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Metabolomic profiling of oesophago-gastric cancer: A systematic review

European Journal of Cancer, Volume 49, Issue 17, November 2013, Pages 3625 - 3637

Editorial comment from Professor Lordick:
Knowledge and awareness increases about the various metabolic changes that precede and go along with cancerogenesis and tumor progression. This review summarizes findings from biological studies focusing on tumor and host metabolic changes in oesophago-gastric cancer. Glycolysis, the tricarboxylic acid cycle, anaerobic respiration and protein as well as lipid metabolism were found to be significantly different between cancer and control samples. Products of lipid metabolism were also identified as biomarkers of oesophago-gastric cancer. But attention needs to be drawn to standardized sampling conditions for biological materials as well as to the development of assays that can be used in clinical research and later in practice.

Abstract

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.

Keywords: Gastric cancer, Oesophageal cancer, Metabolomics, Metabolic profiling, Mass spectrometry, Nuclear magnetic resonance imaging, Glycolysis, TCA cycle, Amino acids, Lipids.

1. Introduction

Oesophago-gastric (OG) cancer affects approximately 1.5 million people a year worldwide and accounts for 15% of cancer related deaths. 1 In Western countries, the incidence is increasing, in particular adenocarcinomas of the distal oesophagus and gastro-oesophageal junction.2, 3, and 4 Prognosis remains poor with 5-year survival rates of 34% for localised disease and 17% for all stages combined. 5

Treatment options and overall survival depend on both the stage of the disease and a patient’s general health. The early symptoms of OG cancer are usually insidious and the majority of patients present late to their doctor with incurable disease. Surgical resection of the tumour remains the mainstay of curative treatment. In the United Kingdom (UK), only 38% percent of newly diagnosed patients are suitable for treatment with curative intent. 6 Identification of these cancers at an earlier stage could potentially improve the survival outcomes of OG cancer. There are currently no screening tests in mainstream clinical practice, despite evidence that treatment of early OG cancer has more favourable outcomes.7, 8, and 9 Oesophago-gastroduodenoscopy (OGD) and biopsy remain the gold standard procedure for the diagnosis of OG cancer. However, this is an invasive procedure and is too costly to be employed in a screening capacity. Radiological tests such as barium oesophograms and serum biomarkers are available; however their use has been limited due to poor sensitivity and specificity.

Metabolomics is defined as a quantitative description of all endogenous low-molecular-weight components (<1 kDa) in a biological sample, such as tissue, urine or plasma. 10 It is an evolving field and it has the potential to be an effective tool for the early diagnosis of OG cancer, through identification of one or more diagnostic biomarkers. The composition of these endogenous compounds is affected by the upstream influence of the proteome and genome as well as environmental factors, lifestyle factors, medication and underlying disease. The exact number of different metabolites in humans is currently unknown. Recent estimates by the human metabolite database (HDMB) suggest up to 5000 compounds, with reference to bio-fluid or tissue concentration data existing in the current literature. 11

The majority of analytical platforms for metabolomic profiling are based on spectroscopic techniques. Mass spectrometry (often combined with chromatographic separation) and nuclear magnetic resonance (NMR) spectroscopy are the two most commonly employed analytical platforms; both techniques allow extensive and rapid analysis of small molecule metabolites, 12 resulting in multi-parameter datasets containing quantitative information on a range of metabolites. Due to the complexity and volume of the resultant spectral data, computer-based pre-processing and multivariate modelling techniques have been developed to facilitate the analysis and interpretation of the data.

Metabolomics studies have primarily focussed on the identification of cancer-specific metabolites. Examples include analysis of serum for the diagnosis of colorectal cancer, 13 exhaled breath for lung cancer 14 and urine for prostate cancer. 15 The aim of these studies has been to identify a panel of metabolites that may provide a metabolic fingerprint of malignancy. In combination, their sensitivity and specificity of predicting cancer has greater statistical power than single metabolites.

This systematic review aims to identify metabolomic biomarkers of OG cancer, found in human biological samples, and to assess the quality of the published data. The dominant metabolic pathways of malignant transformation, associated with the observed changes, will also be discussed.

