of studies that attempted to discover biomarkers of gastroenterological cancer are also outlined. Finally, we discuss metabolome analysis-based disease diagnosis.

Biomarker Discovery and Translation in Metabolomics

The multifaceted field of metabolomics has witnessed exponential growth in both methods development and applications. Owing to the urgent need, a significant fraction of research investigations in the field is focused on understanding, diagnosing and preventing human diseases; hence, the field of biomedicine has been the major beneficiary of metabolomics research. A large body of literature now documents the discovery of numerous potential biomarkers and provides greater insights into pathogeneses of numerous human diseases. A sizable number of findings have been tested for translational applications focusing on disease diagnostics ranging from early detection, to therapy prediction and prognosis, monitoring treatment and recurrence detection, as well as the important area of therapeutic target discovery. Current advances in analytical technologies promise quantitation of biomarkers from even small amounts of bio-specimens using non-invasive or minimally invasive approaches, and facilitate high-throughput analysis required for real time applications in clinical settings. Nevertheless, a number of challenges exist that have thus far delayed the translation of a majority of promising biomarker discoveries to the clinic. This article presents advances in the field of metabolomics with emphasis on biomarker discovery and translational efforts, highlighting the current status, challenges and future directions.

Metabolomic profiling of oesophago-gastric cancer: a systematic review

AIMS

This review aims to identify metabolomic biomarkers of oesophago-gastric (OG) cancer in human biological samples, and to discuss the dominant metabolic pathways associated with the observed changes.

METHODS

A systematic review of the literature, up to and including 9th November 2012, was conducted for experimental studies investigating the metabolomic profile of human biological samples from patients with OG cancer compared to a control group. Inclusion criteria for analytical platforms were mass spectrometry or nuclear magnetic resonance spectroscopy. The QUADAS-2 tool was used to assess the quality of the included studies.

RESULTS

Twenty studies met the inclusion criteria and samples utilised for metabolomic analysis included tissue (n = 11), serum (n = 8), urine (n = 1) and gastric content (n = 1). Several metabolites of glycolysis, the tricarboxylic acid cycle, anaerobic respiration and protein/lipid metabolism were found to be significantly different between cancer and control samples. Lactate and fumurate were the most commonly recognised biomarkers of OG cancer related to cellular respiration. Valine, glutamine and glutamate were the most commonly identified amino acid biomarkers. Products of lipid metabolism including saturated and un-saturated free fatty acids, ketones and aldehydes and triacylglycerides were also identified as biomarkers of OG cancer. Unclear risk of bias for patient selection was reported for the majority of studies due to the lack of clarity regarding patient recruitment.

CONCLUSION

The application of metabolomics for biomarker detection in OG cancer presents new opportunities for the purposes of screening and therapeutic monitoring. Future studies should provide clear details of patient selection and develop metabolite assays suitable for progress beyond phase 1 pre-clinical exploratory studies.

Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer

Breast cancer is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. As an example, only some women will benefit from chemotherapy. Identifying patients who will respond to chemotherapy and thereby improve their long-term survival has important implications to treatment protocols and outcomes, while identifying non responders may enable these patients to avail themselves of other investigational approaches or other potentially effective treatments. In this study, serum metabolite profiling was performed to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy for breast cancer. Metabolic profiles of serum from patients with complete (n = 8), partial (n = 14) and no response (n = 6) to chemotherapy were studied using a combination of nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography-mass spectrometry (LC-MS) and statistical analysis methods. The concentrations of four metabolites, three (threonine, isoleucine, glutamine) from NMR and one (linolenic acid) from LC-MS were significantly different when comparing response to chemotherapy. A prediction model developed by combining NMR and MS derived metabolites correctly identified 80% of the patients whose tumors did not show complete response to chemotherapy. These results show promise for larger studies that could result in more personalized treatment protocols for breast cancer patients.

