Metabolomics Study of Hepatocellular Carcinoma: Discovery and

Article pubs.acs.org/jpr

Metabolomics Study of Hepatocellular Carcinoma: Discovery and Validation of Serum Potential Biomarkers by Using Capillary Electrophoresis−Mass Spectrometry Jun Zeng,†,§ Peiyuan Yin,†,§ Yexiong Tan,‡ Liwei Dong,‡ Chunxiu Hu,† Qiang Huang,† Xin Lu,† Hongyang Wang,*,‡ and Guowang Xu*,† †

Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Dalian 116023, China ‡ International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Institute, The Second Military Medical University, 225 Changhai Road, Shanghai 116023, China S Supporting Information *

ABSTRACT: Hepatocellular carcinoma (HCC) is one of the most lethal malignancies. The lack of effective screening methods for early diagnosis has been a longstanding bottleneck to improve the survival rate. In the present study, a capillary electrophoresis−time-of-flight mass spectrometry (CE−TOF/ MS)-based metabolomics method was employed to discover novel biomarkers for HCC. A total of 183 human serum specimens (77 sera in discovery set and 106 sera in external validation set) were enrolled in this study, and a “serum biomarker model” including tryptophan, glutamine, and 2-hydroxybutyric acid was finally established based on the comprehensive screening and validation workflow. This model was evaluated as an effective tool in that area under the receiver operating characteristic curve reached 0.969 in the discovery set and 0.99 in the validation set for diagnosing HCC from non-HCC (health and cirrhosis). Furthermore, this model enabled the discrimination of small HCC from precancer cirrhosis with an AUC of 0.976, highlighting the potential of early diagnosis. The biomarker model is effective for those a-fetoprotein (AFP) false-negative and false-postive subjects, indicating the complementary function to conventional tumor marker AFP. This study demonstrates the promising potential of CE−MS-based metabolomics approach in finding biomarkers for disease diagnosis and providing special insights into tumor metabolism. KEYWORDS: hepatocellular carcinoma, HCC, metabolomics, serum, biomarkers, capillary electrophoresis, mass spectrometry

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INTRODUCTION Hepatocellular carcinoma (HCC) is one of the most prevalent human malignancies leading to the most frequent causes of death from cancer worldwide, especially in East Asia and Africa.1 China is the area of the world most affected by HCC, and liver cirrhosis is the major precancerous lesion in the majority of HCC cases.2 It is worth noting that the rapid development and early metastasis of HCC always lead to a poor prognosis. In fact, early discrimination of HCC can significantly improve the prognosis of HCC patients, especially the survival rate.3 However, until now, it is still a great challenge for the early detection of HCC for high risk population.4,5 Currently, ultrasonography and the serum surveillance of tumor markers (a-fetoprotein, AFP) are still major screening methods for HCC.6 However, ultrasonography is not sensitive enough to distinguish small malignant HCC from cirrhotic nodules. Although effective, the sensitivity of AFP is far from ideal and specificity in the diagnosis of patients with liver disease (e.g., cirrhosis) is poor. The limited effectiveness of © 2014 American Chemical Society

these traditional screening methods greatly restricts the early detection of HCC. Therefore, the development of novel biomarkers for monitoring HCC in large population would be promising and of great clinical importance for diagnosis. Recently, the introduction of emerging metabolomics strategy provides novel insights into biomarker discovery and broadens our understanding of the pathological mechanism. As a powerful new tool, metabolomics has been increasingly applied in clinical fields.7−10 To date, nuclear magnetic resonance (NMR),11 gas chromatography−mass spectrometry (GC−MS),12 and liquid chromatography−mass spectrometry (LC−MS)9 are the most commonly used analytical techniques for metabolomics, which provide valuable clues to understanding the diseases. Bile acids, phospholipids, and fatty acids were reported to be altered in HCC patients based on a great number of previous studies.13−15 Received: April 16, 2014 Published: May 22, 2014 3420

dx.doi.org/10.1021/pr500390y | J. Proteome Res. 2014, 13, 3420−3431

Journal of Proteome Research

3421

All data are presented as mean ± SE. HBsAg: surface antigen of the hepatitis B virus (HBV); HCV: hepatitis C virus. bOne HCC patient had lack of tumor diameter information. cThese three patients had more than one HCC nodules, and the sum of tumor diameter exceeded 3 cm.

