Metabolomic study for diagnostic model of oesophageal cancer using gas chromatography/mass spectrometry

Esophageal cancer risk is influenced by genetically determined blood metabolites

It remains unclear what causes esophageal cancer (EC), but blood metabolites have been connected to it. Our study performed a Mendelian randomization (MR) analysis to assess the causality from genetically proxied 1400 blood metabolites to EC level. A two-sample MR analysis was employed to evaluate the causal relationship between 1400 blood metabolites and EC. Initially, the EC genome-wide association study (GWAS) data (from Jiang L et al) were examined, leading to the identification of certain metabolites. Subsequently, another set of EC GWAS data from FINNGEN was utilized to validate the findings. Causality was primarily determined through inverse variance weighting, with additional support from the MR-Egger, weighted median, and MR-PRESSO models. Heterogeneity was assessed using the MR Cochran Q test. The MR-Egger intercept and MR-PRESSO global methods were employed to detect multicollinearity. In this study, Bonferroni corrected P value was used for significance threshold. We found 2 metabolites with overlaps, which are lipids. Docosatrienoate (22:3n3) was found to be causally associated with a decreased risk of EC, as evidenced by the EC GWAS data (from Jiang et al) (odds ratio [OR] = 0.620, 95% confidence interval [CI] = 0.390-0.986, P = .044) and the EC GWAS data (from FINNGEN) (OR = 0.77, 95% CI = 0.6-0.99, P = .042), these results were consistent across both data sets. Another overlapping metabolite, glycosyl-N-(2-hydroxyneuramoyl)-sphingosine, was associated with the risk of ES, with EC GWAS data (from Jiang L et al) (OR = 1.536, 95% CI = 1.000-2.360, P = .049), while EC GWAS data (from FINNGEN) (OR = 0.733, 95% CI = 0.574-0.937, P = .013), the 2 data had opposite conclusions. The findings of this study indicate a potential association between lipid metabolites (Docosatrienoate (22:3n3) and glycosyl-N-(2-hydroxynervonoyl)-sphingosine (d18:1/24:1 (2OH))) and the risk of esophageal carcinogenesis.

The Role of Amino Acids in Non-Enzymatic Antioxidant Mechanisms in Cancer: A Review

Currently, the antioxidant properties of amino acids and their role in the physicochemical processes accompanying oxidative stress in cancer remain unclear. Cancer cells are known to extensively uptake amino acids, which are used as an energy source, antioxidant precursors that reduce oxidative stress in cancer, and as regulators of inhibiting or inducing tumor cell-associated gene expression. This review examines nine amino acids (Cys, His, Phe, Met, Trp, Tyr, Pro, Arg, Lys), which play a key role in the non-enzymatic oxidative process in various cancers. Conventionally, these amino acids can be divided into two groups, in one of which the activity increases (Cys, Phe, Met, Pro, Arg, Lys) in cancer, and in the other, it decreases (His, Trp, Tyr). The review examines changes in the metabolism of nine amino acids in eleven types of oncology. We have identified the main nonspecific mechanisms of changes in the metabolic activity of amino acids, and described direct and indirect effects on the redox homeostasis of cells. In the future, this will help to understand better the nature of life of a cancer cell and identify therapeutic targets more effectively.

Development of a Non-invasive Diagnostic Method for Esophageal Squamous Cell Carcinoma by Gas Chromatographic Analysis of Exhaled Breath

Objectives

Since esophageal carcinoma progresses asymptomatically, for many patients the disease is already advanced at the time of diagnosis. The main methods that are currently used to diagnose esophageal carcinoma are upper gastrointestinal radiographic contrast examinations and upper gastrointestinal endoscopy, but early discovery of this disease remains difficult. There is a need to develop a diagnostic method using biomarkers that is non-invasive while both highly sensitive and specific.

Materials and Methods

Exhaled breath was collected from 17 patients with esophageal squamous cell carcinoma (ESCC), as well as 9 control subjects without history of any cancer. For each fasting subject, 1L of exhaled breath was collected in a gas sampling bag. Volatile organic compounds (VOCs) were then extracted from each sample using Solid phase micro-extraction (SPME) fibers and analyzed by gas chromatography-mass spectrometry (GC-MS).

Results

Levels of acetonitrile, acetic acid, acetone, and 2-butanone in exhaled breath were significantly higher in the patient group than in the control group (p = 0.0037, 0.0024, 0.0024 and 0.0037, respectively). ROC curves were drawn for these 4 VOCs, and the results for the area-under-the-curve (AUC) indicated that ESCC patients can be identified with a high probability of 0.93.

Conclusion

We found distinctive VOCs in exhaled breath of ESCC patients. These VOCs have a potential as new clinical biomarkers for ESCC. The measurement of VOCs in exhaled breath may be a useful, non-invasive method for diagnosis of ESCC.

Metabolomics Analysis of the Effect of GAT-2 Deficiency on Th1 Cells in Mice

The classic neurotransmitter γ-aminobutyric acid (GABA) has been shown to shape the activation and function of immune cells. There are four high-affinity GABA transporters (GATs, including GAT-1, GAT-2, GAT-3, and GAT-4) responsible for the transmembrane transport of GABA in mice. To explore the effect of GAT-2 on type 1 helper T (Th1) cells, naïve CD4⁺ T cells were isolated from splenocytes of GAT-2 knockout (KO) and wild-type (WT) mice and cultured for Th1 cell differentiation, and then, metabolomics analysis of Th1 cells was performed via gas chromatography coupled to time-of-flight mass spectrometry added with multivariate analyses. Based on the variable importance projection value > 1 and P < 0.05, a total of nine differentially expressed metabolites (DEMs) were identified between WT and KO. Then, DEMs were mapped to the KEGG database, and five metabolic pathways were significantly enriched, including the cysteine and methionine metabolism, the riboflavin metabolism, the purine metabolism, the glycerolipid metabolism, and the glycerophospholipid metabolism. Collectively, our metabolomics analysis revealed that deficiency of GAT-2 influenced the metabolomics profile of Th1 cells, which will provide insights into T cell response to GAT-2 deficiency in mice. Data are available via MetaboLights with identifier MTBLS3358.

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.

SERS diagnosis of liver fibrosis in the early stage based on gold nanostar liver targeting tags

In order to realize the accurate and early diagnosis of liver fibrosis, a long slow pathological process which may lead to cirrhosis or even liver cancer, liver targeting tags made up of gold nanostars and glycyrrhetinic acid are reported in this paper. Gold nanostars (GNSs) and GNS liver targeting tags (GLTTs) were injected into model mice with stage S1 liver fibrosis and normal mice via the tail vein respectively, then the SERS spectra were collected. GLTTs had a better detection effect on liver tissue than unmodified GNSs (12.85 times), and better detection reproducibility as well. Moreover, according to the MTT and survival analysis experiments, GLTTs also had better biocompatibility. Hence, the changes of 10 SERS signals and other substances in the early stage of liver fibrosis were analyzed at the molecular level, and the SERS characteristic peaks that could be used for the diagnosis of early liver fibrosis were screened out. Revealed by the experimental results, the GLTTs designed and prepared were applicable to the efficient SERS detection of early liver fibrosis in mice, and the strategy we have proposed might be a potential approach for the early diagnosis of this disease in clinics.

