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

  • Authors:
    • Masanori Tokunaga
    • Kenjiro Kami
    • Soji Ozawa
    • Junya Oguma
    • Akihito Kazuno
    • Hayato Miyachi
    • Yoshiaki Ohashi
    • Masatoshi Kusuhara
    • Masanori Terashima
  • View Affiliations

  • Published online on: March 28, 2018     https://doi.org/10.3892/ijo.2018.4340
  • Pages: 1947-1958
Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

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.

Introduction

Esophageal cancer is the eighth most common type of cancer and the sixth leading cause of cancer-related mortality worldwide. It is frequently observed in East Asia (1). The clinicopathological characteristics of esophageal cancer have been investigated and clarified. Pathological tumor depth, nodal status and stage are known to be strongly associated with the survival outcome, which has been recently improved with advancements in multimodal treatments (2). However, the long-term survival outcome remains dismal, and the 5-year survival rate of patients with potentially curable advanced esophageal cancer has been reported to be only 34–55%, according to recent randomized controlled trials (3,4). To improve this poor survival outcome, appropriate treatment strategies tailored for each individual patient are warranted. To achieve this, the biological characteristics and causal factors of the survival outcome require clarification. Recently, it has been reported that the progression of the disease may affect the biological activity of some metabolites (5,6).

Metabolome analysis may enable us to understand tumor-specific metabolic characteristics, which would facilitate the discovery of novel anticancer drug targets and therapeutic strategies (7). Thus far, comparative metabolomic profiling has been conducted for several cancer types, such as gastric, lung, prostate, or colorectal cancers (7,8). Metabolomic profiles of esophageal cancer have also been investigated using blood samples (5,912) or paired tumor and non-tumor tissues (5,13,14). Metabolomic analysis using blood is preferable for the identification of tumor markers by comprehensive analysis; however, it does not reflect the microenvironment of the tumor, which can only be clarified using tissue samples. In addition, the majority of previous studies have used either nuclear magnetic resonance (NMR) (13,14) or gas chromatography-mass spectrometry (GC-MS) (15) for analysis. However, capillary electrophoresis-mass spectrometry (CE-MS), which is specialized for the analysis of ionic metabolites and thus may lead to the identification of novel metabolic properties of cancer, has rarely been used for the metabolomic analysis of paired tumor and non-tumor tissues. Furthermore, the associations between metabolomic characteristics and advancement of the disease or survival outcome have rarely been investigated and remain unclear. Although Wang et al clarified the associations between metabolomic characteristics and tumor stages, only 45 metabolites were identified by NMR analysis, and the associations between metabolomic characteristics and other clinical factors were not investigated (14).

Therefore, the aim of the present study was to clarify the potential association between pathological disease status and metabolome profiles of tissues in patients with esophageal cancer. We also investigated the differences in metabolomic characteristics between tumor and non-tumor tissues from patients with esophageal cancer.

Patients and methods

Patient characteristics

The present study was designed as a single-center, prospective observational study. The institutional review board of Tokai University (Isehara, Japan) approved the study protocol, which had the following inclusion criteria: i) Patients with histologically confirmed adenocarcinoma or squamous cell carcinoma of the esophagus undergoing curative esophagectomy; ii) the size of the primary tumor large enough to obtain 1 g of tumor tissue without affecting the pathological examination; iii) an age of 20 years or older; and iv) written informed consent. Pathological tumor depth, nodal status and stage were assigned according to the Japanese Classification of Esophageal Cancer, 11th edition (16).

Between May, 2012 and October, 2013, a total of 35 patients were enrolled in the present study, and 35 pairs of tumor (Ts) and non-tumor (NTs) esophageal tissues were obtained. The characteristics and pathological findings of the patients are presented in Table I. Neoadjuvant chemotherapy was administered to 17 patients, and the majority of patients underwent subtotal esophagectomy. The surgery was curative (R0) in 24 patients, and resulted in microscopic residual disease (R1) in 7 patients and macroscopic residual disease (R2) in 4 patients. The disease was advanced in the majority of the patients, and the pathological stage was III or IVa in 77% of the patients.

Table I

Characteristics of patients with adenocarcinoma or squamous cell carcinoma (SCC) of the esophagus.

Table I

Characteristics of patients with adenocarcinoma or squamous cell carcinoma (SCC) of the esophagus.

Sex, n
 Male30
 Female5
Age, years
 Median67
 Range42-81
Performance status, n
 030
 1  5
Neoajuvant chemotherapy, n
 +17
 −18
Histology
 Well differientated SCC13
 Moderately differientated SCC18
 Poorly differientated SCC  4
Tumor diameter (mm)
 Median55
 Range25-93
Lymphatic invasion
 −  6
 +29
Vascular invasion
 −  4
 +31
Tumor depth
 T1  1
 T2  7
 T323
 T4  4
Nodal status
 N0  8
 N1  5
 N216
 N3  5
 N4  1
Number of lymph node metastases
 Median  2
 Range0-9
Stage
 I  0
 II  8
 III22
 IVa  5
Curability
 R024
 R1  7
 R2  4
Tissue sampling and metabolite extraction

Tumor and surrounding tissues were surgically resected from each of the 35 patients with esophageal cancer immediately following esophagectomy. The resected tissue samples were promptly frozen in liquid nitrogen and stored at −80°C until metabolite extraction. To inactivate enzymes, ~50 mg of frozen tissue was immersed into 1,500 μl of 50% acetonitrile/Milli-Q water containing internal standards [H3304-1002; Human Metabolome Technologies (HMT), Tsuruoka, Japan] at 0°C. The tissue was homogenized 3 times at 1,500 rpm for 120 sec using a tissue homogenizer (Microsmash MS100R; Tomy Digital biology Co., Ltd., Tokyo, Japan) before the homogenate was centrifuged at 2,300 × g and 4°C for 5 min. Subsequently, 800 μl of the the upper aqueous layer were centrifugally filtered through a Millipore 5,000-Da cut-off filter at 9,100 × g and 4°C for 120 min to remove proteins. The filtrate was centrifugally concentrated and re-suspended in 50 μl of Milli-Q water for capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS) analysis.

