Serum Metabolomic Signatures of Lymph Node ... - ACS Publications

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Serum Metabolomic Signatures of Lymph Node Metastasis of Esophageal Squamous Cell Carcinoma Hai Jin,†,§ Fan Qiao,†,§ Ling Chen,† Chengjun Lu,† Li Xu,† and Xianfu Gao*,‡ †

Department of Cardiothoracic Surgery, Changhai Hospital, Second Military Medical University, Shanghai 200433, China Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

‡

S Supporting Information *

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

1. INTRODUCTION

methods in order to support the ability of clinicians to tailor treatment regimes and improve prognosis of ESCC. Evidence that cancer is primarily a metabolic disease enabled investigations to identify biomarkers for diagnosis and prognosis as well as the pathological mechanism of many cancers from the perspective of metabolism.4 Metabolomics, a growing field of systems biology, aims to quantitatively measure as many small molecule metabolites as possible in a given biological system in order to acquire an overview of the metabolic or disease status and global biochemical events associated with a cellular or biological system.5 It is known that a minor alteration at the level of gene or protein expression usually results in a significant change in small molecule metabolite level; therefore, metabolomics is an intensive and direct approach for studying diseases and has been applied to identify biomarkers for the early prediction and diagnosis of cancers6−13 as well as to obtain fundamental mechanistic insights into carcinogenesis,8,11 metastasis, and staging of cancer.14,15

Esophageal cancer, one of the most common malignant tumors, was ranked as the sixth leading cause of cancer mortality worldwide and the fourth leading cause of death from cancer in China.1,2 There are two major types of esophageal cancer, esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma, with more than 90% of esophageal cancer patients having ESCC in China and other countries of East Asia.1,2 Most ESCC patients are diagnosed at an advanced stage with lymph node metastasis. The lymphatic system serves as the primary pathway for metastasis, and recent data indicate that lymph node metastasis is a key prognostic factor of the clinical outcome of ESCC patients. The overall 5-year survival rate following surgery is between 70 and 92% for nonmetastatic EC patients, whereas it is only 18−47% for metastatic EC patients.3 Although significant progress has been made through surgery and adjuvant chemoradiotherapy, the prognosis of ESCC patients still remains poor, which might be explained by the presence of few early symptoms as well as the current lack of sensitive approaches for early diagnosis. Thus, it is crucial to diagnose early and stage ESCC patients according to lymph node metastasis by more sensitive detection © XXXX American Chemical Society

Received: May 14, 2014

A

dx.doi.org/10.1021/pr500483z | J. Proteome Res. XXXX, XXX, XXX−XXX

Journal of Proteome Research

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Table 1. Clinical Characteristics of Patients and Healthy Human Subjects lymph node non-metastatic ESCC patients no. of subjects age [mean (range)] gender (male/female) TNM Stage I II III IV

lymph node metastatic ESCC patients

40 60.4 (44−73) 29/11

40 63.0 (45−81) 35/5

IA: 2 IIA: 38 0 0

0 IIB: 9 IIIA: 19; IIIB: 8; IIIC: 1 3

healthy controls 30 57.7 (41−74) 22/8

age (p = 0.183) between patients in the non-metastasis [60.4 (44−73) years old] and metastasis [63.0 (45−81) years old] groups. Blood samples from healthy volunteers (n = 30) were obtained under fasting conditions. The clinical features of the patients as well as basic information on the healthy human subjects are provided in Table 1, and more detailed information is provided in Supporting Information Table S1. Each blood sample was allowed to clot for 45 min and was then centrifuged at 3000g for 10 min. The serum was collected, aliquoted into a separate vial, and frozen at −80 °C until GC/MS analysis.

Recently, metabolomics has been applied to the study of esophageal cancer using serum/plasma and tissue, which has principally used NMR and MS platforms.16−21 These studies mainly focused on the discrimination of and biomarker discovery for esophageal cancer, usually based on small-scale samples rather than lymph node metastasis, which is a simple and important prognostic factor in esophageal cancer. Hydrophilic metabolites, including those involved in glycolysis, tricarboxylic acid cycle, fatty acid metabolism, and amino acid metabolism, are closely correlated with carcinogenesis at the level of metabolism. Gas chromatography coupled to mass spectrometry (GC/MS), together with prior chemical derivatization of samples, is a powerful metabolomics tool with holistic superiority in chromatographic retention, structural qualification, intensity, and quantification to comprehensively evaluate these hydrophilic metabolites.19,22,23 In this study, a non-targeted metabolomics approach based on GC/MS in conjunction with univariate and multivariate statistical analyses was performed to comprehensively determine the metabolic alterations between healthy controls and ESCC patients, particularly focusing on those alterations present in patients with lymph node metastasis, with the aim of identifying potential small-molecule biomarkers of metastasis for early diagnosis, staging, and prognostic prediction and improving understanding of the underlying mechanisms of ESCC. To our knowledge, this research is the first metabolomics report of ESCC from the perspective of lymph node metastasis.