Tissue-based metabolomics reveals metabolic biomarkers and potential therapeutic targets for esophageal squamous cell carcinoma

Prognosis for esophageal squamous cell carcinoma (ESCC) is poor, so it is essential to develop a more complete understanding of the disease. The purpose of this study was to explore metabolic biomarkers and potential therapeutic targets for ESCC. An ultra-high-performance liquid chromatography coupled with high resolution mass (UPLC/MS)-based metabolomic analysis was performed in 141 ESCC cancerous tissue samples and 70 non-cancerous counterparts. The results showed that 41 differential metabolites were annotated in the training set, and 37 were validated in the test set. Single-metabolite-based receiver operating characteristic (ROC) curves as well as metabolite-based machine learning models, including Partial Least Squares (PLS), Support Vector Machine (SVM), and Random Forest (RF), were investigated for cancerous and non-cancerous tissue classification. Six most prevalent diagnostic metabolites-adenylsuccinic acid, UDP-GalNAc, maleylacetoacetic acid, hydroxyphenylacetylglycine, galactose, and kynurenine-showed testing predictive accuracies of 0.89, 0.95, 0.97, 0.89, 0.84, and 0.84, respectively. Moreover, the metabolite-based models (PLS, SVM, and RF) had testing predictive accuracies of 0.95, 0.95, and 1.00, respectively. Kaplan-Meier survival analysis and Cox proportional hazards regression analysis demonstrated that 2-hydroxymyristoylcarnitine (HR: 0.55, 95 % CI: 0.32 to 0.92), 3-hydroxyhexadecanoylcarnitine (HR: 0.49, 95 % CI: 0.29 to 0.83), and 2,3-Dinor-TXB1 (HR: 0.56, 95 % CI: 0.33 to 0.95) to be significantly associated with OS. Based on the observation of accumulation in amino acids, immunohistochemistry (IHC) staining revealed that the amino acid transporters SLC7A5/LAT1, SLC1A5/ASCT2, and SLC16A10/MCT10 were up-regulated in ESCC cancerous tissues when compared to non-cancerous equivalents. Consistently, the same panel of amino acids were downregulated in cells with SLC1A5 knockdown. Herein, it is concluded that this study not only identified several metabolites with diagnostic and/or prognostic value, but also provided accurate metabolite-based prediction models for ESCC tissue classification. Furthermore, the three up-regulated amino acid transporters were identified as potential therapeutic targets for ESCC, especially SLC1A5.

Metabolomics of primary cutaneous melanoma and matched adjacent extratumoral microenvironment

BACKGROUND

Melanoma causes the vast majority of deaths attributable to skin cancer, largely due to its propensity for metastasis. To date, few studies have examined molecular changes between primary cutaneous melanoma and adjacent putatively normal skin. To broaden temporal inferences related to initiation of disease, we performed a metabolomics investigation of primary melanoma and matched extratumoral microenvironment (EM) tissues; and, to make inferences about progressive disease, we also compared unmatched metastatic melanoma tissues to EM tissues.

METHODS

Ultra-high performance liquid chromatography-mass spectrometry-based metabolic profiling was performed on frozen human tissues.

RESULTS

We observed 824 metabolites as differentially abundant among 33 matched tissue samples, and 1,118 metabolites as differentially abundant between metastatic melanoma (n = 46) and EM (n = 34) after false discovery rate (FDR) adjustment (p<0.01). No significant differences in metabolite abundances were noted comparing primary and metastatic melanoma tissues.

CONCLUSIONS

Overall, pathway-based results significantly distinguished melanoma tissues from EM in the metabolism of: ascorbate and aldarate, propanoate, tryptophan, histidine, and pyrimidine. Within pathways, the majority of individual metabolite abundances observed in comparisons of primary melanoma vs. EM and metastatic melanoma vs. EM were directionally consistent. This observed concordance suggests most identified compounds are implicated in the initiation or maintenance of melanoma.

Serum Metabolomics for Biomarker Screening of Esophageal Squamous Cell Carcinoma and Esophageal Squamous Dysplasia Using Gas Chromatography-Mass Spectrometry

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is one of the most common malignancies with poor diagnosis. Esophageal squamous dysplasia (ESD) is considered as an immediate precancerous lesion of ESCC. Lack of biomarkers for discriminating ESCC and ESD from healthy subjects limits the early diagnosis and treatment of ESCC. Therefore, a serum metabolomic strategy was conducted to identify and validate potential metabolic markers for the screening of ESCC and ESD subjects.

METHODS

A total of 74 patients with ESCC, 72 patients with ESD, and 75 normal control (NC) subjects were enrolled in this study. Gas chromatography-mass spectrometry was used to acquire serum metabolic profiles. Pathway analysis was conducted to uncover the fluctuated metabolic pathways during ESCC. Multivariate analyses were used to screen and validate the biomarkers.

RESULTS

ESCC, ESD, and NC subjects revealed progressively altered metabolic profiles, in which amino acids globally increased, while fatty acids decreased in ESCCs compared with the control groups. Pathway analysis demonstrated the activated biosynthesis of amino acids and inhibited desaturation of saturated fatty acids. The panel constructed with propanoic acid, linoleic acid, glycerol-3-phosphate, and l-glutamine showed the area under the curve (AUC), sensitivity, and specificity of 0.817, 0.75, and 0.74, respectively, in the discrimination of ESCC/ESD patients from NC subjects. The panel constructed by propanoic acid, l-leucine, and hydroxyproline revealed the AUC, sensitivity, and specificity of 0.819, 0.76, and 0.72, respectively, in the discrimination of ESD from NC subjects. The combination of hypoxanthine, 2-ketoisocaproic acid, l-glutamate, and l-aspartate showed the AUC, sensitivity, and specificity of 0.818, 0.83, and 0.74, respectively, in the discrimination of ESCC patients from ESD subjects.

CONCLUSIONS

Our study revealed the systematic landscape for metabolic alterations in sera of ESD and ESCC patients. The defined metabolite markers showed reasonable performance in the discrimination of ESCC and ESD patients, and may provide helpful reference for clinicians and biologists.