Metabolome analysis

Metabolome analysis was conducted by the basic Scan package from HMT using CE-TOFMS based on previously described methods (17,18). Briefly, CE-TOFMS analysis was conducted using an Agilent CE capillary electrophoresis system equipped with an Agilent 6210 time-of-flight mass spectrometer (Agilent Technologies, Waldbronn, Germany). The systems were controlled by Agilent G2201AA ChemStation software version B.03.01 for CE (Agilent Technologies). The spectrometer was scanned from 50 to 1,000 m/z, and peaks were extracted using MasterHands automatic integration software (Keio University, Tsuruoka, Yamagata, Japan) to obtain peak information including m/z, peak area, and migration time (MT) (19). Signal peaks corresponding to isotopomers, adduct ions and other product ions of known metabolites were excluded, and based on their m/z values with the MTs, remaining peaks were annotated according to the HMT’s proprietary metabolite database. The areas of the annotated peaks were normalized based on internal standard levels and sample quantities to obtain relative levels of each metabolite.

Statistical analysis

Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were performed using the proprietary software from HMT, PeakStat and SampleStat, respectively. Detected metabolites were plotted on metabolic pathway maps using VANTED software (20). All continuous data, including age, tumor diameter and the number of lymph node metastases, are presented as medians (range) and were analyzed by the Wilcoxon rank-sum test. A value of P<0.05 was considered to indicate a statistically significant difference. For any compound that was not detected in a tissue from the subjects, half of the minimum value of the measured compound replaced the missing data. Metabolomic profiles were compared between i) tumor and non-tumor tissues to elucidate differences in metabolomic profiles between them; ii) patients with T1 or T2 disease (pT1-2) and those with T3 or T4 disease (pT3-4); and iii) patients with node-negative (pN) and node-positive (pN+) disease.

Results

Metabolomic characteristics between Ts and NTs

The metabolome data were normalized based on their z-values and used for PCA and HCA. The PCA plot presented in Fig. 1 shows a clear separation between NTs and Ts along the PC1 axis, indicating an apparently different metabolomic profile between NTs and Ts. The PCA plot also indicates a higher heterogeneity in the metabolomic profiles of Ts than of NTs. According to the HCA presented in Fig. 2, approximately two thirds of all the measured metabolites were higher in Ts than in NTs.

Metabolites measured in the present analysis were visualized on a metabolome-wide pathway map (available upon requested), and Fig. 3 illustrates the pathway map of the tricarboxylic acid (TCA) cycle. A total of 110 compounds were measured, and 99 compounds were absolutely quantified in this study (Table II). Of these, the concentrations of as many as 58 compounds were statistically significantly different between Ts and NTs (P<0.05). Fig. 4 and Table II illustrate all the measured metabolites in this study listed in descending order and based on Ts/NTs ratios. The concentrations of most amino acids apart from glutamine were significantly higher in Ts than in NTs (Fig. 5). In addition, as shown in Table II, the levels of nucleoside triphosphates [adenosine triphosphate (ATP), cytidine triphosphate (CTP), guanosine-5′-triphosphate (GTP) and uridine-5′-triphosphate (UTP)] were statistically significantly lower in Ts, whereas those of nucleoside monophosphates, such as guanosine monophosphate (GMP) were much higher. The concentrations of isocitric acid, cis-aconitic acid and citric acid, which are the upstream TCA cycle intermediates, were significantly lower in Ts than in NTs, while the lactic acid level was significantly higher in Ts.

Table II

Concentrations of compounds (listed in descending order based on Ts/NTs ratios) in Ts and NTs.

Table II

Concentrations of compounds (listed in descending order based on Ts/NTs ratios) in Ts and NTs.