2. Methods

2.1. Search strategy

A literature search (title and abstract) of Ovid Medline(R) (1948–2012), Embase (1974–2012), Web of Science and PubMed electronic databases was conducted up to and including 9th November 2012 for studies of metabolomic profiling of oesophago-gastric cancer. The search was conducted using the MeSH terms: mass spectrometry, nuclear magnetic resonance spectroscopy, metabolomic, metabonomic, metabolic profiling in multiple combinations (AND) with gastric cancer and oesophageal/esophageal cancer.

Two reviewers (N.A. and S.K.) independently screened titles and abstracts of studies identified through the electronic search. Full texts of potentially relevant articles were retrieved. Further potentially relevant articles were identified through the searching of reference lists of relevant studies. Experimental studies investigating the metabolomic profile of human biological samples from patients with gastric or oesophageal cancers, compared to an appropriate control group were included in our analysis. Inclusion criteria for analytical platforms were mass spectrometry (MS) or NMR. Exclusion criteria were: studies analysing the proteome rather than the metabolome, in vitro cell line studies, animal studies and studies without a control group. Studies that reported the same patient population were also excluded, except for the most recent or complete publication.

Two reviewers (N.A. and S.K.) independently extracted data from selected studies including primary author; year of publication; number and types of specimen; analytical platform and significantly different metabolites in the cancer and control groups. Compounds presented with varying chemical nomenclature in different articles are described by their common name in this review. The primary outcome measure was the identification of metabolites found to have different abundances between cancer and control samples at a statistically significant level.

2.2. Study quality assessment

The QUADAS-2 tool 16 was used to assess the quality of the included studies. It consists of four key domains covering patient selection, index test, reference standard and flow of patients through the study. The reference standard is defined as the best available method to establish the presence or absence of the target condition. 17 In this review, the reference standard is considered to be histological examination. Each domain is assessed in terms of the risk of bias and the first three are also assessed in terms of concerns regarding applicability. To help reach a judgement on the risk of bias, signalling questions are included, full details of which are provided in Supplementary file 1 .

3. Results

Twenty studies met the inclusion criteria and were eligible for systematic review. A kappa score of 0.85 confirmed excellent agreement between assessors. Search results are presented in Fig. 1 .

gr1

Fig. 1 Systematic search and selection strategy.

3.1. Study characteristics

There were 12 studies of gastric cancer, six of oesophageal cancer and the remaining two had a mixed cohort ( Table 1 ). Biological samples utilised for Metabolomic analysis included tissue (n = 11), serum (n = 8), urine (n = 1) and gastric content (n = 1). The analytical platforms used for metabolite detection included Gas Chromatography–Mass Spectrometry (GC–MS) (n = 10), High Resolution-Magic Angle Spinning-NMR (n = 3), Capillary Electrophoresis–Mass Spectrometry (n = 1), NMR (n = 3), Liquid Chromatography–Mass Spectrometry (n = 2) and Selected Ion Flow Tube–Mass Spectrometry (n = 1).

Table 1 Summary of included studies.

Author and year of publication Analytical platform Cancer site Sample type Cancer type NCa Control Nc Metabolites of cancer compared to control Other findings Refs.
Up-regulated Down-regulated
Mun CW (2004) 1H NMR Gastric Tissue GC 13 NGT 22 Choline Lipids   18
Lactate
Tugnioli V (2006) HR-MAS-NMR Gastric Tissue GC-AC