2.3. Sample Preparation and GC/MS Analysis

Serum samples stored at −80 °C were thawed and vortexed for 5 s at room temperature. Twenty microliters of serum sample was added to an Eppendorf tube with 10 μL of internal standard (0.1 mg/mL [U-13C6]-leucine), and the tube was vortexed for 5 s. Subsequently, 90 μL of ice-cold methanol− chloroform (3:1) was added, and the mixture was vortexed for 30 s, placed at −20 °C for 20 min, and centrifuged at 16 000g at 4 °C for 15 min. A quality control sample was pooled from representative serum samples of ESCC patients and healthy controls, and three QC samples were prepared and analyzed with the same procedure as that for the experiment samples in each batch. Ninety microliters of supernatant in a glass vial was dried under a gentle nitrogen stream, and 30 μL of 20 mg/mL methoxylamine hydrochloride in pyridine was subsequently added. The resultant mixture was vortexed vigorously for 30 s and incubated at 37 °C for 90 min. Thirty microliters of BSTFA (with 1% TMCS) was added into the mixture, which was derivatized at 70 °C for 60 min prior to injection. At the same time, a blank derivatization sample (using deionized water instead of a serum sample) was prepared in order to remove the background noise produced during sample preparation and GC/MS analysis. The derivatized serum samples were analyzed on an Agilent 7890A gas chromatography system coupled to an Agilent 5975C MSD system with inert Triple-Axis Detector (Agilent, CA). A HP-5MS fused-silica capillary column (30 m × 0.25 mm × 0.25 μm, Agilent J&W Scientific, Folsom, CA, USA) was utilized to separate the derivatives. Helium was used as the carrier gas at a constant flow rate of 1 mL/min through the column. The injection volume was 1 μL, and the solvent delay time was set to 5 min. The initial oven temperature was held at 80 °C for 2 min, ramped to 300 °C at a rate of 10 °C/min, and finally held at 300 °C for 6 min. The temperature of the injector, transfer line, and ion source (electron impact) was set to 250, 290, and 230 °C, respectively. The collision energy was 70 eV. Mass data was acquired in a full-scan mode (m/z 50− 600). The samples were analyzed in a random sequence.

2. MATERIALS AND METHODS 2.1. Chemicals

N,O-Bis(trimethylsilyl)trifluoroacetamide (BSTFA) with 1% trimethylchlorosilane (TMCS) was from Regis (Morton Grove, IL, USA). High-performance liquid chromatography grade methanol and chloroform were purchased from CNW Technologies GmbH (Düsseldorf, Germany), and all standard compounds, methoxylamine hydrochloride, and anhydrous pyridine were purchased from Sigma-Aldrich. 2.2. Sample Collection

This study was approved by the ethics committee at Changhai Hospital, which is affiliated with Second Military Medical University (Shanghai, China). All subjects included in the study provided written informed consent in accordance with institutional guidelines. The ESCC patients enrolled in this study did not receive any neoadjuvant chemotherapy or radiation therapy prior to sample collection. Fasting blood samples were collected from ESCC patients that were clinically proven not to have lymph node metastasis (n = 40, 29M/11F) or to have metastasis (n = 40, 35M/5F) by TNM staging (2009) at Changhai Hospital (Shanghai, China) in the morning. There was no significant difference in the average B



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Figure 1. Representative serum GC/MS total ion current (TIC) chromatograms, scores plots, and permutation test: (A) healthy control TIC, (B) non-metastasis ESCC patient TIC, (C) metastasis ESCC patient TIC, (D) PCA scores plot, (E) PLS-DA scores plot, and (F) plot of the permutation test of PLS-DA on non-metastatic ESCC patients, metastatic ESCC patients, and healthy subjects.

2.4. Data Preprocessing and Statistical Analysis

distribution was abnormal) on the normalized peak areas, where metabolites with VIP values larger than 1.0 and p values less than 0.05 were included, respectively. Fold change was calculated as a binary logarithm of the average normalized peak area ratio between the two groups. The software SPSS (version 19.0, IBM) was used to perform variable selection analysis of potential biomarkers, binary logistic regression analysis, and receiver operating characteristic (ROC) analysis.

The acquired GC/MS data were first preprocessed as described in a previous publication.24 The resulting data were normalized to the total peak area of each sample in Excel 2007 (Microsoft, USA) and imported into a SIMCA-P (version 11.0, Umetrics, Umeå, Sweden), where principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and orthogonal partial least-squares discriminant analysis (OPLSDA) were performed. All data were mean-centered and unit variance (UV)-scaled in SIMCA-P. The Hotelling’s T2 region, shown as an ellipse in score plots of the models, defines the 95% confidence interval of the modeled variation. The quality of the models is described by the R2X or R2Y and Q2 values. R2X or R2Y is defined as the proportion of variance in the data explained by the models and indicates goodness of fit. Q2 is defined as the proportion of variance in the data predicted by the model and indicates predictability, calculated by a crossvalidation procedure. A default seven-round cross-validation in SIMCA-P was performed throughout to determine the optimal number of principal components and to avoid model overfitting. The PLS-DA models were also validated by a permutation analysis (200 times). The differential metabolites were selected on the basis of the combination of a statistically significant threshold of variable influence on projection (VIP) values obtained from the OPLSDA model and p values from a two-tailed Student’s t test (if the distribution was normal) or Mann−Whitney U test (if the

2.5. Structural Identification of Metabolites

To obtain reliable results of a compound’s structure, several representative raw GC/MS data files (if the abundance of one compound was too low in one data file, then another data file with relatively high abundance was utilized) were imported into AMDIS software to automatically search against an authorconstructed standard library including retention time and mass spectra, Agilent Fiehn GC/MS Metabolomics RTL library and Golm Metabolome Database, respectively. The other undetermined data were directed to search against the NIST 11 Mass Spectral Library. The metabolites were structurally confirmed by automatically comparing them against the mass fragments and retention times in the author-constructed standard library or the mass fragments and Kovats retention indexes in other databases.

3. RESULTS In this research, the ESCC patients were classified into two subgroups, i.e., a lymph node non-metastatic ESCC group and C



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Table 2. Differential Serum Metabolites among Healthy Subjects, Non-metastatic ESCC Patients, and Metastatic ESCC Patients non-metastatic/healthy no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

metabolitesa S,N

Glucose AlanineS,N Lactic acidS,N GlutamineS Citric acidS Fumaric acidS 1,5-AnhydroglucitolN ValineS,N 2-Ketoisovaleric acidN 2-Ketoisocaproic acidN 3-Methyl-2-oxovaleric acid TryptophanS Indolelactic acidS Iminodiacetic acid PhosphoethanolamineS γ-Aminobutyric acidS,N Glycolic acidS CysteineS Methylcysteine α-TocopherolS γ-TocopherolS ThreonineS,N 2-Hydroxybutyric acidS HypotaurineS Aspartic acidS β-alanineS Uric acidS,N 3-Hydroxybutyric acidS Myristic acidS, N Palmitic acidS,N Oleic acicS,N Linoleic acidS Palmitelaidic acid 1-Monooleoylglycerol ErythritolN InositolS,N myo-Inositol 1-phosphate RiboseS MaltoseS LactoseS CreatinineS CholesterolS HydroxylamineS,N