Name of compoundNTsTsRatio (Ts/NTs)P-valueND/all (70)ND/NTs (35)ND/Ts (35)
2-Oxoglutaric acid0.0000.000NANA703535
cGMP0.0000.000NANA703535
dATP0.0000.000NANA703535
dCTP0.0000.000NANA703535
dTMP0.0000.000NANA703535
dTTP0.0000.000NANA703535
Glyceraldehyde 3-phosphate0.0000.000NANA703535
Glycolic acid0.0000.000NANA703535
Glyoxylic acid0.0000.000NANA703535
Malonyl CoA_divalent0.0000.000NANA703535
Thymine0.0000.000NANA703535
Cys2.737182.62266.72<0.0011916  3
Glutathione (GSH)55.380839.39715.16<0.0011310  3
Uracil21.761168.6677.75<0.001  1  1  0
Betaine aldehyde_+H2O0.0940.6747.20<0.001453114
Hypoxanthine170.444746.5274.38<0.001  0  0  0
S-Adenosylmethionine12.25440.4063.30<0.001  0  0  0
Putrescine36.506119.3423.27<0.001  0  0  0
Gluconic acid28.16887.5673.11<0.001  0  0  0
Guanine15.20543.2182.840.226  2  0  2
GMP29.55676.0262.57<0.001  0  0  0
Met97.882243.8542.49<0.001  0  0  0
GABA12.25030.2952.47<0.001  0  0  0
Tyramine0.1660.4082.460.404643331
Choline161.699395.9252.45<0.001  0  0  0
Homoserine0.8432.0012.37<0.0011210  2
Pro377.514890.4932.36<0.001  0  0  0
Citrulline30.40369.1602.27<0.001  0  0  0
Asn124.410275.3762.21<0.001  0  0  0
Tyr140.516307.5462.19<0.001  0  0  0
Hydroxyproline28.39158.9422.08<0.001  0  0  0
β-Ala36.97375.2602.04<0.001  0  0  0
Betaine47.05694.0122.00<0.001  0  0  0
Ile265.350510.5841.92<0.001  0  0  0
Leu492.049914.8611.86<0.001  0  0  0
Phe229.967418.2001.82<0.001  0  0  0
Asp403.697729.0621.81<0.001  0  0  0
Guanosine16.50428.8721.750.001  0  0  0
Gly1615.6872817.1761.74<0.001  0  0  0
Glu1862.7623242.0471.74<0.001  0  0  0
CoA_divalent0.5750.9841.710.463542826
His222.272375.2961.69<0.001  0  0  0
Val564.942937.0111.66<0.001  0  0  0
NADP+6.22610.2561.650.142  5  3  2
Inosine118.058193.1431.64<0.001  0  0  0
Adenine0.9451.5101.60<0.001  0  0  0
Thr540.238841.3031.56<0.001  0  0  0
Trp56.49687.2911.55<0.001  0  0  0
Cytosine0.0750.1131.500.011643529
Ornithine102.318150.0591.470.010  0  0  0
Uridine50.31072.3031.440.108  0  0  0
AMP234.908327.7231.400.170  0  0  0
Sarcosine11.92916.5841.390.091  0  0  0
Fructose 6-phosphate13.41818.2091.360.196  7  5  2
Lactic acid30047.27740727.3231.360.001  0  0  0
UMP39.62653.3201.350.320  0  0  0
Lys728.484958.4091.320.002  0  0  0
Succinic acid385.179497.4391.290.140  0  0  0
Arg396.418507.5141.280.010  0  0  0
GDP28.00435.6861.270.036  1  1  0
Glycerol 3-phosphate211.332268.6401.270.054  0  0  0
Sedoheptulose 7-phosphate19.23824.4361.270.095  0  0  0
Ser467.826590.5771.260.077  0  0  0
3-Hydroxybutyric acid287.678355.9801.240.051  0  0  0
Glucose 1-phosphate25.48931.1501.220.362  0  0  0
IMP31.86337.7421.180.506  1  0  1
Glutathione (GSSG)_divalent560.285663.0951.180.674  0  0  0
Glucose 6-phosphate84.78399.7491.180.870  0  0  0
Thymidine1.1001.2731.160.082673532
CMP10.60012.0121.130.664  7  5  2
2-Hydroxybutyric acid115.702130.9601.130.344  0  0  0
Ribulose 5-phosphate33.89437.2431.100.753  0  0  0
Spermidine17.56518.9661.080.326  0  0  0
Creatine1608.6151719.8581.070.907  0  0  0
Anthranilic acid0.2330.2491.070.241633330
6-Phosphogluconic acid15.03015.8991.060.318  3  1  2
2-Phosphoglyceric acid8.4738.9181.050.812211011
N,N-Dimethylglycine3.6403.8021.040.398  3  1  2
PRPP1.4231.4861.041.000683434
NAD+156.517163.0761.040.815  0  0  0
dTDP0.6560.6751.030.331693534
Phosphoenolpyruvic acid4.2174.2851.020.947502525
Ala1740.6991756.5701.010.788  0  0  0
Acetyl CoA_divalent0.4110.4141.011.000683434
cAMP0.4250.4220.990.592673334
Fructose 1,6-diphosphate66.85366.2650.990.072  1  1  0
Cytidine3.4523.3560.970.331693435
Malic acid377.443357.1650.950.362  0  0  0
3-Phosphoglyceric acid74.82469.7610.930.247  0  0  0
Creatinine57.64651.4420.890.051  0  0  0
ADP248.784207.3150.830.011  0  0  0
Fumaric acid57.78547.3150.820.019  1  0  1
Gln2277.7791703.2720.75<0.001  0  0  0
Ribose 5-phosphate11.7367.8140.67<0.00111  110
UDP42.20027.6040.65<0.001  0  0  0
Carnosine2.4191.3970.58<0.001  4  0  4
Dihydroxyacetone phosphate28.79816.3840.57<0.001  5  0  5
Pyruvic acid24.61013.8100.560.011612734
GTP34.03717.9620.53<0.001  0  0  0
CDP7.0513.3780.48<0.00114  6  8
Spermine10.2614.7080.460.1511910  9
Citric acid308.711128.5750.42<0.001  0  0  0
Adenosine8.0603.1570.39<0.001  0  0  0
ATP424.260162.9690.38<0.001  0  0  0
Erythrose 4-phosphate5.0641.8010.360.042632934
cis-Aconitic acid5.7242.0250.35<0.001471631
UTP77.54324.6240.32<0.001  3  0  3
Isocitric acid7.3472.2040.30<0.001461630
2-Oxoisovaleric acid5.4661.4980.27<0.001471730
CTP14.0882.9460.21<0.00128  424