HP +ve
5 NGT

HP −ve
11 Triacylglycerides     19
Choline and
Gly
Ligor T (2007) SPME–GCMS Gastric Tissue GC 3 NGT 13 Carbon disulphide   No difference in VOCs of exhaled breath of patients with cancer versus control 20
Breath 3 10 Nil Nil
Buszewski B (2008) SPME–GCMS Gastric Tissue GC 5 NGT 5 Propanol   Butane & carbon disulphide present in the headspace of HP culture 21
Carbon disulphide
Hirayama A (2009) CE–MS Gastric Tissue GC 12 NGT   Lactate, Glucose, fructose, Fumurate Citrate   22
Malate, Glyceraldehyde-3P and
All common AA apart from Gln & Asp
Cai Z (2010) GC–MS Gastric Tissue GC 65 NGT 65 Pyruvic acid, Lactic acid Fumaric acid Confirmation of findings with western blot proteomic analysis 23
Fructose, Glyceraldehyde and Isocitric acid
Wu H (2010) GC–MS Gastric Tissue GC 18 NGT 18 Val, Ile, Ser, Gln Phosphoserine Higher levels of l-Cysteine, hypoxanthine, l-Tryosine and lower levels of phenanthrenol and butanoic acid in late versus early stage cancer 24
Heptanedioic acid, Propanoic acid, l-Altrose
Butenoic acid, Galactofuranoside, l-Mannofuranose and
Phenanthrenol, butanetriol, Acetamide d-Ribofuranose
Oxazolethione and Naphtalene  
Song H (2011) GC–MS Gastric Tissue GC 30 NGT 30 Fumaric acid, Valeric acid, 3-Hydroxybutanoic acid Failed to differentiate pathological stage using multivariate analysis 25
α-Ketoglutaric acid, 9-Hexadecanoic acid
Benzenepropanoic acid, Hexadecanoic acid
1-Phenanthrene, Squalene, Cis-vaccenic acid
Carboxylic acid, Xylonic acid and Arachidonic acid and
Octadecanoic acid 9-Octadecenamide
Calabrese C (2012) HR-MAS-MRS Gastric Tissue GC-AC 5 NGT 12 Choline   Increased presence of lipid bodies found in the cytoplasm of cancer cell with TEM/SEM imaging 26
AAG 5 Gly, Ala and
HP 5 Triacylglycerides
Yu L (2011) GC–MS Gastric Serum GC-AC 9 GSG 19 Urate, Ornithine, Creatinine Surgical removal of GC tissue restored the levels of some metabolites back to those found in CSG 27
Pyroglutamate, Azelaic acid Threonate
11-Eicosenoic acid  
Glu, Asn and y-tocopherol
2-Hydroxybutyrate
Ikeda A GC–MS Gastric Serum GC-AC 11 HV 12 3-Hydroxypropionic acid and Octanoic acid, Phosphoric acid

Pyruvic acid
  28
  3-Hydroxyisobutyric acid,
Oesophageal EAC 15 Lactic acid, Glycolic acid, Malonic acid, Fumaric acid, Ser, Gln and Asp Pyruvic acid
Song H (2012) GC–MS Gastric Serum GC 30 HV 30 Val Gln, Hexanedioic acid, Failed to differentiate pathological stage using multivariate analysis 29
Sarcosine and 9,12 Octadecadienoic acid,
Hexadecanenitrile 9-Octadecenoic acid,
  Trans-13-octadecenoic acid,
Nonahexacontanoic acid,
Cholesterol, fumaric acid, mesyl-arabinose Benzeneacetonitrile,
2-Amino-4-hydroxy-pteridinone and
1,2,4,-Benzenetricarboxylic acid
Aa J (2012) GC–MS Gastric Serum CSG 20 CSG 17 Citrate, Succinate, Fumurate, Malate Glu, β-hydroxybutyrate Glucose Surgical removal of GC tissue restored the levels of some metabolites back to normal 30
Hexadecenoic acid, Docosahexaenoic acid and hepatanoic acid
Wu H (2009) GC–MS Oesophageal Tissue EAC 2 20 NOT 20 Val, Ile, Tyr, Asn, andAla l-Altrose   31
ESC 18 Arabinofuranoside, Tetradecanoic acid, d-Galactofuranoside
Hexadecanoic acid, Naphthalene, Arabinose and
1-Butanamine, Aminoquinolone, Bisethane
Myo-inositol, Phosphoric acid and  
Pyrimidine nucleoside
Yakoub D (20100 HR-MAS-NMR Oesophageal Tissue EAC 35 NOT 35 Phospho-choline, Myo-inositol, Glu,   Stepwise up-regulation of metabolites from cancer to adjacent tissue to healthy distant tissue 32
NOTH 52 Inosine, Adenosine and uridine containing compounds
Djukovic D (2010) HPLC–MS Oesophageal Serum EAC       1-Methyladenosine, Uridine   33
N2,N2-dimethylguanosine,
N2-methylguanosine and Cytidine
Zhang J (2011) NMR Oesophageal Serum EAC 68 HV 34 Citrate, lactate, a-glucose   Up-regulation of metabolites shown in BE-HGD-EAC sequence 34
BE 5 Gln, Lys, B-hydroxybutyrate and
HGD 11 Creatinine
Zhang J (2012) LC–MS Oesophageal Serum EAC 67 HV 34 Lactic acid Val, Leu, Ile, Met, Tyr and Trp   35
BE 3 Carnitine and Myristic acid, Linolenic acid and
Margaric acid Linoleic acid
HGD 9    
Kumar S (2012) SIFT–MS Gastric and Oesophageal GastricContent Cancer 19 HV 11 Acetaldehyde, Acetone and Acetic acid Formaldehyde   36
ID 9 Hexanoic acid, Hydrogen sulphide,
Hydrogen cyanide, Methanol and
Methyl phenol
Hasim A (2012) 1H NMR Oesophageal Urine SCC 108 HV 40 Mannitol, Glucose, Pyruvate, N-acetylcysteine, Val