0 6 8 7

53.36 ± 1.97 (34−72) 18/7 52.03 ± 1.20 (41−64) 21/9 age sex (male/female) AFP > 20 (μg/L) HBsAg (yes/no) HCV (yes/no) tumor diameter (cm)b ≤3 3−5 5−10 >10

a

3c 11 9 3 20

53.40 ± 1.46 (42−69) 17/8 8 (32%) 45.00 ± 1.43 (33−62) 24/7

31 HCC 22 health

cirrhosis 25 30

A total of 183 human fasting serum specimens were enrolled in this study. Written informed consent was given by each participant, and the study was approved by the ethics committee of the Second Military Medical University, Shanghai, China. All HCC patients had not received any other therapy including medications except surgery after sampling. Detailed information on these subjects is summarized in Table 1. In the discovery phase, 77 serum samples were studied to identify marker candidates. All 22 HCC serum samples were obtained from the National Liver Tissue Bank in the Second Military Medical University (Shanghai, China), and 25 cirrhosis subjects were recruited from the Dalian Sixth People’s Hospital (Dalian, China). Another 30 healthy control samples were also enrolled from Dalian in this phase. Every HCC patient was histopathologically diagnosed after the tumor excision, and the diagnoses of cirrhosis were carried out according to clinical, laboratory evidence, and imaging. On the basis of the incidence,

characteristics

Clinical Samples

discovery set

Table 1. Clinical Information of the Subjects Enrolled in the Discovery and Validation Phasesa

HPLC-grade methanol (Merck, Germany), 98% formic acid (Fluka, Germany), HPLC-grade ammonium acetate (Tedia, USA), and ultrapure water (18.2 MΩ-cm, TOC = 6 ppb; Millipore, USA) were used for solvent preparation. Internal Standard Solution 1 (ISS1) contains 10 mM L-methionine sulfone (Wako, Japan) and D-camphor-10-sulfonic acid sodium salt (Wako, Japan) in water. Internal Standard Solution 2 (ISS2) contains 1 mM 3-aminopyrrolidine dihydrochloride (Aldrich, USA), N,N-Diethyl-2-phenylacetamide (Wako, Japan), trimesic acid (Wako, Japan), and disodium 3-hydroxynaphthalene-2,7-disulfonate (Wako, Japan) in methanol. These two internal standard solutions were used to standardize the signal intensity and calibrate migration time, respectively. Besides, isotope internal standards of L-alanine-3,3,3-d3 (Aldrich, USA), succinic acid-13C4 (Aldrich, USA) and cholic acid-2,2,4,4-d4 (Aldrich, USA) were prepared in stock solutions to correct the signal intensity complementally.

health

Chemicals

56.82 ± 2.23 (24−71) 18/4 10 (45.5%) 19/3 0/22

small HCC

25

cirrhosis

50 20 50.00 ± 1.90 (38−64) 18/2 15 (75%) 18/2 0/20

HCC validation set

MATERIALS AND METHODS

number

■

general HCC

However, what we should point out is that in these conventional studies, information on important polar and ionic metabolites was absent or insufficient due to the inherent limitation of these traditional analytical platforms.16 It is worth noting that polar metabolites generally play an indispensable role in the biological system.8 Recently, more and more attention has been paid to their key effect on tumorigenesis and suppression.9,17−20 The novel metabolomics profiling approach based on capillary electrophoresis-mass spectrometry (CE−MS) is helpful to cover polar metabolome and discover HCC potential biomarkers. Until now, there has been rare evaluation of serum polar metabolome using CE−MS in the context of HCC.10 In this study, a total of 183 human serum specimens in which 77 sera for discovery set and 106 sera for external validation set were enrolled, including healthy control, cirrhosis, and HCC subjects. This present CE−TOF/MS-based metabolomics study aims at exploring noninvasive and reliable serum metabolic biomarkers for HCC diagnosis and providing a special perspective into the HCC-related metabolic changes. To improve the practicability of potential biomarkers in the clinic, we designed a three-step analysis strategy and employed it for the utilization of clinical and metabolic information. Small HCC subjects were particularly enrolled in the validation set to verify the potential of biomarkers for early diagnosis.