[i] ND, not detected; cGMP, cyclic guanosine monophosphate; dATP, deoxyadenosine triphosphate; dCTP, deoxycytidine 5′-triphosphate; dTMP, deoxythymidine monophosphate; dTTP, deoxythymidine triphosphate; Cys, cysteine; GMP, guanosine monophosphate; GABA, gamma-aminobutyric acid; Pro, proline; Asn, asparagine; Tyr, tyrosine; β-Ala, β-alanine; Ile, isoleucine; Leu, leucine; Phe, phenylalanine; Asp, aspartic acid; Gly, glycine; Glu, glutamic acid; His, histidine; Val, valine; NADP, nicotinamide adenine dinucleotide phosphate; Thr, threonine; Trp, tryptophan; AMP, adenosine monophosphate; UMP, uridine monophosphate; Lys, lysine; Arg, arginine; GDP, guanosine diphosphate; Ser, serine; IMP, inosine monophosphate; CMP, cytidine monophosphate; PRPP, phosphoribosyl diphosphate; NAD, nicotinamide adenine dinucleotide; dTDP, deoxythymidine diphosphate; Ala, alanine; cAMP, cyclic adenosine monophosphate; ADP, adenosine diphosphate; Gln, glutamine; UDP, uridine diphosphate; GTP, guanosine triphosphate; CDP, cytidine diphosphate; ATP, adenosine triphosphate; UTP, uridine triphosphate; CTP, cytidine triphosphate.

Metabolomics with pathological tumor depth (pT) and pathological nodal status (pN) relevance

Tumor depth is known to be associated with the expression levels of glucose transporter (21) and several glycolytic enzymes, such as hexokinase 2 (22) and pyruvate kinase M2 (23). Thus, in this study, the tumor concentrations of the quantified metabolites were compared between pT1-2 and pT3-4 tumor tissues. Table III presents a list of metabolites of which the concentrations were at least 1.5-fold higher (7 metabolites) or lower (21 metabolites) in pT3-4 than in pT1-2). The concentrations of glycolytic and pentose phosphate pathway intermediates were higher overall in subjects with advanced disease (pT3-4), and the ratios of glucose 1-phosphate, ribose 5-phosphate and ribulose 5-phosphate were 1.92, 1.58 and 1.56, respectively, and >1.5-fold higher in pT3-4 than pT1 -2. By contrast, the concentrations of malic acid and citric acid, also TCA cycle intermediates, and most nucleotides were significantly lower in pT3-4 than in pT1-2, possibly rationalizing relatively hypoxic microenvironment of advanced tumor tissues (24). Moreover, adenine-, cytidine- and uridine-nucleotide concentrations were lower in pT3-4 than in pT1-2 tumors, while the glutathione and cysteine levels were higher in pT3-4 than in pT1-2, with ratios being 1.80 and 3.36, respectively (Table III).

Table III

Concentrations of compounds in pT1-2 and pT3-4, and the pT3-4/pT1-2 ratio.

Table III

Concentrations of compounds in pT1-2 and pT3-4, and the pT3-4/pT1-2 ratio.

Compound namepT1-2pT3-4Ratio (pT3-4/pT1-2)P-value
CTP7.9681.4590.180.022
UTP60.53213.9840.230.651
UDP63.98816.8230.260.088
CDP7.5822.1330.280.030
UMP110.41636.4030.330.010
IMP76.81726.1640.340.046
CMP23.9808.4660.350.007
ATP290.826125.0850.430.406
GTP29.73114.4750.490.143
2-Oxoisovaleric acid2.3991.2310.510.033
AMP517.276271.5590.520.019
CoA_divalent1.5490.8170.530.818
NADP+15.2628.7730.570.112
GDP51.22131.0830.610.009
Citric acid182.291112.6590.620.008
Spermidine26.80816.6420.620.104
Malic acid502.515314.0980.630.015
NAD+228.540143.6800.630.034
ADP288.720183.1950.630.104
Sarcosine22.65314.7860.650.017
Isocitric acid2.9921.9710.660.287
Ribulose 5-phosphate26.04940.5601.560.143
Ribose 5-phosphate5.3908.5321.580.858
Guanosine18.39031.9771.740.042
Glutathione (GSH)520.187933.9781.800.356
Glucose 1-phosphate18.22834.9791.920.923
Tyramine0.1460.4863.330.972
Cys64.742217.5493.360.103

[i] CTP, cytidine triphosphate; UTP, uridine triphosphate; UDP, uridine diphosphate; CDP, cytidine diphosphate; UMP, uridine monophosphate; IMP, inosine monophosphate; CMP, cytidine monophosphate; ATP, adenosine triphosphate; GTP, guanosine triphosphate; AMP, adenosine monophosphate; NADP, nicotinamide adenine dinucleotide phosphate; GDP, guanosine diphosphate; NAD, nicotinamide adenine dinucleotide; ADP, adenosine diphosphate; Cys, cysteine.

Metastatic alterations seemingly affect the balance of energy metabolism between glycolysis and oxidative phosphorylation (25,26). Jin et al identified a series of serum metabolites, such as valine and GABA that differ significantly in patients with esophageal squamous cell carcinoma with or without lymph node metastasis using a metabolomics approach (27). In this study, we thus investigated whether there was any metabolic difference in primary tumor tissues with or without metastasis. Table IV lists the metabolites the concentrations of which were at least 1.5-fold higher (2 metabolites) or lower (18 metabolites) in pN+ than in pN. N,N-dimethylglycine, isocitric acid, fructose 1,6-diphosphate and aspartic acid were statistically significantly lower in the pN+ than the pN tumor tissues. Of note, many nucleotide concentrations including ATP, GTP, CTP and UTP tended to be lower in the pN+ than pN tumors, although the difference was not statistically significant, with the exception of IMP and UMP.