Undetectable Free Aromatic Amino Acids in Nails of Breast Carcinoma: Biomarker Discovery by a Novel Metabolite Purification VTGE System

Background: Metabolic reprogramming in breast cancer is depicted as a crucial change in the tumor microenvironment. Besides the molecular understanding of metabolic heterogeneity, appreciable attention is drawn to characterizing metabolite profiles in tumor tissue and derived biological fluids and tissue materials. Several findings reported on the metabolic alterations of free aromatic amino acids (FAAAs) and other metabolites in biological fluids. Furthermore, there is a significant gap in the development of a suitable method for the purification and analysis of metabolite biomarkers in nails of cancer patients. Methods: To address the metabolite alterations, specifically FAAA levels in nails, fingernail clippings of breast cancer patients (N = 10) and healthy subjects (N-12) were used for extraction and purification of metabolites. Here, we reported a novel and specifically designed vertical tube gel electrophoresis (VTGE) system that helped in the purification of metabolites in the range of 100-1,000 Da from nail materials. Here, the VTGE system uses 15% polyacrylamide under non-denaturing and non-reducing conditions, which makes eluted metabolites directly compatible with LC-HRMS and other analytical techniques. Qualitative and quantitative determination of FAAAs in nail lysates was done in positive ESI mode of the Agilent LC-HRMS platform. Results: The analysis on collected data of nail metabolites clearly suggested that FAAAs including tryptophan, tyrosine, phenylalanine, and histidine were undetectable in nail lysates of breast cancer over healthy subjects. This is a first report that showed highly reduced levels of FAAAs in nails of breast cancer patients. Furthermore, the present observation is in consonance with previous findings that showed cancer cachexia and high amino acid catabolism in breast cancer patients that drive metabolite-led cancer growth and proliferation. Conclusion: This paper provides a proof of concept for a novel and specifically developed VTGE process that showed first evidence on the undetectable level of FAAAs in nails of breast cancer patients as metabolite biomarkers. Here, the authors propose the potential use of a VTGE-assisted process to achieve metabolomic discovery in nails of breast cancer and other tumor types.

Identification of Lesional Tissues and Nonlesional Tissues in Early Gastric Cancer Endoscopic Submucosal Dissection Specimens Using a Fiber Optic Raman System

Aim

To identify lesional and nonlesional tissues from early gastric cancer (EGC) patients by Raman spectroscopy to build a diagnostic model and effectively diagnose EGC.

Method

Specimens were collected by endoscopic submucosal dissection from 13 patients with EGC, and 55 sets of standard Raman spectral data (each integrated 10 times) were obtained using the fiber optic Raman system; there were 33 sets of lesional tissue data, including 18 sets of high-grade intraepithelial neoplasia (HGIN) data and 15 sets of adenocarcinoma data, and 22 sets of nonlesional tissue data. After the preprocessing steps, the average Raman spectrum was obtained.

Results

The nonlesional tissues showed peaks at 891 cm⁻¹, 1103 cm⁻¹, 1417 cm⁻¹, 1206 cm⁻¹, 1234 cm⁻¹, 1479 cm⁻¹, 1560 cm⁻¹, and 1678 cm⁻¹. Compared with the peaks corresponding to nonlesional tissues, the peaks of the lesional tissues shifted by different magnitudes, and a new characteristic peak at 1324 cm⁻¹ was observed. Comparing the peak intensity ratio and the integral energy ratio of the lesional tissues with those of the nonlesional tissues revealed a significant difference between the two groups (independent-samplest-test, P < 0.05). Considering the peak intensity ratio of I1560 cm⁻¹/I1103 cm⁻¹ as a diagnostic indicator, the accuracy, sensitivity, and specificity of diagnosing EGC were 98.8%, 93.9%, and 91.9%, respectively. Considering the integral energy ratio (noncontinuous frequency band and continuous frequency band) as a diagnostic indicator, the accuracy, sensitivity, and specificity of diagnosing EGC were 99.2-99.6%, 93.9-97.0%, and 95.5%, respectively.

Conclusions

The integral energy ratio of the Raman spectrum could be considered an effective indicator for the diagnosis of EGC.

Intestinal gases: influence on gut disorders and the role of dietary manipulations

The inner workings of the intestines, in which the body and microbiome intersect to influence gut function and systemic health, remain elusive. Carbon dioxide, hydrogen, methane and hydrogen sulfide, as well as a variety of trace gases, are generated by the chemical interactions and microbiota within the gut. Profiling of these intestinal gases and their responses to dietary changes can reveal the products and functions of the gut microbiota and their influence on human health. Indeed, different tools for measuring these intestinal gases have been developed, including newly developed gas-sensing capsule technology. Gases can, according to their type, concentration and volume, induce or relieve abdominal symptoms, and might also have physiological, pathogenic and therapeutic effects. Thus, profiling and modulating intestinal gases could be powerful tools for disease prevention and/or therapy. As the interactions between the microbiota, chemical constituents and fermentative substrates of the gut are principally influenced by dietary intake, altering the diet, which, in turn, changes gas profiles, is the main therapeutic approach for gastrointestinal disorders. An improved understanding of the complex interactions within the intestines that generate gases will enhance our ability to prevent, diagnose, treat and monitor many gastrointestinal disorders.

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.

Exploring potential biomarkers of early stage esophageal squamous cell carcinoma in pre- and post-operative serum metabolomic fingerprint spectrum using 1H-NMR method

Esophageal squamous cell carcinoma (ESCC) is one of the most prevalent types of upper gastrointestinal malignancy. Here, we used ¹H nuclear magnetic resonance spectroscopy (¹H-NMR) to identify potential pre- and post-operative serum biomarkers in patients with early stage ESCC using metabolomic fingerprint spectrum. Serum samples from preoperative patients with ESCC (ESCC, n = 25), postoperative patients with ESCC (PO, n = 24), and controls (n = 40) were analysed using ¹H-NMR spectroscopy. Using orthogonal partial least squares-discriminant analysis, 31 altered serum metabolites were successfully identified among the three groups. These metabolites are indicative of the changes that occur with glycometabolism, the metabolism of fatty acids, amino acids, choline, ketone bodies, nucleotides, and lipids. Based on receiver operating characteristic (ROC) curve analysis and a biomarker panel with an area under the curve (AUC) of 0.969, six serum metabolites (α-glucose, choline, glutamine, glutamate, valine, and dihydrothymine) were selected as potential diagnostic biomarkers for early stage ESCC. Additionally, four potential PO biomarkers (α-glucose, pyruvate, glutamate, and valine) with an AUC of 0.985 were selected to distinguish ESCC and PO. Many metabolites trended towards normalisation in PO patients, with only choline remaining high with an AUC of 0.858, suggesting that it may be a valuable potential biomarker for neoplasm progression, recurrence, chemoradiotherapy, and prognosis. ¹H-NMR spectroscopy may be a useful tumour detection approach in the early diagnosis of ESCC. These results also indicate that it is useful to differentiate pre- and post-operative ESCC, evaluate surgery therapeutic responses, and monitor postoperative chemoradiotherapy.