Dihydrothymine,

Hippurate,

Methylguanidine,

Citric acid and

1-methylnicotin-amide,
Metabolites in plasma positively correlated to lymph node metastatic rate 37
Glu, Tyr

c-Propalanine,

Phenylalanine,

Acetate and Allantoin
Serum Dimethylamine Leu, Ala, Ile, Val, Glycoprotein,

Lactate, Acetone, Acetate, Choline

Isobutyrate, Unsaturated lipids

VLDL and LDL
a-Glucose
b-Glucose and
Citric acid

Results not statistically significant.

1H NMR, Proton Nuclear Magnetic Resonance; HR-MAS-NMR, High Resolution-Magic Angle Spinning-Nuclear Magnetic Resonance Spectroscopy; SPME, Solid Phase Micro-extraction; GC–MS, Gas Chromatography–Mass Spectrometry; CE–MS, Capillary Electrophoresis–Mass Spectrometry; HPLC–MS, High Performance Liquid Chromatography–Mass Spectrometry; LC–MS, Liquid Chromatography–Mass Spectrometry; NCa, number of cancer patients; Nc, number of control patients; GC, gastric cancer; EAC, oesophageal adenocarcinoma; ESC, oesophageal squamous cell carcinoma; NGT, normal gastric tissue matched to the same patient; AAG, autoimmune atrophic gastritis; CSG, chronic superficial gastritis; NOT, normal oesophageal tissue from the same patients; NOTH, normal oesophageal tissue from healthy patients; HV, healthy volunteers; BE, Barrett’s oesophagus; HGD, high grade dysplasia; ID, inflammatory disorders; HP, Helicobacter pylori; VOCs, volatile organic compounds; Val, valine; His, histidine; Phe, phenylalanine; Thr, threonine; Leu, leucine; Met, methionine; Trp, tryptophan; Ile, isoleucine; Lys, lysine; Tyr, tyrosine; Gln, glutamine; Glu, glutamate; Ser, serine; Asn, asparagine; Gly, glycine; Ala, alanine; Arg, arginine; Asn, asparagine; Asp, aspartic acid; Cys, cysteine; Pro, proline; TEM, tunnel electron microscopy; SEM, surface electron microscopy.

3.2. Quality assessment of studies

The outcomes of the QUADAS-2 study quality assessment are shown in Fig. 2 . Further details regarding individual studies are presented in Supplementary file 1 . Unclear risk of bias for patient selection was reported for the majority of studies due to the lack of clarity regarding patient recruitment. It is unclear whether consecutive patient data is included or whether highly selected samples were chosen. As a consequence, 35% of studies have an unclear/high risk of applicability with respect to the target population defined by the review.

gr2

Fig. 2 Outcome of QUADAS-2 study quality assessment.

The risk of bias for the index test and reference standard was unclear for the majority of studies due to lack of clarity regarding blinding of assessors. Thirty-five percent of studies had an unclear/high risk of applicability with regard to the reference standard, mostly due to lack of information regarding histological confirmation of control samples.

3.3. Metabolites of cellular respiration

The metabolite intermediates of glycolysis, the tricarboxylic acid (TCA) cycle and anaerobic respiration that were found to be significantly different between cancer and control samples are shown in Table 2 . The majority of metabolites that have been identified show contradictory results in different studies, in terms of relative abundance in the cancer versus control groups. Lactate and fumurate were the most commonly recognised biomarkers.