Table IV

Concentrations of compounds in pN− and pN+, and the pN+/pN− ratio.

Table IV

Concentrations of compounds in pN− and pN+, and the pN+/pN− ratio.

Compound nameNN+Ratio (N+/N)P-value
CoA_divalent2.0940.6560.310.201
N,N-Dimethylglycine6.4923.0040.460.010
ATP268.010131.8450.490.630
Glutathione (GSH)1361.444684.7170.500.130
IMP58.62431.5550.540.027
UTP37.77920.7260.550.280
CDP5.0712.8770.570.374
Sedoheptulose 7-phosphate36.05420.9930.580.061
UMP78.12545.9700.590.019
Ribulose 5-phosphate54.33232.1800.590.286
Isocitric acid3.1991.9090.600.039
GTP25.89215.6120.600.428
Fructose 1,6-diphosphate94.45957.9110.610.015
Asp1030.898639.6290.620.041
CTP4.1552.5880.620.830
UDP38.59724.3470.630.056
Ribose 5-phosphate10.9236.8930.630.538
2-Oxoisovaleric acid2.0421.3370.650.287
Guanine30.86246.8791.520.860
Phosphoenolpyruvic acid2.5374.8031.890.825

[i] ATP, adenosine triphosphate; IMP, inosine monophosphate; UTP, uridine triphosphate; CDP, cytidine diphosphate; UMP, uridine monophosphate; GTP, guanosine triphosphate; Asp, aspartic acid; GTP, guanosine triphosphate; UDP, uridine diphosphate.

Discussion

Thus far, metabolomic differences between tumor and non-tumor tissues have been investigated elsewhere in various types of cancer (7,8,13,14). The results of the present study not only demonstrated the basal metabolomic differences between esophageal tumor and non-tumor tissues, but also identified intriguing associations of metabolites with the degree of tumor advancement and with the presence or absence of lymph node metastasis.

Statistical significances between Ts and NTs were found in 58 out of 110 compounds, including isocitric acid, cis-aconitic acid, and citric acid, which were significantly lower in Ts than NTs, and lactic acid, which was significantly higher in Ts. These features suggest the upregulation of glycolysis and lactate formation, and the downregulation of the flux into the TCA cycle, and thus corroborate the hallmark of cancer metabolism i.e., the Warburg effect (7,28).

In the present study, the tumor concentrations of all amino acids apart from glutamine were higher than their non-tumor counterparts. Amino acid synthesis may be globally enhanced; however, this does not explain the significantly higher concentrations of even essential amino acids. The data thus possibly imply the hyperactivity of amino acid transporters (2931) or autophagic protein degradation (32), both of which contribute to the accumulation of overall amino acids in tumor tissues. Glutamine, however, was the only amino acid that was lower in the tumor than the non-tumor tissues. This is presumably due to hyperactive glutamine breakdown, or glutaminolysis, for producing energy and building blocks for continuous proliferation (33,34). In fact, this trend of overall accumulations of amino acids apart from glutamine in tumor regions has been reported elsewhere (7,8,14); accordingly, the near universality of this tumor amino acid profile is intriguing, and the result is reported herein for the first time (at least to the best of our knowledge) for an esophageal tumor.

Few studies have investigated the association between metabolomic characteristics and the pathological status of tumor tissues. However, Wang et al reported 12 key metabolites, such as glucose, AMP, NAD, formate, creatine and choline metabolites that exhibited strong associations with the advancement of esophageal cancer, and are thus likely to be involved in both the carcinogenic process and metastatic alteration of esophageal cancer (14). While attempting to corroborate previous studies, we identified a novel set of metabolites that show significant correlations with the advancement of cancer, such as glycolytic and pentose phosphate pathway intermediates (Table III), taking advantage of CE-TOFMS-based metabolomics, which is best suited to ionic metabolite analysis.

In contrast to glycolytic and pentose phosphate pathway intermediates, the concentrations of citric acid, isocitric acid and malic acid in pT3-4 disease were relatively lower than in pT1-2 disease, suggesting the downregulation of TCA cycle activity in advanced tumors. These results, i.e., a lower TCA cycle activity and accelerated glycolysis, may be due to a more enhanced Warburg effect in advanced-stage tumors compared with less advanced ones.

A series of nucleotide concentrations were lower in advanced than in less advanced tumors (Table III). Although higher levels of nucleotide metabolites in the advanced tumors were expected, the nucleotide pathway intermediates were mostly lower in the advanced ones. This is possibly due to accelerated utilizations of these nucleotides for their increased DNA synthesis. A lower adenosine monophosphate level in advanced than in less advanced tumors has also been previously reported (14). Total adenylate levels (ATP + ADP + AMP) in pT3-4 (579.8 nmol/g tissue) was almost half of those in pT1-2 (1096.8 nmol/g), again indicating a higher demand of nucleotides in pT3-4 than in pT1-2 tumor tissues for their increased DNA synthesis. The levels of glutathione and cysteine, two primary anti-oxidants, were on average higher in pT3-4 than in pT1-2, indicating a more reduced status and higher resistance against oxidative stress in pT3-4.