VIP valueb 0.95 1.42 1.66 0.87 1.86 1.68 1.28 0.81 1.48 1.72 1.67 1.39 1.77 1.03 1.39 0.86 1.48 1.75 1.26 1.48 1.27 1.08 1.72 0.94 1.31 1.86 1.31 1.07 1.15 1.00 0.99 1.31 1.18 1.14 1.53 1.22 1.09 1.41 1.36 1.01 1.08 1.78 1.03

p valuec 2.76 4.32 2.38 3.51 1.34 1.36 2.23 2.02 5.20 2.61 2.94 4.03 3.01 1.25 6.14 1.06 3.91 1.59 1.40 1.14 6.86 2.52 4.95 6.60 9.93 1.07 1.14 6.98 5.59 1.19 1.37 3.42 3.10 8.02 8.21 1.06 1.35 7.84 1.56 6.35 1.66 6.91 6.92

× × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × ×

−4

10 10−9 10−13 10−3 10−11 10−10 10−7 10−3 10−10 10−13 10−10 10−7 10−11 10−3 10−6 10−3 10−8 10−10 10−8 10−8 10−6 10−5 10−12 10−5 10−11 10−12 10−7 10−9 10−6 10−4 10−4 10−8 10−6 10−11 10−11 10−6 10−4 10−11 10−5 10−12 10−7 10−12 10−5

metastatic/healthy FCd −0.38 −0.50 0.75 −0.72 −2.01 −0.98 −0.65 −0.17 −0.70 −0.89 −0.83 −1.17 −1.16 −0.92 −1.28 −0.27 −0.49 −1.75 −1.28 −1.75 −1.16 −0.39 1.52 0.92 1.89 4.64 −0.62 2.36 0.73 0.38 0.52 1.04 0.79 2.97 −0.52 −0.48 −0.65 1.66 4.43 5.14 1.04 −0.79 −0.50

VIP value 1.05 1.40 1.51 1.14 1.42 1.55 1.35 1.08 1.47 1.59 1.59 1.35 1.56 1.17 1.22 1.42 1.46 1.14 1.26 1.17 1.01 0.86 1.42 1.04 1.26 1.69 1.31 0.95 1.11 1.11 1.25 1.31 0.97 0.90 1.23 1.04 0.98 0.91 1.18 0.92 0.45 1.65 1.16

p value 1.40 2.51 3.57 2.76 3.01 1.85 3.18 6.37 6.57 1.10 6.91 4.03 2.36 8.68 5.80 1.07 1.26 1.06 1.85 1.14 3.77 1.24 2.34 1.30 1.23 1.07 1.97 6.67 3.16 2.98 1.45 1.17 4.47 8.67 5.86 2.19 6.03 3.91 3.11 2.56 2.29 1.28 6.07

× × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × × ×

FC −6

10 10−12 10−15 10−6 10−11 10−11 10−11 10−7 10−14 10−17 10−12 10−9 10−11 10−8 10−6 10−12 10−10 10−8 10−10 10−6 10−5 10−4 10−10 10−6 10−9 10−12 10−10 10−8 10−7 10−7 10−8 10−10 10−8 10−12 10−9 10−6 10−5 10−8 10−5 10−9 10−3 10−12 10−8

−0.63 −0.62 0.92 −1.38 −1.83 −1.09 −0.92 −0.31 −0.85 −1.11 −1.04 −1.62 −1.19 −1.56 −1.30 −0.63 −0.69 −1.27 −1.56 −1.32 −1.00 −0.32 1.19 1.11 2.24 4.91 −0.77 2.25 0.83 0.57 0.79 1.32 1.24 3.52 −0.48 −0.44 −0.67 1.48 4.99 5.05 0.71 −0.98 −0.64

a The metabolites marked with “S” were structurally identified by reference standards, and those in italic type were identified as differential metabolites that are common to both non-metastatic/healthy and metastatic/healthy. bVariable importance in the projection (VIP) was obtained from the OPLS-DA model. cThe p value was calculated from Student’s t test (metabolites with normal distribution are marked as “N”) or nonparametric test Mann−Whitney U test (metabolites with abnormal distribution without marks). dFold change was calculated as a binary logarithm of the average mass response (normalized peak area) ratio between non-metastatic vs healthy group or between the metastatic vs healthy group, where a positive value means that the average mass response of the metabolite in the non-metastatic or metastatic group is larger than that in the healthy group.

a lymph node metastatic ESCC group. GC/MS-based metabolomics was conducted to profile the serum metabolome of ESCC patients and healthy controls. Representative total ion current (TIC) chromatographs from healthy subjects, nonmetastatic ESCC patients, and metastatic ESCC patients are shown in Figure 1A−C, respectively. As an unsupervised multivariate statistical model, PCA was performed to illustrate the metabolic differences among healthy subjects, non-

metastatic ESCC patients, and metastatic ESCC patients (6 PCs, R2X = 0.571, Q2 = 0.456). The PCA scores plot (Figure 1D) shows that the ESCC patients and healthy subjects are distributed in two separate areas, indicating a markedly different serum metabolome between ESCC patients and healthy subjects. However, we did not observe a difference between the non-metastatic and metastatic ESCC patients in the PCA scores plot (Figure 1D). Furthermore, we performed PLS-DA D



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Figure 2. Discriminating plots of non-metastatic and metastatic ESCC patients: (A) scores plot of the PLS-DA model, (B) plot of the permutation test (200 times) of the PLS-DA model, (C) scores plot of the OPLS-DA model, and (D) scores plot of the prediction analysis of the OPLS-DA model.