Table 2 Relative abundance of metabolite intermediates of glycolysis, the tricarboxylic acid (TCA) cycle and anaerobic respiration, in biological samples of patients with oesophageal/gastric cancer in comparison to normal controls. All metabolite comparisons are determined at a statically significant level (P < 0.05).

Author Type of sample Analytical platform Glycolosis Anaerobic respiration The TCA cycle
      Glucose Fructose Glyceraldehyde Pyruvic acid Lactic acid/Lactate Citrate Isocitric acid α-Ketoglutaric acid/α-ketoglutarate Succinate Fumaric acid/Fumurate Malate
Gastric
Hirayama A (2009) Tissue CE–MS        
Cai Z (2010) Tissue GC–MS          
Song H (2011) Tissue GC–MS                  
Ikeda A (Part B) (2011) Serum GC–MS                    
Song H (2012) Serum GC–MS                    
Aa J (2012) Serum GC–MS            
 
Oesophageal
Zhang J (2011) Serum NMR                
Ikeda A (2011) Serum GC–MS                
Zhang J (2012) Serum LC–MS                    
Hasim A (2012) Urine 1H NMR                
Serum                

CE–MS, Capillary Electrophoresis–Mass Spectrometry; GC–MS, Gas Chromatography–Mass Spectrometry; NMR, Nuclear Magnetic Resonance Spectroscopy; LC–MS, Liquid Chromatography–Mass Spectrometry; 1H NMR, 1H Nuclear Magnetic Resonance.

3.4. Amino acid metabolites

Essential and non-essential amino acids that were found to be significantly different between cancer and control samples are shown in Table 3 . Valine, glutamine and glutamate were the most commonly identified amino acid biomarkers in descending order of frequency. The metabolite glutamine is the most consistent biomarker showing up-regulation in the serum, urine and tumour tissues of patients with OG cancer. There is a general trend of up-regulation of amino acids across the majority of studies, however in a study by Zhang et al., 35 they observed contradictory findings in serum samples from patients with oesophageal adenocarcinoma analysed by LC–MS.

Table 3 Relative abundance of essential and non-essential amino acids in biological samples of patients with oesophageal/gastric cancer, in comparison to normal controls. All comparisons are determined at a statically significant level (P < 0.05).

Author Sample type Analytical platform Essential amino acid Non-essential amino acid
      Val His Phe Thr Leu Met Trp Ile Lys Tyr Gln Glu Ser Asn Gly Ala Arg Asn Asp Cys Pro
Gastric
Tugnioli V (2006) Serum HR-MAS-NMR                                        
Hiarayama A (2009) Tissue CE–MS    
Wu H (2010) Tissue GC–MS                                  
Calabrese C (2012) Tissue HR-MAS-MRS                                      
Yu L (2011) Serum GC–MS                                      
Song H (2012) Serum GC–MS                                      
Aa J (2012) Serum GC–MS                                        
 
Oesophageal
Wu H (2009) Tissue GC–MS                              
Yakoub D (2010) Tissue HR-MAS-NMR                                        
Zhang J (2011) Serum NMR                                      
Ikeda A (2011) Serum GC–MS                                  
Zhang J (2012) Serum LC–MS                              
Hasim A (2012) Urine 1H NMR                                    
Serum                                  

CE–MS, Capillary Electrophoresis–Mass Spectrometry; GC–MS, Gas Chromatography–Mass Spectrometry; NMR, Nuclear Magnetic Resonance Spectroscopy; LC–MS, Liquid Chromatography–Mass Spectrometry; 1H NMR, 1H Nuclear Magnetic Resonance; HR-MAS-NMR, High Resolution-Magic Angle Spinning-Nuclear Magnetic Resonance Spectroscopy; Val, valine; His, histidine; Phe, phenylalanine; Thr, threonine; Leu, leucine; Met, methionine; Trp, tryptophan; Ile, isoleucine; Lys, lysine; Tyr, tyrosine; Gln, glutamine; Glu, glutamate; Ser, serine; Asn, asparagine; Gly, glycine; Ala, alanine; Arg, arginine; Asn, asparagine; Asp, aspartic acid; Cys, cysteine; Pro, proline.