30 50.33 ± 1.74 (32−72) 28/2 14 (46.7%) 21/9 1/29

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data. On the basis of the coaxial sheath liquid interface, sheath liquid (methanol/water (50% v/v) and 0.1 μM hexakis (2,2difluoroethoxy) phosphazene) was delivered at 10 μL/min to realize the CE−MS coupling. A fused silica capillary (50 μm i.d. × 80 cm) was applied for CE separation, and the temperature of the capillary was controlled at 20 °C. The sample tray temperature was set below 5 °C by the minichiller. Two different analysis modes, including the cation-positive (CP) mode and the anion-negative (AN) mode, were performed in this experiment. Detailed CE−TOF/ MS methods of these two modes were described in the Supporting Information. All samples were randomized with respect to run order to avoid batch effects. Moreover, QC samples were inserted into the analytical sequence after each set of 10 real samples.

each group was basically matched in age and sex. Only 10 HCC subjects (45.5%) actually had an AFP value greater than the upper limit of 20 μg/L. Nineteen HCC (86.4%) patients were infected with hepatitis B virus (HBV), while no HCC patients were infected with hepatitis C virus (HCV). According to the histopathological results, 6 HCC patients had a solitary nodule smaller than 5 cm, 8 patients were 5 to 10 cm, and 7 patients exceeded than 10 cm. Another 106 subjects were collected to independently validate defined serum metabolic markers. All 50 HCC serum samples were also collected from the National Liver Tissue Bank. Among all HCC patients, 20 subjects had a solitary nodule or the sum of at most two nodules smaller than 3 cm in diameter (i.e., small HCC). These small HCC subjects were enrolled to validate the early diagnosis potential of metabolic markers. Other HCC subjects were defined as “general HCC” to be convenient in the validation set apart from small HCC. Besides, 25 cirrhosis subjects were recruited from the First Hospital of Jilin University (Changchun, China). Another 31 healthy control samples were also enrolled in this external validation phase. The results of AFP, HBsAg, HCV, and tumor diameter for validation set are presented in Table 1. Standard protocol for sample collection and storage was formulated and followed based on our previous study to control the possible interference of sampling.21 All samples were stored immediately at −80 °C until metabolite extraction.

Data Processing

To facilitate peak identification, we preanalyzed about 500 metabolite standards by our collaborator (i.e., Human Metabolome Technologies, Inc. (HMT, Japan)). On the basis of accurate mass and obtained migration time of these standards, peak extraction method was first edited using Quantitative Analysis Software (B.04.00, Agilent). Besides, Qualitative analysis software (B.04.00, Agilent) was used for the extraction of the information on internal standards. All samples were then arranged and grouped according to the migration time of internal standards, and the average migration time of internal standards in each cluster was calculated. On the basis of internal standards’ time in each cluster, migration time correction for metabolites was archived by using the software of MethodMarker (HMT, Japan). The standard migration time, listed in above peak extraction method, was corrected to real migration time corresponding to current experimental conditions. After that, peak extraction and identification were carried out with Quantitative Analysis Software based on accurate mass and corrected migration time of preanalyzed standards. The window was set to ±20 ppm for m/z extraction and ±1.5 min for migration time inspection. Further peak checking and noise removal were necessary to reduce errors. Some standards were also directly added to real samples for the discrimination of isomers. Extracted peak information with matched migration time, m/z value, and peak area were exported. The results of each cluster were finally merged to create a compound table.

Serum Preparation

Serum samples were thawed on ice before preparation. We mixed 50 μL of serum with 450 μL of methanol containing internal standards (10 μM of ISS1, 0.9 μg/mL of L-alanine3,3,3-d3, 0.9 μg/mL of succinic acid-13C4, and 0.7 μg/mL of cholic acid-2,2,4,4-d4) and then 500 μL of chloroform successively. The mixture was thoroughly vortexed for 1 min both before and after chloroform addition. Next, 200 μL of Milli-Q water was added to form a two-phase system. After vortexing, the mixture was left to stand for 5 min and then centrifuged at 5000g for 10 min at 4 °C. Subsequently, 420 μL of the upper layer was transferred and centrifugally filtered through a 5 kDa cutoff filter (Millipore, USA) to remove proteins (13 000g, 3h at 4 °C). The filtrate was lyophilized and stored in a −80 °C freezer. Prior to CE−TOF/MS analysis, the dried serum sample was reconstituted in Milli-Q water containing 50 μM ISS2. To monitor the robustness of sample preparation and the stability of instrument analysis, we prepared quality control (QC) sample by pooling equal aliquots of serum from each sample. The pretreatment of serum QC samples was consistent with real samples.22 It should be noted that the pretreatment of samples from subjects suffering from hepatitis infections needs to be performed in the lab with biosafety, and the operation should be carried out in a hood to minimize exposure to chloroform (a type-2 carcinogen) during sample preparation.