(9 PCs, R2X = 0.596, R2Y = 0.951, Q2 = 0.585) and found a dramatic difference between the ESCC patients and healthy subjects as well as an obvious trend of profile separation between the non-metastatic and metastatic ESCC patients (Figure 1E). Model validation by permutation test on ESCC patients and healthy subjects assured that this PLS-DA model was not random and overfitting and thus was reliable for explaining and predicting the variations (Figure 1F). To identify the metabolites that are mainly contributing to the metabolomic distinctions, we carried out two separate supervised models (OPLS-DA) to distinguish between the nonmetastatic ESCC patients and healthy volunteers and between metastatic ESCC patients and healthy volunteers. Supporting Information Figure S1A (1 PC, R2Y = 0.922, Q2 = 0.911) and S1B (1 PC, R2Y = 0.928, Q2 = 0.922) demonstrates significant differences between the ESCC patients and healthy subjects in the OPLS-DA scores plot by the first principal component. Using a combination of the VIP values (>1) from OPLS-DA with results from Student’s t test (normal distribution, p < 0.05) or Mann−Whitney U test (abnormal distribution, p < 0.05), 37 and 36 metabolites were identified as differential variables for non-metastatic and metastatic ESCC patients, respectively (Table 2). Among them, 29 differential metabolites were shared as being potentially characteristic of both non-metastatic and metastatic ESCC patients. If simply considering statistical significance, then more differential metabolites can be included (Table S2), which will improve the systematic understanding of the pathogenesis of this cancer. Metabolites that were significantly changed in patients included increased serum lactic acid and fatty acids, whereas those that were decreased included glucose, glutamine, and TCA cycle intermediates (citric acid, fumaric acid), which is a common metabolic signature of many cancers. Importantly,

several catabolic products (2-ketoisovaleric acid, 2-ketoisocaproic acid, and 3-methyl-2-oxovaleric acid) of branched-chain amino acids (BCAAs) were significantly decreased in ESCC patients. Metabolites relating to oxidative stress, such as cysteine, methylcysteine, pyrogallol, tocopherol (α- and γ-), were also decreased markedly in ESCC patients. Additionally, serum tryptophan, indolelactic acid, uric acid, p-cresol, phosphoethanolamine, and cholesterol were significantly decreased in patients, whereas 2-hydroxybutyric acid, aspartic acid, β-alanine, and maltose were markedly elevated; these abnormal changes in serum metabolite concentration not only reflect alterations in the metabolic phenotype but also could provide insight into the underlying metabolism of ESCC. Further investigation was conducted to discriminate between non-metastatic and metastatic ESCC patients in order to characterize metabolic profiles and to identify potential biomarkers of lymph node metastatic esophageal cancer (i.e., poor prognosis). The scores plot of PLS-DA (2PCs, R2Y = 0.724, Q2 = 0.545) showed a clear separation trend between non-metastatic and metastatic ESCC patients. The permutation test indicates that the current PLS-DA model is a reliable model for explaining and predicting the observed variations (Figure 2B). We further performed OPLS-DA to discriminate the two groups of patients, and, resultantly, the two groups of patients were mainly distributed at the two sides of PC1 in the scores plot, with only two samples being misclassified. Also, each class of non-metastatic patients or metastatic patients was randomly classified into a training set (n = 30) and a validation set (n = 10) to visually evaluate the prediction accuracy of the OPLSDA model. There was no significant difference in the mean age values of the two subgroups (non-metastasis: training set 61.3 vs validation set 57.7, p = 0.224; metastasis: training set 63.9 vs validation set 59, p = 0.284). Figure 2D demonstrates that 2 out E



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Table 3. Differential Metabolites and Their Sensitivities and Specificities for Discrimination between Metastatic and Nonmetastatic ESCC Patients no.

metabolitesa

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

S,N

Lactic acid GlutamineS LeucineS,N ValineS,N Palmitic acidS,N Oleic acidS,N Palmitoleic acid Pyrrole-2-carboxylic acid CystineS PyrogallolS 2-Hydroxybutyric acidS α-TocopherolS Iminodiacetic acid TryptophanS CholesterolS γ-Aminobutyric acidS,N Glutamic acidS Glycolic acidS

VIP valueb 1.04 1.2 1.26 1.28 1.13 1.33 1.29 1.99 1.55 1.37 1.33 1.05 1.66 1.13 1.13 1.88 1.08 1.46

p valuec 2.29 8.14 5.73 4.99 1.37 6.10 9.37 5.58 4.90 1.23 5.92 2.09 4.61 5.52 3.04 1.82 2.97 1.76

× × × × × × × × × × × × × × × × × ×

−2

10 10−3 10−3 10−3 10−2 10−3 10−3 10−6 10−3 10−4 10−3 10−2 10−4 10−3 10−2 10−5 10−2 10−3

FCd

AUCe

sensitivity (%)

.

0.17 −0.66 −0.16 −0.15 0.19 0.27 0.56 −1.04 −0.99 −0.74 −0.33 0.43 −0.64 −0.46 −0.19 −0.36 0.44 −0.2

0.64 (0.52−0.76) 0.67 (0.55−0.79) 0.69 (0.57−0.80) 0.71 (0.60−0.83) 0.61 (0.49−0.74) 0.68 (0.56−0.80) 0.67(0.55−0.79) 0.80 (0.70−0.89) 0.70 (0.57−0.82) 0.75 (0.64−0.86) 0.68 (0.56−0.80) 0.65 (0.53−0.77) 0.73 (0.61−0.84) 0.68 (0.56−0.80) 0.64 (0.52−0.76) 0.74 (0.64−0.85) 0.64 (0.52−0.76) 0.70 (0.59−0.82)

77.5 42.5 62.5 70.0 75.0 83.3 50.0 87.5 78.1 60.0 47.5 55.0 80.0 61.5 90.0 97.5 67.5 50.0

52.5 82.5 72.5 67.5 40.0 55.0 82.5 62.5 58.3 82.5 80.0 75.0 67.5 72.5 37.5 42.5 65.0 90.0

a The metabolites marked with “S” were structurally identified by reference standards, and those in italic type were subjected to variable selection analysis prior to a binary logistic regression analysis. bVariable importance in the projection (VIP) was obtained from the OPLS-DA model. cThe p value was calculated from Student’s t test (metabolites with normal distribution are marked as “N”) or nonparametric Mann−Whitney U test (metabolites with abnormal distribution without marks). dFold change was calculated as a binary logarithm of the average normalized peak area ratio between the metastasis group vs non-metastasis group, where a positive value indicates that the average normalized peak area ratio in the metastasis group is larger than that in the non-metastasis group. eArea under the receiver operating characteristic (ROC) curve, with the 95% confidence interval (CI) range in parentheses.