Several other metabolites related to amino acid metabolism have been identified in relation to OG cancer. Sulphur containing compounds, thought to be generated from the incomplete metabolism of methionine in the transamination pathway or by bacterial metabolism has been found to be up-regulated. 20 Two studies (from the same research group) investigating the volatile organic compounds (VOCs) in the headspace of tissue found a significant increase in the presence of carbon disulphide in cancer versus normal tissue. Buszewski et al., also demonstrated the presence of carbon disulphide in the headspace of Helicobacter pylori cultures. 21

3.5. Lipid metabolites

The saturated and un-saturated free fatty acids (FFA) that were found to be significantly different between cancer and control samples are shown in Table 4 . Metabolites of FFA oxidation, such as aldehydes and ketones have also been identified as potential biomarkers. Of the three endogenous ketones, 38 acetone and β-hydroxybutyrate have been described as potential biomarkers of oesophago-gastric cancer. β-Hydroxybutyrate has been shown to be up-regulated in the serum of patients with gastric cancer using GC–MS27 and 30 and in the serum of patients with oesophageal cancer using NMR. 35 The median concentration of acetone was found to be higher in the headspace of gastric content from patients with OG cancer versus controls. 36 However, it was found in lower quantities in the serum of oesophageal cancer patients when compared to healthy controls. 37 The study by Kumar et al. also identified aldehydes as potential biomarkers for cancer in the headspace of gastric content. This study demonstrated an increase in acetaldehyde in cancer patients compared with controls. 36

Table 4 Relative abundance of saturated and un-saturated free fatty acids in biological samples of patients with oesophageal/gastric cancer, in comparison to normal controls. All comparisons are determined at a statically significant level (P < 0.05).

Author Sample type Analytical platform Un-saturated fatty acid Saturated fatty acid
      Palmitoleic acid Cervonic acid Linoleic acid Oleic acid Linolenic acid Vaccenic acid Gondoic acid Arachidonic acid Myristic acid Enanthic acid Margaric acid Caprylic acid
Gastric
Mun CW (2004) Tissue 1H NMR Decrease in lipids
Tugnioli V (2006) Tissue HR-MAS-NMR Increase in tryiacyglycerides
Song H (2011) Tissue GC–MS                
Calabrese C (2012) Tissue HR-MAS-MRS Increase in tryiacyglycerides
Ikeda A (2011) Serum GC–MS                      
Yu L (2011) Serum GC–MS                      
Song H (2012) Serum GC–MS                    
Aa J (2012) Serum GC–MS                  
 
Oesophageal
Wu H (2009) Tissue GC–MS                    
Zhang J (2012) Serum LC–MS                
Hasim A (2012) Urine 1H NMR                        
Serum Decrease in unsaturated lipids, VLDL and LDL

CE–MS, Capillary Electrophoresis–Mass Spectrometry; GC–MS, Gas Chromatography–Mass Spectrometry; LC–MS, Liquid Chromatography–Mass Spectrometry; 1H NMR, 1H Nuclear Magnetic Resonance; HR-MAS-NMR, High Resolution-Magic Angle Spinning-Nuclear Magnetic Resonance Spectroscopy; VLDL, very-low-density lipoprotein; LDL, low-density lipoprotein.

Lipid metabolites of the cell membrane including: myo-inositol up-regulated in three studies using HR-MAS-NMR19 and 32 and GC–MS 31 ; choline and phospho-choline up-regulated in three studies of OG cancer tissue using HR-MAS-NMR18, 26, and 32 and down-regulated in the serum of oesophageal cancer using 1H NMR, 37 and squalene up-regulated in the gastric cancer tissue using GC–MS, 25 have also been identified as potential biomarkers.