Statistical Analysis

Before statistical analysis, normalization to the area of internal standards for each sample was performed. For each metabolite, internal standard was selected independently to obtain the least %RSD of area in QC samples. On the basis of the partial leastsquares discriminant analysis (PLS-DA) model, the multivariate pattern recognition with unit variance (UV) scaling was performed by using SIMCA-P software (Umetrics, Sweden). Response permutation test with 200 times was also performed to evaluate whether the model had overfitting. Multi Experiment Viewer (MeV) software (open-source genomic analysis software created by the MeV development team) was used for univariate analysis. Wilcoxon Mann−Whitney Test and stricter false discovery rate (FDR) correction based on the Benjamini− Hochberg method were performed to assess the statistical significance. Hierarchical cluster analysis (HCA) was also applied to visualize the relative levels of all chosen differential expression metabolites using this software. On the basis of the in-house developed program using Matlab software (The MathWorks, USA),

CE−TOF/MS Analysis

The CE experiment was performed using a CE system (G7100A, Agilent, USA) equipped with a 1260 ISO pump (G1310B, Agilent, USA) and a minichiller (Huber, Germany). ChemStation software (B.04.03, Agilent) was used to control the CE system. The MS experiment was carried out with a TOF/MS system (G6224A, Agilent) equipped with a CE− electrospray ionization (ESI)−MS sprayer kit (G1607A, Agilent, USA). Mass Hunter Workstation software (B.04.00, Agilent) was used to control the TOF/MS system and acquire 3422



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z-score plots were employed to depict the relative distribution of enrolled subjects in different comparison groups. Receiver operating characteristic (ROC) curve and the binary logistic regression were made by using SPSS Statistics software (SPSS, Inc., USA) and Medcacl software (Ostend, Belgium). Furthermore, the pathway analysis module of MetaboAnalyst (a web service for metabolomics data analysis) was applied to analyze important metabolic pathways.

demonstrated the acceptable stability and reproducibility in the present metabolomics study. To obtain a direct overview of systemic alterations in metabolome, we performed multivariate pattern recognition analysis based on the PLS-DA model. Original data sets, provided by two different analytical methods independently, were combined for multivariate discussion. Meanwhile, metabolites with a % RSD of area in QC samples higher than 30% were also removed from this combined data set to reduce the error. Two principal components (PCs) were calculated based on this PLS-DA model. As shown in Figure 2A, healthy individuals cluster closely apart from HCC group and cirrhosis group. HCC and cirrhosis individuals exhibit a clear discrimination trend along the direction of the first principal component. The model parameters of R2Y and Q2 (cum) are 73.3 and 67%, respectively, representing the evaluation of the fitness and prediction ability of PLS-DA model. Subsequently, the permutation test with 200 iterations was performed. This PLS-DA model was valid without overfitting as the permuted R2 and Q2 values to the left were lower than the original points to the right, and the intercept of Q2 was below zero (Figure S2 in the Supporting Information). Therefore, the classification result shows a robust metabolic difference, indicating the great potential of multivariate analysis for HCC screening.

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RESULTS The workflow of HCC metabolomics study on biomarker discovery, validation, and “serum biomarker model” establishment is shown in Figure 1.