Metabolome analysis of esophageal cancer tissues using capillary electrophoresis-time-of-flight mass spectrometry

Reports of the metabolomic characteristics of esophageal cancer are limited. In the present study, we thus conducted metabolome analysis of paired tumor tissues (Ts) and non-tumor esophageal tissues (NTs) using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS). The Ts and surrounding NTs were surgically excised pair-wise from 35 patients with esophageal cancer. Following tissue homogenization and metabolite extraction, a total of 110 compounds were absolutely quantified by CE-TOFMS. We compared the concentrations of the metabolites between Ts and NTs, between pT1 or pT2 (pT1-2) and pT3 or pT4 (pT3-4) stage, and between node-negative (pN-) and node-positive (pN+) samples. Principal component analysis and hierarchical clustering analysis revealed clear metabolomic differences between Ts and NTs. Lactate and citrate levels in Ts were significantly higher (P=0.001) and lower (P<0.001), respectively, than those in NTs, which corroborated with the Warburg effect in Ts. The concentrations of most amino acids apart from glutamine were higher in Ts than in NTs, presumably due to hyperactive glutaminolysis in Ts. The concentrations of malic acid (P=0.015) and citric acid (P=0.008) were significantly lower in pT3-4 than in pT1-2, suggesting the downregulation of tricarboxylic acid (TCA) cycle activity in pT3-4. On the whole, in this study, we demonstrate significantly different metabolomic characteristics between tumor and non-tumor tissues and identified a novel set of metabolites that were strongly associated with the degree of tumor progression. A further understanding of cancer metabolomics may enable the selection of more appropriate treatment strategies, thereby contributing to individualized medicine.

Integrative Pathway Analysis of Genes and Metabolites Reveals Metabolism Abnormal Subpathway Regions and Modules in Esophageal Squamous Cell Carcinoma

Aberrant metabolism is one of the main driving forces in the initiation and development of ESCC. Both genes and metabolites play important roles in metabolic pathways. Integrative pathway analysis of both genes and metabolites will thus help to interpret the underlying biological phenomena. Here, we performed integrative pathway analysis of gene and metabolite profiles by analyzing six gene expression profiles and seven metabolite profiles of ESCC. Multiple known and novel subpathways associated with ESCC, such as 'beta-Alanine metabolism', were identified via the cooperative use of differential genes, differential metabolites, and their positional importance information in pathways. Furthermore, a global ESCC-Related Metabolic (ERM) network was constructed and 31 modules were identified on the basis of clustering analysis in the ERM network. We found that the three modules located just to the center regions of the ERM network-especially the core region of Module_1-primarily consisted of aldehyde dehydrogenase (ALDH) superfamily members, which contributes to the development of ESCC. For Module_4, pyruvate and the genes and metabolites in its adjacent region were clustered together, and formed a core region within the module. Several prognostic genes, including GPT, ALDH1B1, ABAT, WBSCR22 and MDH1, appeared in the three center modules of the network, suggesting that they can become potentially prognostic markers in ESCC.

Applications of Metabolomics in Cancer Studies

Since the start of metabolomics as a field of research, the number of studies related to cancer has grown to such an extent that cancer metabolomics now represents its own discipline. In this chapter, the applications of metabolomics in cancer studies are explored. Different approaches and analytical platforms can be employed for the analysis of samples depending on the goal of the study and the aspects of the cancer metabolome being investigated. Analyses have concerned a range of cancers including lung, colorectal, bladder, breast, gastric, oesophageal and thyroid, amongst others. Developments in these strategies and methodologies that have been applied are discussed, in addition to exemplifying the use of cancer metabolomics in the discovery of biomarkers and in the assessment of therapy (both pharmaceutical and nutraceutical). Finally, the application of cancer metabolomics in personalised medicine is presented.

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.

Chromatographic determination of some biomarkers of liver cirrhosis and hepatocellular carcinoma in Egyptian patients

Metabolomics has been shown to be an effective tool for disease diagnosis, biomarker screening and characterization of biological pathways. A total of 140 subjects were included in this study; urine metabolomes of patients with liver cirrhosis (LC, n = 40), patients with hepatocellular carcinoma (HCC; n = 55) and healthy male subjects (n = 45) as a control group were studied. Gas chromatography/mass spectrometry-based urine metabolomics profiles were investigated for all participants. Diagnostic models were constructed with a combination of marker metabolites, using principal components analysis and receiver operator characteristic curves. A total of 57 peaks could be auto-identified of which 13 marker metabolites (glycine, serine, threonine, proline, urea, phosphate, pyrimidine, arabinose, xylitol, hippuric acid, citric acid, xylonic acid and glycerol) were responsible for the separation of HCC group from healthy subjects. Also, eight markers metabolites (glycine, serine, threonine, proline, citric acid, urea, xylitol and arabinose) showed significant differences between the LC group and healthy subjects. No significant difference was detected between HCC and LC groups regarding all these metabolites. Metabolomic profile using GC-MS established an optimized diagnostic model to discriminate between HCC patients and healthy subjects; also it could be useful for diagnosis of LC patients. However, it failed to differentiate between HCC and LC patients.

Metabolic profiling of ob/ob mouse fatty liver using HR-MAS 1H-NMR combined with gene expression analysis reveals alterations in betaine metabolism and the transsulfuration pathway

Metabolic perturbations resulting from excessive hepatic fat accumulation are poorly understood. Thus, in this study, leptin-deficient ob/ob mice, a mouse model of fatty liver disease, were used to investigate metabolic alterations in more detail. Metabolites were quantified in intact liver tissues of ob/ob (n = 8) and control (n = 8) mice using high-resolution magic angle spinning (HR-MAS) ¹H-NMR. In addition, after demonstrating that HR-MAS ¹H-NMR does not affect RNA integrity, transcriptional changes were measured by quantitative real-time PCR on RNA extracted from the same specimens after HR-MAS ¹H-NMR measurements. Importantly, the gene expression changes obtained agreed with those observed by Affymetrix microarray analysis performed on RNA isolated directly from fresh-frozen tissue. In total, 40 metabolites could be assigned in the spectra and subsequently quantified. Quantification of lactate was also possible after applying a lactate-editing pulse sequence that suppresses the lipid signal, which superimposes the lactate methyl resonance at 1.3 ppm. Significant differences were detected for creatinine, glutamate, glycine, glycolate, trimethylamine-N-oxide, dimethylglycine, ADP, AMP, betaine, phenylalanine, and uridine. Furthermore, alterations in one-carbon metabolism, supported by both metabolic and transcriptional changes, were observed. These included reduced demethylation of betaine to dimethylglycine and the reduced expression of genes coding for transsulfuration pathway enzymes, which appears to preserve methionine levels, but may limit glutathione synthesis. Overall, the combined approach is advantageous as it identifies changes not only at the single gene or metabolite level but also deregulated pathways, thus providing critical insight into changes accompanying fatty liver disease. Graphical abstract A Evaluation of RNA integrity before and after HR-MAS ¹H-NMR of intact mouse liver tissue. B Metabolite concentrations and gene expression levels assessed in ob/ob (steatotic) and ob/+ (control) mice using HR-MAS ¹H-NMR and qRT-PCR, respectively.