3.6. Nucleotide metabolites

Studies of oesophageal cancer tissue have shown up-regulation of pyrimidine nucleotides using GC–MS, 31 and adenine and uridine containing compounds using HR-MAS-NMR. 32 A study of the serum of patients with oesophageal cancer demonstrates up-regulation of guanosine and cytidine containing compounds in comparison to healthy controls. 37

4. Discussion

This systematic review demonstrates variation in the relative abundance of the metabolites of glycolysis, lactic acid fermentation, de novo lipid and amino acid synthesis in biological samples of patients with OG cancer versus controls. The results of several studies are contradictory; this variation may be due to sample selection and preparation, type of biological medium investigated or analytical technique. The metabolite glutamine is the most consistent biomarker, showing up-regulation in the serum, urine and tumour tissues of patients with OG cancer.22, 27, 28, 30, 32, and 37

Weinberg and Hanahan described six essential alterations in cell physiology which collectively dictate malignant transformation. 39 These characteristics were identified as the hallmarks of cancer and since 2000 reprogramming of energy metabolism has been added to the list.40 and 41 The increased rate of cell proliferation in cancer requires metabolic pathways to be redesigned to satisfy large demands for adenosine triphosphate (ATP), nicotinamide adenine dinucleotide phosphate (NADPH), nicotinamide adenine dinucleotide (NADH) and carbon skeletons. 42 The emerging field of metabolomics has aided in the identification of metabolite intermediates involved in these pathways, which have been identified as potential biomarkers in biological samples from cancer patients ( Fig. 3 ).

gr3

Fig. 3 Metabolic pathways associated with oesophago-gastric (OG) cancer.

The metabolic intermediates of glycolysis including glucose, fructose, glyceraldehyde and pyruvic acid are shown to be up-regulated in several studies of gastric cancer tissue.22 and 23 This may be due to a phenomenon known as the Warburg effect,43 and 44 which states that even in the presence of oxygen, cancer cells reprogram their glucose metabolism to produce energy largely by glycolysis rather than oxidative phosphorylation via the tricarboxylic acid (TCA) cycle. 45 The poor efficiency of generating ATP by glycolysis is rationalised by the advantages of diverting glycolytic intermediates into various biosynthetic pathways, which are essential for the synthesis of necessary macromolecules (i.e. amino acids, nucleosides and lipids required for assembling new cells).45 and 46 The increased production of pyruvate by the Warburg effect and subsequent lactic acid fermentation propagated by the enzyme lactate dehydrogenase, may also explain the increased levels of lactate/lactic acid found in the tissues of gastric cancer,22 and 23 as well as in the serum of patients with oesophageal cancer.28, 34, and 35

The metabolite intermediates of the TCA cycle have been identified as biomarkers in the tissues of gastric cancer,22, 23, and 25 and in the serum of gastric and oesophageal cancer.28, 29, 30, 34, 35, and 37 Fumurate was the most frequently identified biomarker, but showed inconsistencies in relative abundance, with two studies observing down-regulation in cancer. The increased abundance of Krebs cycle metabolites, despite cancer cell preference of glycolysis to oxidative phosphorylation, is dependent on the process of anaplerosis. It refers to replenishment of metabolites in the cycle through the generation of α-ketoglutarate from the amino acid glutamate after its conversion from glutamine. 47 The aneplerotic flux generates macromolecules for cellular proliferation and is more specific than a high glycolytic flux, which can be induced by other factors such as hypoxia.48 and 49 This review confirms that in the majority of studies, there is an increased presence of glutamine and glutamate, suggesting an important role of this pathway in OG cancer metabolism.

In addition to higher levels of glutamine and glutamate, our review demonstrates the increase of other amino acid metabolites in the serum and tissues of oesophago-gastric cancer. The availability of amino acids in the tumour environment is important for cell proliferation and as an energy substrate. The source of these amino acids has not been determined; however it is likely to be a combination of systemic protein catabolism, 50 tumour environment degradation of extracellular matrix and de novo biosynthesis. 22 Contradictory findings of low levels of amino acids in the serum of oesophageal cancer patients in one study were attributed to the over-utilisation of amino acids in the tumour tissue. 35