Defining of Metabolic Biomarker Candidates

As shown in the flowchart (Figure 1), the combination of multivariate and univariate analysis was performed to analyze data set containing metabolites with a %RSD of area in QC samples less than 30%. Metabolites were well checked before being selected as biomarker candidates in this discovery phase to reduce the risk of false-positives. First, metabolites with variable importance in the projection (VIP) value exceeding 1 were retained, which have the above statistical importance on the classification. As is shown in Figure 2C,D, 41 and 42 metabolites were spotted on the V plot responsible for the two PCs (VIP [1] and VIP [2]) of the model, respectively. An intersection of these metabolites containing 30 metabolites was obtained to preserve the variances in two PCs (Figure 2B). Second, three metabolites including glycocholic acid, tyrosine (Tyr), and malic acid with negative confidence intervals on the VIP column plot were removed (Figure 2E,F). Third, the covariance p(corr) values of these metabolites were also taken into consideration as a complement. On the basis of the result of V plots (Figure 2C,D), the metabolite was further retained if its absolute p(corr) value of either component was larger than 0.3.24 Finally, 27 metabolites were obtained from the multivariate screening. For univariate screening, 45 and 53 significantly changed metabolites (p < 0.05) were found between HCC and the other two cohorts of health and cirrhosis group, respectively. Detailed statistical information on all differential expression metabolites is presented in Table S1 in the Supporting Information. To have an overview, we applied heat map based on the analysis of Pearson correlation coefficients to visualize their relative levels (Figure S3 in the Supporting Information). Meanwhile, the distribution of these metabolites across all specimens was presented in the z-score plots (Figure 3B,E). Among these metabolites, 31 metabolites were found significantly changed in both comparison groups simultaneously (Figure 3A). Taken together, a total of 19 metabolites were cross-selected (i.e., the intersection) based on the preliminary screening results of multivariate and univariate analysis. To further define effective

Figure 1. Scheme of the metabolomics study of hepatocellular carcinoma (HCC).

Metabolic Profiling of CE−TOF/MS

After the peak identification and refining based on the 80% rule,23 120 metabolites from serum discovery set were retained, including amino acids, organic acids, amines, sugar phosphates, and other kinds of polar and ionic metabolites. Samples from each group were analyzed in random order to ensure the high quality of acquired data, and QCs were inserted into the analysis sequence. The analytical characteristics of metabolic profiling were studied to evaluate the robustness of sample preparation and the stability of instrument analysis. Principal component analysis (PCA) shows that all QC samples were within two times of the standard deviation (SD) in score plots (Figure S1A,C in the Supporting Information).22 Furthermore, the distributions of %RSD for QC samples indicate that 99.02% (CP mode, Figure S1B in the Supporting Information) and 98.05% (AN mode, Figure S1D in the Supporting Information) of sum of responses have a %RSD of 1 and VIP [2] > 1 were spotted on the V plots responsible for the two principal components of the model, respectively. (E,F) Column plots of VIP value with jack-knifed confidence intervals.

into two groups, including small HCC group and general HCC group. Despite the difficulties in the collection of small HCC samples, those subjects were particularly enrolled in the validation set to test biomarkers’ potential for early diagnosis. Thus, a stepwise validation was performed in this phase. First, the statistical significance (p < 0.05) when all HCC subjects were compared with healthy controls and cirrhosis subjects was respectively required. Except for pipecolic acid, these five metabolic candidates were validated successfully (Table 2). Furthermore, statistical significance between small HCC and cirrhosis and between small HCC and healthy controls was validated for these five candidates. Finally, four metabolites including Gln, Trp, Arg, and 2-hydroxybutyric acid fulfilled the demand of small HCC discrimination (Table 2). The %RSD of peak area for these candidates in QC samples was 1−6%, indicating the stability and reproducibility of the validation analysis. Subsequently, the diagnostic potential of these four candidates was evaluated. ROC curve was exploited based on the results of area under the curve (AUC), the sensitivity, and specificity at best cutoff points. Table 3 shows the results of ROC analysis for the discovery and validation sets. To obtain the best combination of biomarkers, we performed an optimized algorithm of Forward Stepwise (Wald) method for binary logistic regression,25 and the results are presented in Table S2 in the Supporting Information. For the discovery set, Trp in the model was enough to obtain

markers, we studied the distribution of these 19 metabolites based on z-score plots. The confidence interval of average value was subsequently validated. As a result, gluconic acid was removed from the candidate pool because of the discrete z-score ranges (−0.38 to 1718.68 (HCC vs Health) and −0.27 to 1256.77 (HCC vs cirrhosis)) and its negative confidence interval. Then, stricter statistical analysis of FDR correction based on the Benjamini−Hochberg method was performed to reduce the risk of false-postive. A total of 12 metabolites with FDR < 1.5% were retained in the candidate pool. Finally, HCA was performed to reveal the correlation between these metabolic candidates and clinical biomarker of AFP. Considering the complementary role of novel metabolic biomarkers to AFP in the improvement of diagnostic sensitivity and specificity, we are more interested in metabolites that are not related with AFP level. Figure 3C indicated that these 12 metabolites and AFP were clustered into 4 groups, and 6 metabolites were not obviously correlated with AFP levels (|Cij| < 0.4), including tryptophan (Trp), pipecolic acid, glutamine (Gln), 3/4-methyl-2-oxovaleric acid, arginine (Arg), and 2-hydroxybutyric acid. Validation and Evaluation of Metabolic Biomarkers