Gas Chromatography/Mass Spectrometry-Based Metabolomic Profiling Reveals Alterations in Mouse Plasma and Liver in Response to Fava Beans

Favism is a life-threatening hemolytic anemia resulting from the intake of fava beans by susceptible individuals with low erythrocytic glucose 6-phosphate dehydrogenase (G6PD) activity. However, little is known about the metabolomic changes in plasma and liver after the intake of fava beans in G6PD normal and deficient states. In this study, gas chromatography/mass spectrometry was used to analyze the plasma and liver metabolic alterations underlying the effects of fava beans in C3H- and G6PD-deficient (G6PDx) mice, and to find potential biomarkers and metabolic changes associated with favism. Our results showed that fava beans induced oxidative stress in both C3H and G6PDx mice. Significantly, metabolomic differences were observed in plasma and liver between the control and fava bean treated groups of both C3H and G6PDx mice. The levels of 7 and 21 metabolites in plasma showed significant differences between C3H-control (C3H-C)- and C3H fava beans-treated (C3H-FB) mice, and G6PDx-control (G6PDx-C)- and G6PDx fava beans-treated (G6PDx-FB) mice, respectively. Similarly, the levels of 7 and 25 metabolites in the liver showed significant differences between C3H and C3H-FB, and G6PDx and G6PDx-FB, respectively. The levels of oleic acid, linoleic acid, and creatinine were significantly increased in the plasma of both C3H-FB and G6PDx-FB mice. In the liver, more metabolic alterations were observed in G6PDx-FB mice than in C3H-FB mice, and were involved in a sugar, fatty acids, amino acids, cholesterol biosynthesis, the urea cycle, and the nucleotide metabolic pathway. These findings suggest that oleic acid, linoleic acid, and creatinine may be potential biomarkers of the response to fava beans in C3H and G6PDx mice and therefore that oleic acid and linoleic acid may be involved in oxidative stress induced by fava beans. This study demonstrates that G6PD activity in mice can affect their metabolic pathways in response to fava beans.

Oesophageal squamous cell carcinoma (ESCC): Advances through omics technologies, towards ESCC salivaomics

Oesophageal Squamous Cell Carcinoma (ESCC) is one of the two main subtypes of oesophageal cancer, affecting mainly populations in Asia. Though there have been great efforts to develop methods for a better prognosis, there is still a limitation in the staging of this affection. As a result, ESCC is detected at advances stages, when the interventions on the patient do not have such a positive outcome, leading in many cases to recurrence and to a very low 5-year survival rate, causing high mortality. A way to decrease the number of deaths is the use of biomarkers that can trace the advance of the disease at early stages, when surgical or chemotherapeutic methodologies would have a greater effect on the evolution of the subject. The new high throughput omics technologies offer an unprecedented chance to screen for thousands of molecules at the same time, from which a new set of biomarkers could be developed. One of the most convenient types of samples is saliva, an accessible body fluid that has the advantage of being non-invasive for the patient, being easy to store or to process. This review will focus on the current status of the new omics technologies regarding salivaomics in ESCC, or when not evaluated yet, the achievements in related diseases.

Determination of urinary 5-hydroxyindoleacetic acid as a metabolomics in gastric cancer

PURPOSE

The aim of this paper is to study urinary5-hydroxyindoleacetic acid (5-HIAA) in gastric cancer patients with a biochemical method and compare this metabolite with normal control and individuals with chronic gastritis.

MATERIALS AND METHODS

The subjects were 48 histologically proven gastric adenocarcinoma patients. They were 10 women and 38 men with mean age of 63.73 years. For determination of urinary excretion of 5-HIAA, a biochemical method was applied. According to kit protocol, the patients' fresh urine was added to the reagent material, and the color of the sediment that was the result of interaction between 5-HIAA and the mercury salt was compared with the standard colorimetric plate of the kit. The same method was also performed for a group of 47 patients with chronic gastritis and also a group of 50 normal individuals (age and sex matched).

RESULTS

Urinary 5-HIAA was significantly higher in gastric cancer patients compared to individuals with chronic gastritis and normal controls (P value <0.001), but no association was detected in urinary 5-HIAA based on age, sex, or site of tumor and tumor grade in gastric cancer patients group. Also, no significant difference was noted in 5-HIAA excretion between chronic gastritis and normal control groups.

CONCLUSION

Urinary excretion of 5-HIAA is significantly higher in the gastric cancer patients in comparison with that of chronic gastritis patients or normal individuals. So, this test could be regarded as a tumor marker in conjunction with other modalities in diagnosis of gastric cancer.

GC-MS-based metabolomic analysis of human papillary thyroid carcinoma tissue

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Elucidating the molecular network that is altered in PTC may lead to the identification of the critical insight into the pathogenesis of PTC. Thus far, little is known regarding the global metabolomic alterations of PTC. Gas chromatography coupled with mass spectrometry-based metabolomics was used to analyze metabolomic alterations in matched PTC and normal thyroid tissues obtained from the patients. Multivariate statistical analyses were employed to determine the significant metabolomic differences. The mRNA levels of the associated metabolic enzyme genes were further assayed with reverse transcription-quantitative polymerase chain reaction analysis. Principal component analysis, partial least-squares discriminant analysis (PLS-DA) and orthogonal PLS-DA models were established, which could clearly separate human normal thyroid and PTC samples, and identified that metabolites in carbohydrate metabolism, including glucose, fructose, galactose, mannose, 2-keto-D-gluconic acid and rhamnose, consistently decreased, while metabolites in nucleotide metabolism, including malonic acid and inosine, and lipid metabolism, including cholesterol and arachidonic acid, significantly altered in PTC. Furthermore, the mRNA levels of metabolic enzyme genes, including glucose-6-phosphate dehydrogenase, phosphoglycerate kinase 1, lactate dehydrogenase A, phosphoglycerate dehydrogenase and prostaglandin-endoperoxide synthase 2, significantly increased in PTC. Based on the metabolomic and mRNA data, various metabolites may be used for increased synthesis of nucleotides and oncogenic lipids in PTC, which may contribute to the pathogenesis of PTC. The present study provides a new understanding of the dysregulated metabolism in PTC and identifies potential avenues for the therapeutic intervention for this disease.