metabolites. The AUC, sensitivity, and specificity of each metabolite were produced and are listed in Table 2. As a result, five metabolites, pyrrole-2-carboxylic acid, iminodiacetic acid, γaminobutyric acid (GABA), cholesterol, and oleic acid, displayed relatively high sensitivities (more than 80%), whereas another five metabolites, glutamine, pyrogallol, glycolic acid, 2hydroxybutyric acid, and palmitoleic acid, demonstrated specificities of more than 80%. However, none of the metabolites showed both high sensitivity and specificity, making it necessary to apply multiple serum metabolites in the diagnosis of metastatic ESCC patients out of all ESCC patients. Complex diseases generally contain systematic dysregulation of multiple metabolic pathways; hence, a panel of biomarkers, rather than one biomarker, will have more power to diagnose and provide overall clinical information. A binary logistic regression model involving multiple differential metabolites was examined. With ESCC progression (from healthy subjects to non-metastatic patients and thereafter to metastatic patients), 15 metabolites showed progressive elevation or a declining trend (Figure 3) and thus were further studied. They could be assigned to five groups with different functions, i.e., cancer cell proliferation, apoptosis, immune escape, migration, and oxidative stress (Figure 3), which will be discussed below. These metabolites were subjected to a stepwise variable selection method (Forward Wald) based on the samples in the training set utilized in the above OPLS-DA model; as a result, three metabolites, i.e., valine, GABA, and pyrrole-2-carboxylic acid, were selected to construct a binary logistic regression model on the training set samples of nonmetastatic and metastatic ESCC patients. These three metabolites did not show multicollinearity (data not shown), and their variations among three groups are shown in Figure 4A. The prediction model is as follows: P = 1/[1 +

of 20 samples are wrongly assigned, implying that 90% of the samples are predicted correctly. This research proves that lymph node metastasis can be used as a classifying factor to stage ESCC patients into two subgroups with different prognostic effects. Likewise, 18 serum metabolites were identified as potential marker metabolites for use in discriminating between nonmetastatic and metastatic ESCC patients. In contrast with that for the non-metastasis group, a majority of amino acids and their derivatives (glutamine, cystine, tryptophan, γ-aminobutyric acid, valine, leucine, and pyrrole-2-carboxylic acid) were decreased in the metastasis group, whereas glutamic acid was markedly increased. Lactic acid was significantly increased in the metastasis group, suggesting stronger aerobic glycolysis in metastatic ESCC patients compared to that in nonmetastatic ESCC patients. Long chain fatty acids (palmitic acid, oleic acid, and palmitoleic acid) were entirely increased significantly in the metastasis group when compared with that of the non-metastasis group. The marked elevation of lactic acid and long chain fatty acids is suggestive of the commonly progressive metabolic phenotype of many cancers. Pyrrol-2carboxylic acid and cystine are two special metabolites that are markedly reduced only in the metastasis group, indicating their potential as characteristic metabolites of metastatic ESCC patients. These differential metabolites were mainly related to cancer cell proliferation, apoptosis, migration, immune escape, matrix degradation, and oxidative stress (Table 3). Collectively, the 18 differentially abundant metabolites help to facilitate an understanding of the mechanism of lymph node metastasis in esophageal cancer. To evaluate the capability of the differential metabolites to discriminate between lymph node non-metastatic (good prognosis) and metastatic (poor prognosis) ESCC patients, ROC curves were produced individually for these 18 F



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4.1. Cancer Cell Proliferation

4.1.1. Dysregulation of Energy Metabolism. In contrast to normal cells, cancer cells are characterized by increased aerobic glycolysis, fatty acid biosynthesis, and glutaminolysis for proliferation.25,26 Most cancer cells produce energy through the glycolytic breakdown of glucose rather than mitochondrial oxidative phosphorylation via the TCA cycle, even in the presence of an adequate oxygen supply, which is known as the Warburg effect.27,28 Excessive depletion of glucose by cancer cells would theoretically lead to a deficiency of available blood glucose for normal cells. The markedly elevated blood lactic acid and decreased glucose and glutamine observed in this study are metabolic signatures of cancers. The significantly increased serum lactic acid and decreased glucose demonstrated that ESCC cells functioned with strong aerobic glycolysis (Warburg effect). To meet energetic and biosynthetic requirements, cancer cells, in turn, increased aerobic glycolysis and lactic acid excretion. In this study, the significantly higher abundance of serum lactic acid in metastatic ESCC patients compared to that in non-metastatic ESCC patients suggested a potential relationship between lactic acid and metastasis of ESCC, which is supported by the fact that lactic acid level is a strong indicator of poor prognosis in many types of cancers.28 The excessive lactic acid probably created an acidic microenvironment, resulting in immune protection, acid-mediated matrix degradation, invasiveness, and metastasis of cancer cells.26,28,29 Additionally, 1,5-anhydroglucitol (1,5-AG), a metabolically inert polyol mainly from food, is a validated marker of shortterm glycemic control and is rapidly depleted as blood glucose levels exceed the renal threshold for glucosuria.30 The simultaneously decreased blood glucose and 1,5-AG probably suggest the presence of sustained glucose depletion from blood or decreased food intake of the patients. Such tentative dual effects might cause a serious deficiency in blood glucose. Our results are also supported by previous reports based on blood and tumor tissue of esophageal cancer patients by NMR and LC−MS metabolomics.17,18 Additionally, we detected reduced pyruvic acid, but without statistical significance (data not shown), as well as significantly decreased citric acid, succinic acid, fumaric acid, and alanine in ESCC patients compared to their levels in healthy subjects (Tables 2 and S2). Such decreases could be a synthetic result of both the disordered energy metabolism of cancer cells and the reduced energy metabolism of normal cells with inadequate glucose supply (Figure 5). Therefore, the present work demonstrates that the dysfunction of energy metabolism is closely correlated with the carcinogenesis and metastasis of ESCC. 4.1.2. De Novo Fatty Acid Biosynthesis. Proliferating cancer cells require a massive amount of building blocks such as protein (amino acids), lipids (fatty acids), and nucleic acids (purine and pyrimidine). It is well-known that most normal human cells prefer exogenous fatty acid sources for use as building blocks, whereas tumor cells seem to prefer de novo synthesis of fatty acids.31 Previous studies demonstrated that fatty acid synthase overexpression was associated with esophageal tumorigenesis as well as with the progression, aggressiveness, and metastasis of other cancers.32−34 This study observed significantly enhanced serum long chain fatty acids in ESCC patients (Tables 2 and S2), indicating that the increased abundance of serum long chain fatty acids was probably the results of strong de novo fatty acid synthesis during proliferation and metastasis of esophageal cancer cells. A