Another set of 106 serum samples was collected and analyzed in the external validation phase to validate the reliability of these 6 candidates for HCC diagnosis, especially discriminating HCC from cirrhosis. In particular, HCC subjects were divided 3424



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Figure 3. Univariate statistical analysis. (A) Venn diagram of the Wilcoxon Mann−Whitney test results. On the basis of all significant differential metabolites (Table S1 in the Supporting Information), (B) the z-score plot for the comparison between HCC subjects (yellow) and healthy controls (blue) and (E) the z-score plot for the comparison between HCC subjects (yellow) and cirrhosis subjects (blue). Each point represents one metabolite in one sample. A total of 19 metabolites were cross-selected (i.e., the intersection) based on the preliminary screening result of multivariate analysis and univariate Wilcoxon Mann−Whitney test, which were plotted with * in z-score plots. For HCC subjects, the z-score ranges of gluconic acid are −0.38 to 1718.68 (HCC vs Health) and −0.27 to 1256.77 (HCC vs cirrhosis), respectively. Some HCC subjects with excessive z-score for gluconic acid were not presented in z-score plots. (C) Dendrogram of candidates and AFP based on the Pearson correlation coefficients. (D) Histogram of potential biomarkers. The first set of columns comprises health, cirrhosis, and HCC groups (discovery set), and the second set of columns comprises health, cirrhosis, all HCC, small HCC, and general HCC groups (validation set). The black * means the statistical significance between the health group and other groups. The red # means the statistical significance between the cirrhosis group and other groups. The blue & means the statistical significance between the small HCC and general HCC groups.* (# or &): 0.01 < p < 0.05, ** (# #): 0.001 < p < 0.01, and *** (# # #): p < 0.001. All data are presented as mean ± SD.

effective discrimination, whereas the combination of Trp, Gln, and 2-hydroxybutyric acid was better to establish the discrimination model for the validation set (including small HCC subjects).

Compared with single biomarker, it is obvious that the rational combination of these three metabolites achieved better AUC values, sensitivity, and specificity in both discovery and validation 3425



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Table 2. Statistical Information of Potential Metabolic Biomarkersa Trp discovery set

multivariate

univariate

HCC/health

HCC/cirrhosis

validation set

univariate

all HCC/health

all HCC/cirrhosis

general HCC/healthb

general HCC/cirrhosis

small HCC/health

small HCC/cirrhosis

small HCC/general HCC

VIP[1] VIP[2] p(corr)[1] p(corr)[2] p adj. p ratio p adj. p ratio p adj. p ratio p adj. p ratio p adj. p ratio p adj. p ratio p adj. p ratio p adj. p ratio p adj. p ratio

2.34 1.68 0.71 0.07 2.00 1.07 0.60 6.12 6.55 0.52 9.89 5.34 0.58 1.29 8.17 0.52 5.77 3.12 0.54 1.11 7.48 0.49 6.59 1.78 0.63 3.95 1.71 0.57 3.25 3.91 1.17

Gln

× 10−8 × 10−6 × 10−8 × 10−6 × 10−13 × 10−11 × 10−9 × 10−9 × 10−11 × 10−9 × 10−8 × 10−8 × 10−8 × 10−6 × 10−6 × 10−5 × 10−2 × 10−1

1.72 1.24 0.66 −0.05 5.71 × 2.66 × 0.84 8.75 × 6.24 × 0.78 1.02 × 3.05 × 0.88 3.58 × 3.22 × 0.70 1.40 × 4.58 × 0.85 1.11 × 7.48 × 0.68 8.70 × 2.41 × 0.92 5.03 × 4.18 × 0.73 1.27 × 5.51 × 1.08