Application of 1H NMR spectroscopy-based metabonomics to feces of cervical cancer patients with radiation-induced acute intestinal symptoms

OBJECTIVE

Radiation-induced acute intestinal symptoms (RIAISs) are a common complication of radiotherapy for cervical cancer. The aim of this study was to use (1)H nuclear magnetic resonance ((1)H NMR) combined with chemometric analysis to develop a metabolic profile of patients with RIAISs.

METHODS

Fecal samples were collected from 66 patients with cervical cancer before and after pelvic radiotherapy. After radiotherapy, RIAISs occurred in eleven patients. We selected another 11 patients from participants without RIAISs whose age, stage, histological type and treatment methods are matched with RIAIS patients as the control group. (1)H NMR spectroscopy combined with multivariate pattern recognition analysis was used to generate metabolic profile data, as well as to establish a RIAIS-specific metabolic phenotype.

RESULTS

Orthogonal partial least-squares discriminant analysis was used to distinguish samples between the pre- and post-radiotherapy RIAIS patients and between RIAIS patients and controls. Fecal samples from RIAIS patients after pelvic radiotherapy were characterized by increased concentrations of α-ketobutyrate, valine, uracil, tyrosine, trimethylamine N-oxide, phenylalanine, lysine, isoleucine, glutamine, creatinine, creatine, bile acids, aminohippurate, and alanine, accompanied by reduced concentrations of α-glucose, n-butyrate, methylamine, and ethanol relative to samples from RIAIS patients before pelvic radiotherapy, while in RIAIS patients relative to controls, trimethylamine, n-butyrate, fumarate and acetate were down-regulated and valine, TMAO, taurine, phenylalanine, lactate, isoleucine and creatinine were up-regulated.

CONCLUSIONS

We obtained the metabolic profile of RIAIS patients from fecal samples using NMR-based metabonomics. This profile has the potential to be developed into a novel clinical tool for RIAIS diagnosis or therapeutic monitoring, and could contribute to an improved understanding of the disease mechanism. However, because of the limitations of methods, technique, bacterial contamination of feces and small sample size, further research and verification are needed.

LC-MS-based serum metabolomic analysis reveals dysregulation of phosphatidylcholines in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive cancers with poor prognosis. Here, we carried out liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS)-based untargeted metabolomic analysis of ESCC serum samples. Statistical analysis resulted in the identification of 652 significantly dysregulated molecular features in serum from ESCC patients as compared to the healthy subjects. Phosphatidylcholines were identified as a major class of dysregulated metabolites in this study suggesting potential perturbation of phosphocholine metabolism in ESCC. By using a targeted MS/MS approach both in positive and negative mode, we were able to characterize and confirm the structure of seven metabolites. Our study describes a quantitative LC-MS approach for characterizing dysregulated lipid metabolism in ESCC.

BIOLOGICAL SIGNIFICANCE

Altered metabolism is a hallmark of cancer. We carried out (LC-MS)-based untargeted metabolomic profiling of serum from esophageal squamous cell carcinoma (ESCC) patients to characterize dysregulated metabolites. Phosphatidylcholine metabolism was found to be significantly altered in ESCC. Our study illustrates the use of mass spectrometry-based metabolomic analysis to characterize molecular alterations associated with ESCC. This article is part of a Special Issue entitled: Proteomics in India.

Characterization and noninvasive diagnosis of bladder cancer with serum surface enhanced Raman spectroscopy and genetic algorithms

This study aims to characterize and classify serum surface-enhanced Raman spectroscopy (SERS) spectra between bladder cancer patients and normal volunteers by genetic algorithms (GAs) combined with linear discriminate analysis (LDA). Two group serum SERS spectra excited with nanoparticles are collected from healthy volunteers (n = 36) and bladder cancer patients (n = 55). Six diagnostic Raman bands in the regions of 481-486, 682-687, 1018-1034, 1313-1323, 1450-1459 and 1582-1587 cm(-1) related to proteins, nucleic acids and lipids are picked out with the GAs and LDA. By the diagnostic models built with the identified six Raman bands, the improved diagnostic sensitivity of 90.9% and specificity of 100% were acquired for classifying bladder cancer patients from normal serum SERS spectra. The results are superior to the sensitivity of 74.6% and specificity of 97.2% obtained with principal component analysis by the same serum SERS spectra dataset. Receiver operating characteristic (ROC) curves further confirmed the efficiency of diagnostic algorithm based on GA-LDA technique. This exploratory work demonstrates that the serum SERS associated with GA-LDA technique has enormous potential to characterize and non-invasively detect bladder cancer through peripheral blood.

Analytical protocols based on LC-MS, GC-MS and CE-MS for nontargeted metabolomics of biological tissues

Invasive, site-specific metabolite information could be better obtained from tissues. Hence, highly sensitive mass spectrometry-based metabolomics coupled with separation techniques are increasingly in demand in clinical research for tissue metabolomics application. Applying these techniques to nontargeted tissue metabolomics provides identification of distinct metabolites. These findings could help us to understand alterations at the molecular level, which can also be applied in clinical practice as screening markers for early disease diagnosis. However, tissues as solid and heterogeneous samples pose an additional analytical challenge that should be considered in obtaining broad, reproducible and representative analytical profiles. This manuscript summarizes the state of the art in tissue (human and animal) treatment (quenching, homogenization and extraction) for nontargeted metabolomics with mass spectrometry.

Review article: breath analysis in inflammatory bowel diseases

BACKGROUND

There is an urgent need for cheap, reproducible, easy to perform and specific biomarkers for diagnosis, differentiation and stratification of inflammatory bowel disease (IBD) patients. Technical advances allow for the determination of volatile organic compounds in the human breath to differentiate between health and disease.

AIM

Review and discuss medical literature on volatile organic compounds in exhaled human breath in GI disorders, focusing on diagnosis and differentiation of IBD.

METHODS

A systematic search in PubMed, Ovid Medline and Scopus was completed using appropriate keywords. In addition, a bibliography search of each article was performed.

RESULTS

Mean breath pentane, ethane, propane, 1-octene, 3-methylhexane, 1-decene and NO levels were elevated (P < 0.05 to P < 10(-7)) and mean breath 1-nonene, (E)-2-nonene, hydrogen sulphide and methane were decreased in IBD compared to healthy controls (P = 0.003 to P < 0.001). A combined panel of 3 volatile organic compounds (octene, (E)-2-nonene and decene) showed the best discrimination between paediatric IBD and controls (AUC 0.96). Breath condensate cytokines were higher in IBD compared to healthy individuals (P < 0.008). Breath pentane, ethane, propane, isoprene and NO levels correlated with disease activity in IBD patients. Breath condensate interleukin-1β showed an inverse relation with clinical disease activity.

CONCLUSIONS

Breath analysis in IBD is a promising approach that is not yet ready for routine clinical use, but data from other gastrointestinal diseases suggest the feasibility for use of this technology in clinical practice. Well-designed future trials, incorporating the latest breath detection techniques, need to determine the exact breath metabolome pattern linked to diagnosis and phenotype of IBD.