Lipids and triglycerides have been shown to be present in increased levels in gastric cancer tissue by two studies using HR-MAR-NMR.19 and 26 One study confirmed these findings by demonstrating increased abundance of cytosolic lipid droplets with scanning electron microscopy. 26 Studies using GC–MS and LC–MS have demonstrated a mixed outcome of up-regulation and down-regulation, of saturated and unsaturated FFA in both tissue and serum. Low levels of FFA or lipids have been attributed to increased consumption by tumours due to their anabolic metabolism. However, much of the scientific evidence demonstrates the increased presence of fatty acids in the tumour environment as a consequence of metabolic reprogramming in the cancer disease state.51 and 52 There may be increased lipid availability from the bloodstream as a consequence of systemic lipolysis secondary to cancer cachexia or de novo fatty acid synthesis leading to FFA accumulation. 53 The role of lipid synthesis in cancer is not fully understood; however it is likely that de novo lipogenesis contributes to the formation of structural lipids for cell membrane synthesis, generation of energy by β-oxidation and cell signalling molecules in the cancer cell.51, 54, and 55

Fatty acid β-oxidation has been confirmed as a dominant pathway for energy generation in numerous malignancies including prostate and pancreatic cancer.56 and 57 Ketones and aldehydes, which are metabolic products of β-oxidation, are shown to be increased in biological samples of patients with OG cancer.27, 30, 34, and 36 Up-regulation of acylcarnitines, which are metabolic intermediates of β-oxidation involved in the transfer of FFA across the mitochondrial membrane, 58 has also been confirmed in metabolomic studies of kidney, 59 liver 60 and colorectal cancer. 61 Converse findings have been confirmed in a recent metabolic profiling study of the plasma of patients with oesophageal squamous cell cancer using LC–MS. 62

Local inflammation and oxidative stress associated with the cancer disease state have been shown to influence the production of eicosanoids from the lipid cell membrane and FFAs. 63 The increased abundance of eicosanoids have been confirmed in cell line, 64 animal 65 and human studies66 and 67 of OG cancer. Oxidative stress also promotes lipid peroxidation leading to the production of aldehydes, 68 which has been found in the headspace of gastric content for patients with OG cancer. 36 Lipid metabolic profiles have great potential in detecting OG cancer; however a limitation of current research is that only small molecular endogenous FFAs and intermediate metabolites have been investigated. Therefore, the roles of macromolecular lipids warrant further investigation.

The changes in the metabolic pathways that have been described may explain some of the observed differences in metabolites found in cancer and control samples. However, several of the hypotheses of cancer cell metabolism do not always hold true in human samples and contradictory findings are evident. The differences in study findings may be due to the type of biological sample analysed, its preparation and metabolite derivatisation, but consideration must also be given to the analytical platform employed. Variable detection limits and sensitivities of instruments may also influence the detection of metabolites that are present in a sample.

The majority of studies were unclear as to the nature of their patient selection and therefore we do not know if positive results are due to highly selected test subjects. Therefore, there can only be limited application of these results to real patient groups. A further weakness of this systematic review is the potential for publication bias and the lack of data representing no difference between compared groups. Due to lack of reported statistical data, we were not able to account for this phenomenon in the full cohort of included studies.

The application of metabolomics in cancer biomarker detection and development has largely not advanced beyond Phase 1 pre-clinical exploratory studies. Future studies have an obligation to provide transparent details of patient selection with attention being paid to variability in patient demographics, histology and treatment modalities (e.g. chemotherapy). Control subjects should be matched to cancer case subjects for factors that may potentially influence the identified biomarkers including age, sex, race, medication and lifestyle factors. Discovery of a biomarker or group of biomarkers with adequate sensitivity and specificity should be cross-validated across several analytical platforms and with different statistical methods. The development of an appropriate clinical assay suitable for screening purposes will then allow advancement of metabolomic biomarker discovery into phase 2 clinical studies and beyond.

Conflict of interest statement

None declared.

Appendix A. Supplementary data

 

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Supplementary data 1 QUADAS-2 Evaluation.

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Footnotes

a Department of Surgery and Cancer, Imperial College London, 10th Floor, QEQM Wing, St Mary’s Hospital, London W2 1NY, UK

b Centre for Pathology, Imperial College London, 4th Floor, Clarence Wing, St Mary’s Hospital, London W2 1NY, UK

Corresponding author: Address: Head of Division of Surgery, Department of Surgery and Cancer, 10th Floor, QEQM, St Mary’s Hospital, Praed St., London W2 1NY, UK. Tel.: +44 2078862124; fax: +44 2078866309.