10−4 10−3 10−5 10−4 10−4 10−4 10−10 10−9 10−4 10−4 10−8 10−8 10−3 10−2 10−7 10−6 10−1 10−1

2-hydroxybutyric acid

Arg 1.42 1.70 0.52 0.50 1.59 4.16 0.60 1.99 6.66 0.75 1.18 2.56 0.68 1.76 4.04 0.73 2.55 3.94 0.68 1.25 2.69 0.73 3.83 1.78 0.68 3.99 9.18 0.73 8.74 9.35 1.01

1.14 1.12 −0.34 −0.27 1.59 × 1.00 × 1.78 4.00 × 1.22 × 1.42 2.29 × 7.11 × 1.90 2.48 × 4.79 × 1.39 1.99 × 5.38 × 1.90 8.69 × 1.59 × 1.39 5.92 × 1.78 × 1.89 3.21 × 5.88 × 1.38 8.74 × 9.35 × 0.99

× 10−7 × 10−6 × 10−3 × 10−3 × 10−11 × 10−10 × 10−5 × 10−5 × 10−9 × 10−8 × 10−4 × 10−4 × 10−8 × 10−6 × 10−4 × 10−4 × 10−1 × 10−1

10−5 10−4 10−3 10−2 10−12 10−11 10−4 10−4 10−10 10−9 10−4 10−3 10−8 10−6 10−3 10−3 10−1 10−1

3/4-methyl-2-oxovaleric acid 1.29 1.28 0.29 0.40 8.05 5.75 0.65 1.85 6.61 0.73 8.16 5.51 0.65 4.94 9.04 0.69 1.29 9.97 0.60 8.20 1.97 0.64 3.33 1.44 0.73 7.86 1.06 0.77 1.00 3.62 1.21

× 10−6 × 10−5 × 10−3 × 10−3 × 10−8 × 10−7 × 10−4 × 10−4 × 10−7 × 10−7 × 10−5 × 10−4 × 10−4 × 10−3 × 10−2 × 10−1 × 10−2 × 10−1

a

adj. p: the adjusted p value obtained from the false discovery rate (FDR) correction using Benjamini−Hochberg method. bInformation on general HCC and small HCC is presented in Table 1.

Table 3. ROC Analysis Results for Discovery Set and Validations Set 95% CI discovery set HCC vs (Health and Cirrhosis)

validation set HCCb vs (Health and Cirrhosis)

metabolites

AUC

standard error

lower

upper

sensitivity

specificity

Trp Arg Gln 2-hydroxybutyric acid 1 + 2 + 3 + 4a 1 + 3 + 4a Trp Arg 2-hydroxybutyric acid Gln 1 + 2 + 3 + 4a 1 + 3 + 4a

0.961 0.854 0.806 0.804 0.969 0.969 0.955 0.886 0.874 0.842 0.991 0.990

0.020 0.043 0.050 0.061 0.026 0.026 0.021 0.033 0.036 0.038 0.009 0.010

0.890 0.755 0.700 0.698 0.902 0.902 0.896 0.809 0.796 0.759 0.950 0.947

0.991 0.924 0.887 0.886 0.995 0.995 0.986 0.939 0.931 0.906 0.998 0.998

0.909 0.818 0.636 0.818 0.955 0.955 0.980 0.625 0.920 0.840 0.980 0.980

0.945 0.818 0.873 0.782 0.964 0.982 0.821 1.000 0.750 0.696 0.964 0.946

a

Based on their AUC values, these metabolic biomarkers are sorted in descending order, and the number corresponds to the order listed in the table. In the discovery set, 1, 2, 3, and 4 in the column “metabolites” are Trp, Arg, Gln, and 2-hydroxybutyric acid, respectively. In the validation set, 1, 2, 3, and 4 in the column “metabolites” are Trp, Arg, 2-hydroxybutyric acid, and Gln, respectively. bIncluding small HCC and general HCC.