Current state of the art of mass spectrometry-based metabolomics studies – a review focusing on wide coverage, high throughput and easy identification

Metabolomics aims at the comprehensive assessment of a wide range of endogenous metabolites and attempts to identify and quantify the attractive metabolites in a given biological sample.

Identification of bile biomarkers of biliary tract cancer through a liquid chromatography/mass spectrometry-based metabolomic method

The incidence and mortality rate of biliary tract cancer have been increasing worldwide; however, its diagnosis and prognosis have not improved in recent years. A novel approach, termed 'metabolomics', may have the potential to be developed as an effective diagnostic tool. The present study prospectively obtained bile samples from 115 individuals, including 32 patients with biliary tract cancer, 61 patients with benign biliary tract diseases and 22 normal controls. A liquid chromatography/mass spectrometry (LC/MS)‑based approach was used to investigate the differences in bile samples between the three groups, followed by multivariate statistical analysis, which included partial least squares projection to latent structures with discriminant analysis (PLS‑DA) and orthogonal projection to latent structures with discriminant analysis (OPLS‑DA). The metabolomic 2D score plot and 3D plot revealed clear separation between the cancer, benign and normal control groups by PLS‑DA. To further address the significant difficulties in clinically differentiating between biliary tract cancer and benign biliary tract diseases, OPLS‑DA was performed to distinguish between the two disease groups and to select potential biomarkers. The cancer and benign groups were well differentiated. The metabolic analysis revealed significantly lower levels of lysophosphatidylcholine, phenylalanine, 2‑octenoylcarnitine, tryptophan and significantly higher levels of taurine‑ and glycine‑conjugated bile acids in the bile from patients with biliary tract cancer compared with those in the bile from patients with benign biliary tract disease. The present study suggested that an LC/MS‑based metabolomic investigation provides a potent and promising approach for discriminating biliary tract cancer from benign biliary tract diseases and the identified specific metabolites may offer potential as novel biomarkers for the early detection of biliary tract cancer.

Age-related alterations in the metabolic profile in the hippocampus of the senescence-accelerated mouse prone 8: a spontaneous Alzheimer's disease mouse model

Alzheimer's disease (AD), the most common age-dependent neurodegenerative disorder, produces a progressive decline in cognitive function. The metabolic mechanism of AD has emerged in recent years. In this study, we used multivariate analyses of gas chromatography-mass spectrometry measurements to determine that learning and retention-related metabolic profiles are altered during aging in the hippocampus of the senescence-accelerated mouse prone 8 (SAMP8). Alterations in 17 metabolites were detected in mature and aged mice compared to young mice (13 decreased and 4 increased metabolites), including metabolites related to dysfunctional lipid metabolism (significantly increased cholesterol, oleic acid, and phosphoglyceride levels), decreased amino acid (alanine, serine, glycine, aspartic acid, glutamate, and gamma-aminobutyric acid), and energy-related metabolite levels (malic acid, butanedioic acid, fumaric acid, and citric acid), and other altered metabolites (increased N-acetyl-aspartic acid and decreased pyroglutamic acid, urea, and lactic acid) in the hippocampus. All of these alterations indicated that the metabolic mechanisms of age-related cognitive impairment in SAMP8 mice were related to multiple pathways and networks. Lipid metabolism, especially cholesterol metabolism, appears to play a distinct role in the hippocampus in AD.

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.

Serum metabolomic signatures of lymph node metastasis of esophageal squamous cell carcinoma

Lymph node metastasis was recently proven to be the single most important prognostic factor for esophageal cancer, an important malignant tumor with poor prognosis. A global metabolomics approach was applied to study lymph node metastasis of esophageal squamous cell carcinoma (ESCC). Metabolomics analyses were performed using gas chromatography/mass spectrometry together with univariate and multivariate statistical analyses. There were clear metabolic distinctions between ESCC patients and healthy subjects. ESCC patients could be well-classified according to lymph node metastasis. We further identified a series of differential serum metabolites for ESCC and lymph node metastatic ESCC patients, suggesting metabolic dysfunction in proliferation (aerobic glycolysis, glutaminolysis, fatty acid metabolism, and branched-chain amino acid consumption), apoptosis, migration, immune escape, and oxidative stress of cancer cells in metastatic ESCC patients. In total, three serum metabolites (valine, γ-aminobutyric acid, and pyrrole-2-carboxylic acid) were selected by binary logistic regression analysis, and their combined use resulted in high diagnostic capacity for ESCC metastasis by receiver operating characteristic analysis. The present metabolomics study staged ESCC patients by lymph node metastasis, and the results suggest promising applications of this approach in prognostic prediction, tailored therapeutics, and understanding the pathological mechanisms of poor prognosis of ESCC patients.

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.

Extraction for metabolomics: access to the metabolome

INTRODUCTION

The value of information obtained from a metabolomic study depends on how much of the metabolome is present in analysed samples. Thus, only a comprehensive and reproducible extraction method will provide reliable data because the metabolites that will be measured are those that were extracted and all conclusions will be built around this information.

OBJECTIVE

To discuss the efficiency and reliability of available sample pre-treatment methods and their application in different fields of metabolomics.

METHODS

The review has three sections: the first deals with pre-extraction techniques, the second discusses the choice of extraction solvents and their main features and the third includes a brief description of the most used extraction techniques: microwave-assisted extraction, solid-phase extraction, supercritical fluid extraction, Soxhlet and a new method developed in our laboratory--the comprehensive extraction method.

RESULTS

Examination of over 200 studies showed that sample collection, homogenisation, grinding and storage could affect the yield and reproducibility of results. They also revealed that apart from the solvent used for extraction, the extraction techniques have a decisive role on the metabolites available for analysis.

CONCLUSION

It is essential to evaluate efficacy and reproducibility of sample pre-treatment as a first step to ensure the reliability of a metabolomic study. Among the reviewed methods, the comprehensive extraction method appears to provide a promising approach for extracting diverse types of metabolites.

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.

Repetitive transcranial magnetic stimulation applications normalized prefrontal dysfunctions and cognitive-related metabolic profiling in aged mice

Chronic high-frequency repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain stimulation technique that has recently received increasing interests as a therapeutic procedure for neurodegenerative diseases. To identify the metabolism mechanism underlying the improving effects of rTMS, we observed that high frequency (25Hz) rTMS for 14 days could reverse the decline of the performance of the passive avoidance task in aged mice. We further investigated the metabolite profiles in the prefrontal cortex (PFC) in those mice and found that rTMS could also reverse the metabolic abnormalities of gamma-aminobutyric acid, N-acetyl aspartic, and cholesterol levels to the degree similar to the young mice. These data suggested that the rTMS could ameliorate the age-related cognitive impairment and improving the metabolic profiles in PFC, and potentially can be used to improve cognitive decline in the elderly.