Early diagnostic biomarkers for esophageal adenocarcinoma--the current state of play

Esophageal adenocarcinoma (EAC) is one of the two most common types of esophageal cancer with alarming increase in incidence and very poor prognosis. Aiming to detect EAC early, currently high-risk patients are monitored using an endoscopic-biopsy approach. However, this approach is prone to sampling error and interobserver variability. Diagnostic tissue biomarkers related to genomic and cell-cycle abnormalities have shown promising results, although with current technology these tests are difficult to implement in the screening of high-risk patients for early neoplastic changes. Differential miRNA profiles and aberrant protein glycosylation in tissue samples have been reported to improve performance of existing tissue-based diagnostic biomarkers. In contrast to tissue biomarkers, circulating biomarkers are more amenable to population-screening strategies, due to the ease and low cost of testing. Studies have already shown altered circulating glycans and DNA methylation in BE/EAC, whereas disease-associated changes in circulating miRNA remain to be determined. Future research should focus on identification and validation of these circulating biomarkers in large-scale trials to develop in vitro diagnostic tools to screen population at risk for EAC development.

1H-NMR based metabonomic profiling of human esophageal cancer tissue

BACKGROUND

The biomarker identification of human esophageal cancer is critical for its early diagnosis and therapeutic approaches that will significantly improve patient survival. Specially, those that involves in progression of disease would be helpful to mechanism research.

METHODS

In the present study, we investigated the distinguishing metabolites in human esophageal cancer tissues (n = 89) and normal esophageal mucosae (n = 26) using a (1)H nuclear magnetic resonance ((1)H-NMR) based assay, which is a highly sensitive and non-destructive method for biomarker identification in biological systems. Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) were applied to analyse (1)H-NMR profiling data to identify potential biomarkers.

RESULTS

The constructed OPLS-DA model achieved an excellent separation of the esophageal cancer tissues and normal mucosae. Excellent separation was obtained between the different stages of esophageal cancer tissues (stage II = 28; stage III = 45 and stage IV = 16) and normal mucosae. A total of 45 metabolites were identified, and 12 of them were closely correlated with the stage of esophageal cancer. The downregulation of glucose, AMP and NAD, upregulation of formate indicated the large energy requirement due to accelerated cell proliferation in esophageal cancer. The increases in acetate, short-chain fatty acid and GABA in esophageal cancer tissue revealed the activation of fatty acids metabolism, which could satisfy the need for cellular membrane formation. Other modified metabolites were involved in choline metabolic pathway, including creatinine, creatine, DMG, DMA and TMA. These 12 metabolites, which are involved in energy, fatty acids and choline metabolism, may be associated with the progression of human esophageal cancer.

CONCLUSION

Our findings firstly identify the distinguishing metabolites in different stages of esophageal cancer tissues, indicating the attribution of metabolites disturbance to the progression of esophageal cancer. The potential biomarkers provide a promising molecular diagnostic approach for clinical diagnosis of human esophageal cancer and a new direction for the mechanism study.

Metabolic signatures of esophageal cancer: NMR-based metabolomics and UHPLC-based focused metabolomics of blood serum

Focused metabolic profiling is a powerful tool for the determination of biomarkers. Here, a more global proton nuclear magnetic resonance ((1)H NMR)-based metabolomic approach coupled with a relative simple ultra high performance liquid chromatography (UHPLC)-based focused metabolomic approach was developed and compared to characterize the systemic metabolic disturbances underlying esophageal cancer (EC) and identify possible early biomarkers for clinical prognosis. Serum metabolic profiling of patients with EC (n=25) and healthy controls (n=25) was performed by using both (1)H NMR and UHPLC, and metabolite identification was achieved by multivariate statistical analysis. Using orthogonal projection to least squares discriminant analysis (OPLS-DA), we could distinguish EC patients from healthy controls. The predictive power of the model derived from the UHPLC-based focused metabolomics performed better in both sensitivity and specificity than the results from the NMR-based metabolomics, suggesting that the focused metabolomic technique may be of advantage in the future for the determination of biomarkers. Moreover, focused metabolic profiling is highly simple, accurate and specific, and should prove equally valuable in metabolomic research applications. A total of nineteen significantly altered metabolites were identified as the potential disease associated biomarkers. Significant changes in lipid metabolism, amino acid metabolism, glycolysis, ketogenesis, tricarboxylic acid (TCA) cycle and energy metabolism were observed in EC patients compared with the healthy controls. These results demonstrated that metabolic profiling of serum could be useful as a screening tool for early EC diagnosis and prognosis, and might enhance our understanding of the mechanisms involved in the tumor progression.