Figure 3. Heatmap and function classification of 15 differential metabolites between non-metastatic and metastatic ESCC patients. Heatmap (left) was produced by average normalized peak areas with zscore scaling of healthy controls (C), non-metastatic ESCC patients (E), and metastatic ESCC patients (M). These metabolites showed progressive elevation or decline with the progression of ESCC (from C to E to M). In total, 13 metabolites were assigned to five function groups (right), except for iminodiacetic acid and glycolic acid. The green background indicates that the function is improved in metastatic ESCC patients because of the metabolites with changed levels (left) compared to that in non-metastatic ESCC patients, whereas the functions classified with a red background represent the opposite of this.

exp(−(29.446 − 0.47 × (valine) − 80.226 × (GABA) − 43.931 × (pyrrole-2-carboxylic acid)))]. The prediction model obtained 90 and 85% accuracies for the training set (60 samples) and validation set (20 samples), respectively. We further conducted two ROC analyses on the generated logistic regression model to obtain the diagnostic values of the biomarker panel. The results showed that a panel of three metabolites provided an AUC of 0.964 (90% sensitivity and 96.67% specificity) and 0.91 (90% both for sensitivity and specificity) for the training and validation sets, respectively (Figure 4B). On the basis of the highest prediction sensitivity (90%) and specificity (96.67%) of the ROC curves on the training set, an optimal cutoff value of 0.558 was obtained. On the basis of this cutoff value, it was found that 56 out of 60 samples (93.3%) in the training set as well as 17 out of 20 samples (85%) in test set could be accurately predicted (Figure 4C). These results demonstrate that lymph node metastatic and non-metastatic esophageal cancer patients can be welldiscriminated with high accuracy based on the combination of valine, GABA, and pyrrole-2-carboxylic acid. Future work will involve a larger scale of samples or independent serum samples to validate these results.

4. DISCUSSION Lymph node metastasis is the most important prognostic factor for esophageal cancer. The present research conducted a metabolomics profiling to uncover systematic metabolic variations related to lymph node metastasis of ESCC. Serum metabolomics profiling could discriminate the esophageal cancer patients according to lymph node metastasis. We identified a panel of potential biomarkers, with their combined use potentially serving as a diagnostic factor for ESCC metastasis, and we proposed potential metabolic dysfunction in ESCC and metastatic ESCC, including malignant proliferation, oxidative stress, migration, immune escape, and apoptosis of ESCC cells (Figure 5). G



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Figure 4. Box plots of serum valine, γ-aminobutyric acid, and pyrrole-2-carboxylic acid, ROC curves based on the binary logistic regression model by the combination of three serum metabolites, and their prediction plots based on the optimal cutoff value from ROC curves. (A) Values in the box plots are shown as the normalized peak areas of the metabolites in healthy subjects (healthy, green), non-metastatic patients (ESCC, blue), and metastatic patients (mESCC, red). (B) The ESCC samples from the training set were applied to construct a binary logistic regression model based on the combination of serum valine, γ-aminobutyric acid, and pyrrole-2-carboxylic acid, and the ROC curves of the training set (B, left) and test set (B, right) were obtained from the above established prediction model. (C) The optimal cutoff value with the highest sensitivity and specificity in the ROC curves of the training set was obtained (0.558) and applied to evaluate the prediction capacity (85 and 93.3% for test set and training set, respectively) of the current model, where 0 and 1 on the x axis represent ESCC and mESCC patients, respectively, and red diamonds represent samples.

NMR-based metabolomics study proved that polyunsaturated fatty acids and unsaturated fatty acids are significantly upregulated in esophageal cancer tissue.18 Interestingly, we also found that serum fatty acids, such as palmitic acid, oleic acid, and palmitoleic acid, were significantly higher in the metastasis group compared to that in the non-metastasis group. Therefore, long chain fatty acids are crucial serum metabolites that serve as a metabolic hallmark of tumorigenesis and metastasis of ESCC. 4.1.3. Glutaminolysis. Glutamine is the most abundant serum amino acid and plays a unique role in the metabolism of proliferating cells and tumor cells. Our work demonstrated that

serum glutamine is decreased in the non-metastasis group and is progressively reduced in the metastasis group. Glutaminolysis is an alternative source of energy production via the TCA cycle, especially when glycolytic energy production is low in tumor cells. Increased glutaminolysis is a key metabolic feature of cancer cells.26 Recent works have also revealed that glutamine supports de novo fatty acid synthesis by reductive glutamine metabolism.35 Glutamine also serves as a nitrogen source.26 The amino group from glutamine contributes to multiple biosynthetic pathways, including synthesis of other nonessential amino acids and nucleotides. In particular, glutamine, as an obligate nitrogen donor in nucleotide biosynthesis, has been H