Review of mass spectrometry-based metabolomics in cancer research

Metabolomics, the systematic investigation of all metabolites present within a biologic system, is used in biomarker development for many human diseases, including cancer. In this review, we investigate the current role of mass spectrometry-based metabolomics in cancer research. A literature review was carried out within the databases PubMed, Embase, and Web of Knowledge. We included 106 studies reporting on 21 different types of cancer in 7 different sample types. Metabolomics in cancer research is most often used for case-control comparisons. Secondary applications include translational areas, such as patient prognosis, therapy control and tumor classification, or grading. Metabolomics is at a developmental stage with respect to epidemiology, with the majority of studies including less than 100 patients. Standardization is required especially concerning sample preparation and data analysis. In the second part of this review, we reconstructed a metabolic network of patients with cancer by quantitatively extracting all reports of altered metabolites: Alterations in energy metabolism, membrane, and fatty acid synthesis emerged, with tryptophan levels changed most frequently in various cancers. Metabolomics has the potential to evolve into a standard tool for future applications in epidemiology and translational cancer research, but further, large-scale studies including prospective validation are needed.

Metabolomics in cancer biomarker discovery: current trends and future perspectives

Cancer is one of the most devastating human diseases that causes a vast number of mortalities worldwide each year. Cancer research is one of the largest fields in the life sciences and despite many astounding breakthroughs and contributions over the past few decades, there is still a considerable amount to unveil on the function of cancer. It is well known that cancer metabolism differs from that of normal tissue and an important hypothesis published in the 1950s by Otto Warburg proposed that cancer cells rely on anaerobic metabolism as the source for energy, even under physiological oxygen levels. Following this, cancer central carbon metabolism has been researched extensively and beyond respiration, cancer has been found to involve a wide range of metabolic processes, and many more are still to be unveiled. Studying cancer through metabolomics could reveal new biomarkers for cancer that could be useful for its future prognosis, diagnosis and therapy. Metabolomics is becoming an increasingly popular tool in the life sciences since it is a relatively fast and accurate technique that can be applied with either a particular focus or in a global manner to reveal new knowledge about biological systems. There have been many examples of its application to reveal potential biomarkers in different cancers that have employed a range of different analytical platforms. In this review, approaches in metabolomics that have been employed in cancer biomarker discovery are discussed and some of the most noteworthy research in the field is highlighted.

Global gas chromatography/time-of-flight mass spectrometry (GC/TOFMS)-based metabonomic profiling of lyophilized human feces

Gas chromatography mass spectrometry (GC/MS)-based fecal metabonomics represents a powerful systems biology approach for elucidating metabolic biomarkers of lower gastrointestinal tract (GIT) diseases. Unlike metabolic profiling of fecal water, the profiling of complete fecal material remains under-explored. Here, a gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) method was developed and validated for the global metabonomic profiling of human feces. Fecal and fecal water metabotypes were also profiled and compared. Additionally, the unclear influence of blood in stool on the fecal metabotype was investigated unprecedentedly. Eighty milligram of lyophilized feces was ultrasonicated with 1mL of methanol:water (8:2) for 30min, followed by centrifugation, drying of supernatant, oximation and trimethylsilylation for 45min. Lyophilized feces demonstrated a more comprehensive metabolic coverage than fecal water, based on the number of chromatographic peaks. Principal component analysis (PCA) indicated occult blood (1mgHb/g feces) exerted a negligible effect on the fecal metabotype. Conversely, a unique metabotype related to feces spiked with gross blood (100mgHb/g feces) was revealed (PCA, R(2)X=0.837, Q(2)=0.794), confirming the potential confounding effect of gross GIT bleeding on the fecal metabotype. This pertinent finding highlights the importance of prudent interpretation of fecal metabonomic data, particularly in GIT diseases where bleeding is prevalent.

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.

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.

Global and targeted metabolomics of esophageal squamous cell carcinoma discovers potential diagnostic and therapeutic biomarkers

Diagnostic and therapeutic biomarkers useful for esophageal squamous cell carcinoma (ESCC) have the ability to increase the long term survival of cancer patients. A metabolomics study, using plasma from four groups including ESCC patients before, during, and after chemoradiotherapy (CRT) and healthy controls, was originally carried out by LC-MS to determine global alterations in the metabolic profiles and find biomarkers potentially applicable to diagnosis and monitoring treatment effects. It is worth pointing out that a clear clustering and separation of metabolic data from the four groups was observed, which indicated that disease status and treatment intervention resulted in specific metabolic perturbations in the patients. A series of metabolites were found to be significantly altered in ESCC patients versus healthy controls and in pre- versus post-treatment patients based on multivariate statistical data analysis (MVDA). To further validate the reliability of these potential biomarkers, an independent validation was performed by using the selected reaction monitoring (SRM) based targeted approach. Finally, 18 most significantly altered plasma metabolites in ESCC patients, relative to healthy controls, were tentatively identified as lysophosphatidylcholines (lysoPCs), fatty acids, l-carnitine, acylcarnitines, organic acids, and a sterol metabolite. The classification performance of these metabolites were analyzed by receiver operating characteristic (ROC)(1) analysis and a biomarker panel was generated. Together, biological significance of these metabolites was discussed. Comparison between pre- and post-treatment patients generated 11 metabolites as potential therapeutic biomarkers that were tentatively identified as amino acids, acylcarnitines, and lysoPCs. Levels of three of these (octanoylcarnitine, lysoPC(16:1), and decanoylcarnitine) were closely correlated with treatment effect. Moreover, variation of these three potential biomarkers was investigated over the treatment course. The results suggest that these biomarkers may be useful in diagnosis, as well as in monitoring therapeutic responses and predicting outcomes of the ESCC.

TD-GC-MS Investigation of the VOCs Released from Blood Plasma of Dogs with Cancer

An analytical TD-GC-MS method was developed and used for the assessment of volatile organic compounds (VOCs) released from the blood plasma of dogs with/without cancer. VOCs released from 40 samples of diseased blood and 10 control samples were compared in order to examine the difference between both sample groups that were showing qualitatively similar results independent from the disease's presence. However, mild disturbances in the spectra of dogs with cancer in comparison with the control group were observed, and six peaks (tentatively identified by comparison with mass spectral library as hexanal, octanal, toluene, 2-butanone, 1-octen-3-ol and pyrrole) revealed statistically significant differences between both sample groups, thereby suggesting that these compounds are potential biomarkers that can be used for cancer diagnosis based on the blood plasma TD-GC-MS analysis. Statistical comparison with the application of principal component analysis (PCA) provided accurate discrimination between the cancer and control groups, thus demonstrating stronger biochemical perturbations in blood plasma when cancer is present.