A micro-extraction technique using a new digitally controlled syringe combined with UHPLC for assessment of urinary biomarkers of oxidatively damaged DNA

The formation of reactive oxygen species (ROS) within cells causes damage to biomolecules, including membrane lipids, DNA, proteins and sugars. An important type of oxidative damage is DNA base hydroxylation which leads to the formation of 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG) and 5-hydroxymethyluracil (5-HMUra). Measurement of these biomarkers in urine is challenging, due to the low levels of the analytes and the matrix complexity. In order to simultaneously quantify 8-oxodG and 5-HMUra in human urine, a new, reliable and powerful strategy was optimised and validated. It is based on a semi-automatic microextraction by packed sorbent (MEPS) technique, using a new digitally controlled syringe (eVol(®)), to enhance the extraction efficiency of the target metabolites, followed by a fast and sensitive ultrahigh pressure liquid chromatography (UHPLC). The optimal methodological conditions involve loading of 250 µL urine sample (1:10 dilution) through a C8 sorbent in a MEPS syringe placed in the semi-automatic eVol(®) syringe followed by elution using 90 µL of 20% methanol in 0.01% formic acid solution. The obtained extract is directly analysed in the UHPLC system using a binary mobile phase composed of aqueous 0.1% formic acid and methanol in the isocratic elution mode (3.5 min total analysis time). The method was validated in terms of selectivity, linearity, limit of detection (LOD), limit of quantification (LOQ), extraction yield, accuracy, precision and matrix effect. Satisfactory results were obtained in terms of linearity (r(2) > 0.991) within the established concentration range. The LOD varied from 0.00005 to 0.04 µg mL(-1) and the LOQ from 0.00023 to 0.13 µg mL(-1). The extraction yields were between 80.1 and 82.2 %, while inter-day precision (n = 3 days) varied between 4.9 and 7.7 % and intra-day precision between 1.0 and 8.3 %. This approach presents as main advantages the ability to easily collect and store urine samples for further processing and the high sensitivity, reproducibility, and robustness of eVol(®)MEPS combined with UHPLC analysis, thus retrieving a fast and reliable assessment of oxidatively damaged DNA.

Study of metabonomic profiles of human esophageal carcinoma by use of high-resolution magic-angle spinning 1H NMR spectroscopy and multivariate data analysis

Esophageal carcinoma (EC) is one of the most common malignant tumors. EC survival has remained disappointingly low because of the high malignancy of esophageal cancer and the lack of obvious clinical symptoms at an early stage. Early diagnosis is often difficult because the small tumor nodules are frequently missed. Metabonomics based on high-resolution magic-angle spinning (HRMAS) NMR has been popular for tumor detection because it is highly sensitive, provides rich biochemical information and requires no sample pretreatment. (1)H HRMAS spectra of non-involved adjacent esophageal tissues and of well differentiated and moderately differentiated esophageal carcinoma tumors were recorded and analyzed by use of multivariate and statistical analysis techniques. Moderately differentiated EC tumors were found to have increased total choline, alanine, and glutamate and reduced creatine, myo-inositol, and taurine compared with non-involved adjacent tissues. Moreover, clear differences between the metabonomic profiles of EC tissues enabled tumor differentiation. Furthermore, the integral Gly/MI ratio for samples of different tissue types were statistically significantly different; this was sufficient both for distinguishing non-involved tissues from esophageal carcinoma and for classification of well differentiated and moderately differentiated EC tumors.

A rough guide to metabolite identification using high resolution liquid chromatography mass spectrometry in metabolomic profiling in metazoans

Compound identification in mass spectrometry based metabolomics can be a problem but sometimes the problem seems to be presented in an over complicated way. The current review focuses on metazoans where the range of metabolites is more restricted than for example in plants. The focus is on liquid chromatography with high resolution mass spectrometry where it is proposed that most of the problems in compound identification relate to structural isomers rather than to isobaric compounds. Thus many of the problems faced relate to separation of isomers, which is usually required even if fragmentation is used to support structural identification. Many papers report the use of MS/MS or MS² as an adjunct to the identification of known metabolites but there a few examples in metabolomics studies of metazoans of complete structure elucidation of novel metabolites or metabolites where no authentic standards are available for comparison.