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Figure 5. Proposed mechanisms for the lymph node metastases of esophageal squamous cell carcinoma. Cancer cells excessively utilize blood glucose to support aerobic glycolysis, and, resultantly, the input of glucose to normal cells is significantly decreased. Branched-chain amino acids are transferred into cancer cells to supply an amino group to form glutamine and subsequent glutaminolysis, whereas fewer BCAAs are transferred into normal cells, which releases fewer catabolic products. Increased oxidative stress (ROS) causes a decrease in the production of antioxidants such as cystine, cysteine, methylcysteine, tocopherol, and other metabolites. Red dashed arrows represent suppression, for example, decreased pyrogallol and/or phosphoethanolamine attenuate the inhibition of cancer cell cycle arrest and apoptosis; green dashed arrows represent promotion, for example, decreased glutamine, tryptophan, or increased lactic acid improves immune escape of cancer cells. Red solid arrows represent metabolites that were excreted with significantly elevated levels, whereas blue solid arrows indicate metabolites that were released into the blood flow in reduced amounts. ROS, reactive oxygen species. GSH, reduced glutathione. CD44, a cell-surface glycoprotein involved in cell−cell interactions, cell adhesion, and migration.

with that in healthy subjects. This study suggests that BCAAs provide a nitrogen source (such as to glutamine) for cancer cell proliferation, and the decrease of serum catabolic metabolites of BCAAs could be a new metabolic indicator of ESCC.

implicated in the ongoing support of cancer cell proliferation. An excessive glutamine requirement by cancer cells would result in the targeting of glutamine to tumor tissues and consequently a decrease of blood glutamine. Our results suggest that serum glutamine was utilized as alternative energy, carbon, and nitrogen sources to support the proliferation of malignant esophageal cancer cells. As a result, serum glutamine was significantly decreased in the non-metastasis group and was progressively reduced in the metastasis group. 4.1.4. Branched-Chain Amino Acids. Branched-chain amino acids (BCAAs, including valine, leucine, and isoleucine) are essential amino acids acting as nitrogen donors for the major nitrogen carriers, glutamine and alanine. BCAAs are largely transferred into cancer tissues to provide amino groups such as to form glutamine,36 probably resulting in lowered supplies of BCAAs to normal cells and subsequently reduced excretion of their catabolic metabolites to the blood. We observed significantly decreased serum valine only in ESCC patients, probably because of the complicated origin of BCAAs both from diet and breakdown of skeletal muscle protein, but their catabolic metabolites (2-ketoisovaleric acid, 2-ketoisocaproic acid, and 3-methyl-2-oxovaleric acid) were significantly decreased in the serum samples of ESCC patients compared

4.2. Increased Oxidative Stress

Many types of cancer cells have increased levels of reactive oxidative species (ROS), generated by a defective mitochondrial electron-transport chain.37 ROS are normally cleared through the synergistic action of antioxidant enzymes and antioxidants such as reduced glutathione (GSH), biosynthesized from glutamic acid, cysteine, and glycine. Although GSH, in this study, was not detected due to methodological limitations, the dramatically decreased serum cysteine and its precursors such as cystine and methionine suggested an increased requirement for antioxidants against elevated ROS in ESCC. Epidemiological studies have indicated that higher plasma or serum concentrations of cysteine are significantly associated with a lower risk of breast cancer,38 ESCC, and gastric cardia adenocarcinomas.39 Additionally, as a natural antioxidant of thiol-methylation of cysteine, mainly from garlic and onion, methylcysteine shows a chemopreventive effect on liver carcinogenesis.40 The decreased methylcystiene concenI



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indicates that GABA and glutamic acid are probably good serum biomarkers for the diagnosis of ESCC metastasis. Cholesterol is an essential component of mammalian cell membranes as well as a precursor of bile acids and steroid hormones. So far, it is well-known that cancer cells need excess cholesterol and intermediates of the cholesterol biosynthesis pathway to maintain a high level of proliferation and that cholesterol is lower in the blood of cancer patients and higher in membranes of mammary tumor cells.50,51 In this study, the marked depletion of serum cholesterol in esophageal cancer patients could be due to the excessive cholesterol requirements of the cancer cells because of their high level of proliferation and relatively insufficient output of de novo cholesterol synthesis.52 The metastatic phenotype of cancer cells is mediated by signaling mechanisms that decrease cell adhesion and promote cell migration. Antalis et al. reported that excess cholesterol utilization was perhaps an important feature of breast cancer metastasis.53 Further research proposed that cholesterol (membrane or circulating levels) might affect the progression of metastasis by dissociating CD44, an adhesion molecule expressed in cancer cells, from lipid rafts.54 In this study, the progressive decrease of serum cholesterol in metastatic esophageal cancer patients might suggest that serum cholesterol was largely transferred into cell membranes to dissociate CD44 and improve cancer cell migration. Serum tryptophan was progressively lower in metastatic esophageal cancer patients compared to that in healthy subjects. Previous studies showed that enhanced tryptophan catabolic metabolism is associated with both decreased proliferation of T cells and a reduced immune response mediated by the enzyme indoleamine-2,3-dioxygenase (IDO), an enzyme catalyzing tryptophan to produce kynurenine.55,56 We could not detect serum kynurenine, but we found lower levels of indolelactic acid (Table 2), a catabolic product of tryptophan through another pathway. This phenomenon may be explained by the input of tryptophan to normal cells being significantly reduced. In spite of that, the decrease of serum tryptophan in this study possibly suggests a strong tryptophan catabolism and an improved cancer cell immune escape in ESCC. Additionally, glutamine is essential for proper immune function and therefore the depletion of serum glutamine observed in this study also contributed to the suppression of immune cell function. Thus, decreased serum tryptophan and glutamine, as well as the aforementioned lactic acid, would decrease the immune response toward cancer cells, improving the survival of esophageal cancer cells.