Of note, in cases with pN+, both glutathione and cysteine levels were lower than in cases with pN, with ratios being 0.50 (P=0.130) (Table IV) and 0.83, respectively, translating to a lower resistance against oxidative stress in pN+ (note that the ratio of cysteine is not shown in Table IV). Generally, the tumor microenvironment is in a highly oxidative state, and thus, tumor cells tend to be more resistant to oxidative stress. Pavlides et al (35) proposed that stromal tissues rely primarily on glycolysis, producing lactate and ketones, whereas metastatic cancer cells rather use oxidative phosphorylation for energy production, availing the carbon sources provided by the neighboring stromal tissues, and coined the term, ‘reverse Warburg effect’ (35,36). In this perspective, proliferative tumor regions may contain more cells that mainly use typical Warburg-type energy metabolism, which presumably reduces oxidative stress assuming that oxidative phosphorylation via electron transport chain is a primary source of reactive oxygen species (ROS) (37). By contrast, metastatic tumor cells are rich in mitochondria, producing higher concentrations of ROS, and thus may develop a tumor microenvironment with higher oxidative stress (25,26,36). Taken together, the results thus reflect the basal metabolic differences between advanced (but without metastatic) and metastatic tumors.

The present study is limited to the elucidation of the metabolic microenvironment of tissues with or without cancerous cells and may not be suitable for discovery of a potential biomarker for early detection of cancer, as our analysis was performed using surgically resected specimens and not liquid biopsies. Although not as comprehensive as our study, the metabolomics of biopsy specimens are being realized (3840). Moreover, once we focus on some specific metabolite markers for pathological tumor status and survival outcome, a minimal amount of tissue, such as a biopsy specimen, may be sufficient for such targeted analysis.

A limitation of this study is that the effects of potential confounding factors affecting the metabolome characteristics, such as the use of chemotherapy and each patient’s nutritional status, could not be eliminated. Therefore, the difference in metabolome characteristics between advanced and less-advanced tumors might have been influenced by these confounding factors. Due to the limited number of cases in this study, it would be difficult to exclude the effects of all potential confounding factors completely; however, these effects should be clarified in future trials with sufficient numbers of cases.

In conclusion, in this study, we demonstrated significantly different metabolomic characteristics between tumor and non-tumor tissues of esophageal cancer and identified a novel set of metabolites that correlate well with the degree of tumor advancement. This suggests that the pathological disease status and survival outcome may be predicted by analysis of several primary metabolites, possibly even from a biopsy specimen. Further clarification of cancer metabolomics, particularly in relation to the advancement of disease and survival outcome, will enable the selection of more appropriate treatment strategies contributing to individualized medicine.

Acknowledgments

The authors would like to thank Dr Tamaki Fujimori and Ms. Aya Hoshi, HMT, for their data analysis support. The authors used the English Language Service (International Medical Information Center) for language editing.

Funding

This study was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI (Grant no. JP26461998).

Availability of data and materials

The analyzed datasets generated during the study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

The Institutional Review board of Tokai University (Isehara, Japan) approved the study protocol and all patients provided written informed consent prior to obtaining the samples.

Authors’ contributions

MTo, KK and SO conceived and designed the study; MTo, KK, JO and AK were involved in data acquisition; KK and YO were involved in data analysis; MTo, KK, SO, HM, MK and MTe were involved in data interpretation. All authors have read and approval the final manuscript.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Domper Arnal MJ, Ferrández Arenas Á and Lanas Arbeloa Á: Esophageal cancer: Risk factors, screening and endoscopic treatment in Western and Eastern countries. World J Gastroenterol. 21:7933–7943. 2015. View Article : Google Scholar

2 

Tachimori Y, Ozawa S, Numasaki H, Fujishiro M, Matsubara H, Oyama T, Shinoda M, Toh Y, Udagawa H and Uno T: Comprehensive Registry of Esophageal Cancer in Japan, 2008. Esophagus. 12:130–157. 2015. View Article : Google Scholar

3 

van Hagen P, Hulshof MC, van Lanschot JJ, Steyerberg EW, van Berge Henegouwen MI, Wijnhoven BP, Richel DJ, Nieuwenhuijzen GA, Hospers GA, Bonenkamp JJ, et al CROSS Group: Preoperative chemoradiotherapy for esophageal or junctional cancer. N Engl J Med. 366:2074–2084. 2012. View Article : Google Scholar

4 

Ando N, Kato H, Igaki H, Shinoda M, Ozawa S, Shimizu H, Nakamura T, Yabusaki H, Aoyama N, Kurita A, et al: A randomized trial comparing postoperative adjuvant chemotherapy with cisplatin and 5-fluorouracil versus preoperative chemotherapy for localized advanced squamous cell carcinoma of the thoracic esophagus (JCOG9907). Ann Surg Oncol. 19:68–74. 2012. View Article : Google Scholar

5 

Abbassi-Ghadi N, Kumar S, Huang J, Goldin R, Takats Z and Hanna GB: Metabolomic profiling of oesophago-gastric cancer: A systematic review. Eur J Cancer. 49:3625–3637. 2013. View Article : Google Scholar

6 

Pavlova NN and Thompson CB: The emerging hallmarks of cancer metabolism. Cell Metab. 23:27–47. 2016. View Article : Google Scholar

7 

Kami K, Fujimori T, Sato H, Sato M, Yamamoto H, Ohashi Y, Sugiyama N, Ishihama Y, Onozuka H, Ochiai A, et al: Metabolomic profiling of lung and prostate tumor tissues by capillary electrophoresis time-of-flight mass spectrometry. Metabolomics. 9:444–453. 2013. View Article : Google Scholar

