Metabolome analysis of esophageal cancer tissues using capillary electrophoresis-time-of-flight mass spectrometry
- Authors:
- Published online on: March 28, 2018 https://doi.org/10.3892/ijo.2018.4340
- Pages: 1947-1958
Abstract
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 (5,9–12) 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 ICharacteristics of patients with adenocarcinoma or squamous cell carcinoma (SCC) of the esophagus. |
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 IIConcentrations of compounds (listed in descending order based on Ts/NTs ratios) in Ts and NTs. |
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).
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.
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 (29–31) 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 (38–40). 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.
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