tration was reportedly the product of increased ROS in ESCC. Cystine is the preferred form of cysteine for the synthesis of glutathione in cells of the immune system as well as those of skeletal and connective tissues. The persistently decreased cystine concentrations during ESCC progression suggests a progressive oxidative stress as well as its potential use as an indicator for the proliferation and metastasis of ESCC. We also detected significantly decreased serum tocopherol (α-, β-, and γ-) in ESCC patients. α-Tocopherol is traditionally recognized as the most active form of vitamin E in humans and is a powerful biological antioxidant. Epidemiological studies suggested that a low vitamin E status was associated with increased cancer risk.41 Evidence from China suggested that a combination of β-carotene, α-tocopherol, and selenium may protect against esophageal cancer.42 Another nutrition study found that higher levels of vitamin E in the diet were negatively associated with ESCC,43 and the supplementation with αtocopherol inhibited the development of esophageal adenocarcinoma in a rat model.44 We suggest that the decreased serum tocopherol observed in this study probably reflects increased oxidative stress in ESCC patients. 4.3. Suppression of Esophageal Cancer Cell Apoptosis

Pyrogallol, a polyphenol compound derived from green tea or garlic, is known as a superoxide anion generator and glutathione depletor. Recent studies showed that pyrogallol induced the apoptosis of many types of cancer cells, such as gastric cancer cells and HeLa cells, via inducing cell cycle arrest as well as triggering apoptosis.45 Phosphoethanolamine is a compound involved in phospholipid turnover, acting as a substrate for many phospholipids of cell membranes, especially phosphatidylcholine. Increasing evidence has demonstrated that phosphoethanolamine acts to inhibit the growth and metastasis of all tumor cell lines without affecting normal cells by inducing cell cycle arrest and apoptosis through the mitochondrial pathway. 46,47 Decreased levels of serum pyrogallol and phosphoethanolamine suggest a favorable condition for attenuating ESCC cell apoptosis. Pyrogallol, with a markedly lower level in the metastasis group than that in the non-metastasis group, could be used as a potential biomarker for ESCC metastasis. 4.4. Cancer Cell Migration and Immune Escape

Neurotransmitters are important initiators of migratory activity. It is reported that GABA, a principal inhibitory neurotransmitter, also contributes to the proliferation, differentiation, and migration of several kinds of cells, including cancer cells.48 Recent work reported that GABA inhibited human liver cancer cell migration and invasion via the ionotropic GABAA receptor as a result of the induction of liver cancer cell cytoskeletal reorganization, and pretreatment with GABA also significantly reduced intrahepatic liver metastasis and primary tumor formation in vivo. In this study, the downregulated serum GABA presumably attenuated its inhibition effect on tumor cell migration and consequently favored metastasis. GABA is predominantly synthesized from glutamic acid by glutamate decarboxylase. It was reported that increased levels of glutamic acid improved pancreatic cancer cell invasion and migration.49 In this study, the elevated glutamic acid probably increased esophageal cancer cell invasion and migration. Interestingly, GABA is an inhibitory neurotransmitter, whereas glutamic acid is an excitatory one. The decreased GABA and increased glutamic acid suggest that neurotransmitters possibly play important roles in esophageal cancer metastasis. Our study

4.5. Potential Biomarkers for ESCC Metastasis

Early diagnosis of cancer metastasis will aid personalized treatment and improve prognostic effects. In this study, we identified a panel of potential small-molecule serum biomarkers for the diagnosis of ESCC metastasis. In addition to the potential biomarkers discussed above, we also identified two other important metabolites, iminodiacetic acid and pyrrole-2carboxylic acid. So far, just one paper has reported that decreased levels of pyrrole-2-carboxylic acid are a urinary biomarker of lung cancer.57 Pyrrole-2-carboxylic acid is a catabolic metabolite of 4-hydroxyproline in mammals, and both of these were decreased in the serum samples of ESCC patients. Pyrrole-2-carboxylic acid, showing a significant decrease only in the metastasis group, is suggested to be a unique potential indicator of metastasis of ESCC. However, it is J



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and was partly supported by the National Nature Science Foundation of China (no. 81170755).

unfortunate that none of the metabolites exhibited high diagnostic performance. Metabolic diseases generally have systematic dysregulation of several biochemical pathways and therefore multiple biomarkers will be more powerful for use in early diagnosis and staging. By constructing a binary logistic regression model, the combination of valine, GABA, and pyrrole-2-carboxylic acid could well-discriminate lymph node metastatic ESCC patients from non-metastatic ESCC patients with high diagnostic capacity. These three biomarkers are mainly involved in cancer cell proliferation, migration, and oxidative stress.

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5. CONCLUSIONS The present metabolomics work globally profiled the serum metabolome and identified characteristic metabolites relating to the carcinogenesis and metastasis of ESCC. ESCC patients have significantly different metabolome profiles from that of healthy controls, and, importantly, ESCC patients could be well-classified according to lymph node metastasis, the single most important prognostic factor, by an OPLS-DA analysis of the serum metabolome. We identified a panel of serum metabolite markers relating to ESCC metastasis, of which the combination of serum valine, GABA, and pyrrole-2-carboxylic acid, involved in cancer cell proliferation, migration, and oxidative stress, could discriminate metastatic ESCC patients from non-metastatic ESCC patients with high diagnostic capacity, sensitivity, and specificity. The present study is the first clinical metabolomics study focused on lymph node metastasis of esophageal cancer, suggesting a promising application of serum metabolomics on non-invasive staging, prognosis prediction, and tailored therapeutics of esophageal cancer. Future work should focus on the study of a larger scale of blood samples for screening and validation as well as on systematic mechanisms of lymph node metastasis based on tissue samples, such as lymphoid tissues, by global and targeted metabolomics and other approaches of systems biomedicine.

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ASSOCIATED CONTENT

S Supporting Information *

Table S1: Clinicopathologic characteristics of esophageal squamous cell carcinoma (ESCC) patients. Table S2: The differential serum metabolites without considering VIP values of OPLS-DA model between healthy subjects, non-metastatic ESCC patients, and metastatic ESCC patients. Figure S1: Scores plots of OPLS-DA of non-metastatic or metastatic ESCC patients vs healthy subjects and permutation tests of their corresponding PLS-DA models. This material is available free of charge via the Internet at http://pubs.acs.org.

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REFERENCES

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected], Tel./Fax: +86-21-54920191. Author Contributions §

H.J. and F.Q. contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study was financially supported by the foundation of Shanghai Municipal Health Bureau of China (no. 20134403) K



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