8 

Hirayama A, Kami K, Sugimoto M, Sugawara M, Toki N, Onozuka H, Kinoshita T, Saito N, Ochiai A, Tomita M, et al: Quantitative metabolome profiling of colon and stomach cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry. Cancer Res. 69:4918–4925. 2009. View Article : Google Scholar

9 

Ikeda A, Nishiumi S, Shinohara M, Yoshie T, Hatano N, Okuno T, Bamba T, Fukusaki E, Takenawa T, Azuma T, et al: Serum metabolomics as a novel diagnostic approach for gastrointestinal cancer. Biomed Chromatogr. 26:548–558. 2012. View Article : Google Scholar

10 

Xu J, Chen Y, Zhang R, Song Y, Cao J, Bi N, Wang J, He J, Bai J, Dong L, et al: Global and targeted metabolomics of esophageal squamous cell carcinoma discovers potential diagnostic and therapeutic biomarkers. Mol Cell Proteomics. 12:1306–1318. 2013. View Article : Google Scholar

11 

Zhang X, Xu L, Shen J, Cao B, Cheng T, Zhao T, Liu X and Zhang H: Metabolic signatures of esophageal cancer: NMR-based metabolomics and UHPLC-based focused metabolomics of blood serum. Biochim Biophys Acta. 1832:1207–1216. 2013. View Article : Google Scholar

12 

Ma H, Hasim A, Mamtimin B, Kong B, Zhang HP and Sheyhidin I: Plasma free amino acid profiling of esophageal cancer using high-performance liquid chromatography spectroscopy. World J Gastroenterol. 20:8653–8659. 2014. View Article : Google Scholar

13 

Yang Y, Wang L, Wang S, Liang S, Chen A, Tang H, Chen L and Deng F: Study of metabonomic profiles of human esophageal carcinoma by use of high-resolution magic-angle spinning 1H NMR spectroscopy and multivariate data analysis. Anal Bioanal Chem. 405:3381–3389. 2013. View Article : Google Scholar

14 

Wang L, Chen J, Chen L, Deng P, Bu Q, Xiang P, Li M, Lu W, Xu Y, Lin H, et al: 1H-NMR based metabonomic profiling of human esophageal cancer tissue. Mol Cancer. 12:252013. View Article : Google Scholar

15 

Wu H, Xue R, Lu C, Deng C, Liu T, Zeng H, Wang Q and Shen X: Metabolomic study for diagnostic model of oesophageal cancer using gas chromatography/mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci. 877:3111–3117. 2009. View Article : Google Scholar

16 

The Japan Eshophageal Society: Japanese Classification of Esophageal Cancer 11th edition. Esophagus. Nov 10–2016.Epub ahead of print.

17 

Ohashi Y, Hirayama A, Ishikawa T, Nakamura S, Shimizu K, Ueno Y, Tomita M and Soga T: Depiction of metabolome changes in histidine-starved Escherichia coli by CE-TOFMS. Mol Biosyst. 4:135–147. 2008. View Article : Google Scholar

18 

Ooga T, Sato H, Nagashima A, Sasaki K, Tomita M, Soga T and Ohashi Y: Metabolomic anatomy of an animal model revealing homeostatic imbalances in dyslipidaemia. Mol Biosyst. 7:1217–1223. 2011. View Article : Google Scholar

19 

Sugimoto M, Wong DT, Hirayama A, Soga T and Tomita M: Capillary electrophoresis mass spectrometry-based saliva metab-olomics identified oral, breast and pancreatic cancer-specific profiles. Metabolomics. 6:78–95. 2010. View Article : Google Scholar

20 

Junker BH, Klukas C and Schreiber F: VANTED: A system for advanced data analysis and visualization in the context of biological networks. BMC bioinformatics. 7:1092006. View Article : Google Scholar

21 

Kawamura T, Kusakabe T, Sugino T, Watanabe K, Fukuda T, Nashimoto A, Honma K and Suzuki T: Expression of glucose transporter-1 in human gastric carcinoma: Association with tumor aggressiveness, metastasis, and patient survival. Cancer. 92:634–641. 2001. View Article : Google Scholar

22 

Hamabe A, Yamamoto H, Konno M, Uemura M, Nishimura J, Hata T, Takemasa I, Mizushima T, Nishida N, Kawamoto K, et al: Combined evaluation of hexokinase 2 and phosphorylated pyruvate dehydrogenase-E1α in invasive front lesions of colorectal tumors predicts cancer metabolism and patient prognosis. Cancer Sci. 105:1100–1108. 2014. View Article : Google Scholar

23 

Fukuda S, Miyata H, Miyazaki Y, Makino T, Takahashi T, Kurokawa Y, Yamasaki M, Nakajima K, Takiguchi S, Mori M, et al: Pyruvate kinase M2 modulates esophageal squamous cell carcinoma chemotherapy response by regulating the pentose phosphate pathway. Ann Surg Oncol. 22(Suppl 3): S1461–S1468. 2015. View Article : Google Scholar

24 

Hockel M, Schlenger K, Aral B, Mitze M, Schaffer U and Vaupel P: Association between tumor hypoxia and malignant progression in advanced cancer of the uterine cervix. Cancer Res. 56:4509–4515. 1996.