The Fanconi anemia pathway: repairing the link between DNA damage and squamous cell carcinoma

Fanconi anemia (FA) is a rare inherited recessive disease caused by mutations in one of fifteen genes known to encode FA pathway components. In response to DNA damage, nuclear FA proteins associate into high molecular weight complexes through a cascade of post-translational modifications and physical interactions, followed by the repair of damaged DNA. Hematopoietic cells are particularly sensitive to the loss of these interactions, and bone marrow failure occurs almost universally in FA patients. FA as a disease is further characterized by cancer susceptibility, which highlights the importance of the FA pathway in tumor suppression, and will be the focus of this review. Acute myeloid leukemia is the most common cancer type, often subsequent to bone marrow failure. However, FA patients are also at an extreme risk of squamous cell carcinoma (SCC) of the head and neck and gynecological tract, with an even greater incidence in those individuals who have received a bone marrow transplant and recovered from hematopoietic disease. FA tumor suppression in hematopoietic versus epithelial compartments could be mechanistically similar or distinct. Definition of compartment specific FA activities is now critical to assess the effects of today's bone marrow failure treatments on tomorrow's solid tumor development. It is our hope that current therapies can then be optimized to decrease the risk of malignant transformation in both hematopoietic and epithelial cells. Here we review our current understanding of the mechanisms of action of the Fanconi anemia pathway as it contributes to stress responses, DNA repair and squamous cell carcinoma susceptibility.

Differentiating hepatocellular carcinoma from hepatitis C using metabolite profiling

Hepatocellular carcinoma (HCC) accounts for most liver cancer cases worldwide. Contraction of the hepatitis C virus (HCV) is considered a major risk factor for liver cancer. In order to identify the risk of cancer, metabolic profiling of serum samples from patients with HCC (n=40) and HCV (n=22) was performed by ¹H nuclear magnetic resonance spectroscopy. Multivariate statistical analysis showed a distinct separation of the two patient cohorts, indicating a distinct metabolic difference between HCC and HCV patient groups based on signals from lipids and other individual metabolites. Univariate analysis showed that three metabolites (choline, valine and creatinine) were significantly altered in HCC. A PLS-DA model based on these three metabolites showed a sensitivity of 80%, specificity of 71% and an area under the receiver operating curve of 0.83, outperforming the clinical marker alpha-fetoprotein (AFP). The robustness of the model was tested using Monte-Carlo cross validation (MCCV). This study showed that metabolite profiling could provide an alternative approach for HCC screening in HCV patients, many of whom have high risk for developing liver cancer.

Revealing the metabonomic variation of EC using ¹H-NMR spectroscopy and its association with the clinicopathological characteristics

In this study, (1)H NMR-based metabonomics has been applied to investigate esophageal cancer metabolic signatures in plasma and urine, purpose of assessing the diagnostic potential of this approach and gaining novel insights into esophageal cancer metabolism and systemic effects. Plasma and urine samples from esophageal cancer patients (n = 108) and a control healthy group (n = 40) were analyzed by Nuclear Magnetic Resonance (NMR) spectroscopy (600 MHz), and their spectral profiles subjected to Orthogonal Projections to Latent Structures (OPLS-DA) for multivariate statistics. Potential metabolic biomarkers were identified using data base comparisons used for examining the significance of metabolites. Compared to healthy controls, esophageal cancer plasma had higher levels of dimethylamine, α-glucose, β-glucose, citric acid, together with lower levels of Leucine, alanine, isoleucine, valine, glycoprotein, lactate, acetone, acetate, choline, isobutyrate, unsaturated lipid, VLDL, LDL, 1-methylhistidine; Compared to healthy controls, esophageal cancer urine had higher levels of Mannitol, glutamate, γ-propalanine, phenylalanine, acetate, allantoin, pyruvate, tyrosine, β-glucose and guinolinate, together with lower levels of N-acetylcysteine, valine, dihydrothymine, hippurate, methylguanidine, 1-methylnicotin- amide and Citric acid; Very good discrimination between cancer and control groups was achieved by multivariate modeling of plasma and urinary profiles. (1)H NMR-based metabolite profiling analysis was shown to be an effective approach to differentiating between patients with EC and healthy subjects. Good sensitivity and selectivity were shown by using the metabolite markers discovered to predict the classification of samples from the healthy control group and the patients with the disease. Plasma and urine metabolic profiling may have potential for early diagnosis of EC and may enhance our understanding of its mechanisms.