25 

Payen VL, Porporato PE, Baselet B and Sonveaux P: Metabolic changes associated with tumor metastasis, part 1: Tumor pH, glycolysis and the pentose phosphate pathway. Cell Mol Life Sci. 73:1333–1348. 2016. View Article : Google Scholar

26 

Porporato PE, Payen VL, Baselet B and Sonveaux P: Metabolic changes associated with tumor metastasis, part 2: Mitochondria, lipid and amino acid metabolism. Cell Mol Life Sci. 73:1349–1363. 2016. View Article : Google Scholar

27 

Jin H, Qiao F, Chen L, Lu C, Xu L and Gao X: Serum metabolomic signatures of lymph node metastasis of esophageal squamous cell carcinoma. J Proteome Res. 13:4091–4103. 2014. View Article : Google Scholar

28 

Warburg O: On the origin of cancer cells. Science. 123:309–314. 1956. View Article : Google Scholar

29 

Honjo H, Kaira K, Miyazaki T, Yokobori T, Kanai Y, Nagamori S, Oyama T, Asao T and Kuwano H: Clinicopathological significance of LAT1 and ASCT2 in patients with surgically resected esophageal squamous cell carcinoma. J Surg Oncol. 113:381–389. 2016. View Article : Google Scholar

30 

Kobayashi H, Ishii Y and Takayama T: Expression of L-type amino acid transporter 1 (LAT1) in esophageal carcinoma. J Surg Oncol. 90:233–238. 2005. View Article : Google Scholar

31 

Younes M, Pathak M, Finnie D, Sifers RN, Liu Y and Schwartz MR: Expression of the neutral amino acids transporter ASCT1 in esophageal carcinomas. Anticancer Res. 20:3775–3779. 2000.

32 

Morselli E, Galluzzi L, Kepp O, Vicencio JM, Criollo A, Maiuri MC and Kroemer G: Anti- and pro-tumor functions of autophagy. Biochim Biophys Acta. 1793:1524–1532. 2009. View Article : Google Scholar

33 

Vander Heiden MG, Cantley LC and Thompson CB: Understanding the Warburg effect: The metabolic requirements of cell proliferation. Science. 324:1029–1033. 2009. View Article : Google Scholar

34 

Tsun ZY and Possemato R: Amino acid management in cancer. Semin Cell Dev Biol. 43:22–32. 2015. View Article : Google Scholar

35 

Pavlides S, Whitaker-Menezes D, Castello-Cros R, Flomenberg N, Witkiewicz AK, Frank PG, Casimiro MC, Wang C, Fortina P, Addya S, et al: The reverse Warburg effect: Aerobic glycolysis in cancer associated fibroblasts and the tumor stroma. Cell Cycle. 8:3984–4001. 2009. View Article : Google Scholar

36 

Sotgia F, Whitaker-Menezes D, Martinez-Outschoorn UE, Flomenberg N, Birbe RC, Witkiewicz AK, Howell A, Philp NJ, Pestell RG and Lisanti MP: Mitochondrial metabolism in cancer metastasis: Visualizing tumor cell mitochondria and the ‘reverse Warburg effect’ in positive lymph node tissue. Cell Cycle. 11:1445–1454. 2012. View Article : Google Scholar

37 

Orrenius S: Reactive oxygen species in mitochondria-mediated cell death. Drug Metab Rev. 39:443–455. 2007. View Article : Google Scholar

38 

Benahmed MA, Elbayed K, Daubeuf F, Santelmo N, Frossard N and Namer IJ: NMR HRMAS spectroscopy of lung biopsy samples: Comparison study between human, pig, rat, and mouse metabolomics. Magn Reson Med. 71:35–43. 2014. View Article : Google Scholar

39 

Li M, Song Y, Cho N, Chang JM, Koo HR, Yi A, Kim H, Park S and Moon WK: An HR-MAS MR metabolomics study on breast tissues obtained with core needle biopsy. PLoS One. 6:e255632011. View Article : Google Scholar

40 

Choi JS, Baek HM, Kim S, Kim MJ, Youk JH, Moon HJ, Kim EK and Nam YK: Magnetic resonance metabolic profiling of breast cancer tissue obtained with core needle biopsy for predicting pathologic response to neoadjuvant chemotherapy. PLoS One. 8:e838662013. View Article : Google Scholar

Related Articles

Journal Cover

June-2018
Volume 52 Issue 6

Print ISSN: 1019-6439
Online ISSN:1791-2423

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Tokunaga M, Kami K, Ozawa S, Oguma J, Kazuno A, Miyachi H, Ohashi Y, Kusuhara M and Terashima M: Metabolome analysis of esophageal cancer tissues using capillary electrophoresis-time-of-flight mass spectrometry. Int J Oncol 52: 1947-1958, 2018
APA
Tokunaga, M., Kami, K., Ozawa, S., Oguma, J., Kazuno, A., Miyachi, H. ... Terashima, M. (2018). Metabolome analysis of esophageal cancer tissues using capillary electrophoresis-time-of-flight mass spectrometry. International Journal of Oncology, 52, 1947-1958. https://doi.org/10.3892/ijo.2018.4340
MLA
Tokunaga, M., Kami, K., Ozawa, S., Oguma, J., Kazuno, A., Miyachi, H., Ohashi, Y., Kusuhara, M., Terashima, M."Metabolome analysis of esophageal cancer tissues using capillary electrophoresis-time-of-flight mass spectrometry". International Journal of Oncology 52.6 (2018): 1947-1958.
Chicago
Tokunaga, M., Kami, K., Ozawa, S., Oguma, J., Kazuno, A., Miyachi, H., Ohashi, Y., Kusuhara, M., Terashima, M."Metabolome analysis of esophageal cancer tissues using capillary electrophoresis-time-of-flight mass spectrometry". International Journal of Oncology 52, no. 6 (2018): 1947-1958. https://doi.org/10.3892/ijo.2018.4340