2-hydroxybutyric acid) was defined. Figure 3D presents the relative contents of these three potential biomarkers across all groups. Furthermore, the possible influence of preanalytical bias such as samples from different medical centers was evaluated, and under the standard collection protocol no significant

sets (Table 3). Meanwhile, it is interesting that these three metabolites came from complementary clusters (Figure 3C), and the combination of them would be advantageous in the improvement of diagnostic performance. Thus, a “serum biomarker model” containing three biomarkers (Trp, Gln, and 3426



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Figure 4. Evaluation of the diagnostic potential for defined biomarkers listed in Table 3. The serum samples from healthy control, cirrhosis, and HCC groups were classified using binary logistic regression. (A) ROC curves for the differential diagnosis using data of discovery set. (B−D) ROC curves for the differential diagnosis using data of validation set. (E) Discrimination of the non-HCC subjects (i.e., health and cirrhosis) and HCC patients using three markers of Trp, Gln, and 2-hydroxybutyric acid (discovery set). (F) Discrimination of the non-HCC subjects and different HCC patients (i.e., small HCC and general HCC) using three markers (validation set). (G) Comparison of AFP and three metabolic markers for the discrimination of false-postive and false-negative subjects. AFP cutoff is 20 μg/L, and the metabolic markers used a cutoff of probability of 0.5.

impacts were found for these three biomarkers (Supporting Information). In the discovery set, the AUC value of this “serum biomarker model” was determined as 0.969 (Figure 4A) for the discrimination of HCC and non-HCC (i.e., a combination of healthy controls and cirrhosis), and the sensitivity and specificity of the HCC diagnosis were 95.5 and 98.2% at the best cutoff value, respectively. In the external validation set, 98% of all HCC subjects (small HCC group and general HCC group) and 94.6% of the non-HCC subjects can be correctly diagnosed at the best cutoff value, with an AUC value of 0.99 (Figure 4B). As shown in Figure 4E,F, prediction probability values of this “serum biomarker model” obtained from the binary logistic regression were applied in the discrimination. At the traditional cutoff value (i.e., 0.5),26 100, 96, and 90.9% of healthy controls, cirrhosis subjects, and HCC subjects in the discovery set were correctly discriminated, respectively. 100, 92, 90, and 96.7% of healthy controls, cirrhosis subjects, small HCC subjects, and general

HCC subjects in the validation set were correctly discriminated, respectively. Comparison with Clinical Results

Furthermore, the diagnostic potentials of these three metabolic markers and AFP were compared carefully. For the challenging diagnosis of small HCC from precancer cirrhosis (Figure 4D), the AUC of this biomarker model was 0.976, which was greater than that of AFP (AUC = 0.746). When all HCC samples were enrolled, similar comparison results for the diagnosis of HCC and cirrhosis are presented in Figure 4C. Noticeably, the diagnostic accuracy of this “serum biomarker model” for the AFP false-negative and false-positive subjects was greatly improved (Figure 4G). AFP value of 20 μg/L and the probability of 0.5 for metabolic markers were used as the cutoff.26 For HCC patients with AFP false-negative (AFP < 20 μg/L), 83.3% in the discovery set and 80% (small HCC) and 100% (general HCC) in the validation set showed positive results based on these three metabolic markers. For cirrhosis patients with AFP false-positive (AFP > 20 μg/L), 100% of them can be diagnosed as non-HCC 3427



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effective for the discrimination of small HCC. Subsequently, a simplified “serum biomarker model” containing only three metabolites (Trp, Gln, and 2-hydroxybutyric acid) was constructed using an optimization algorithm to discriminate HCC patients from non-HCC subjects while retaining high accuracy. This simplified biomarker model is of better clinical value. It is worth noting that 54.5 and 42% HCC patients were AFP false-negative (AFP < 20 μg/L) in the discovery set and validation set, respectively. This metabolic “serum biomarker model” can be a good classifier in that AUC achieved 0.969 in the discovery set and 0.99 in the validation set for diagnosing HCC from non-HCC (health and cirrhosis), suggesting the diagnostic robustness of the biomarker model. Moreover, this model enabled the discrimination of small HCC patients from precancer cirrhosis patients with an AUC of 0.976, highlighting the potential of early diagnosis. In particular, the effective diagnostic performance of this metabolic markers for those false-negative (HCC, AFP < 20 μg/L) and falsepostive (Cirrhosis, AFP > 20 μg/L) patients may imply the complementary function to conventional AFP. Therefore, the combinational use of them has the promising clinical potential to improve the diagnostic accuracy of HCC. However, it is worth noting that for the purpose of final clinic applications further studies are still needed to advance our current work. In the future, more validation in a larger and heterogeneous population is under consideration as extensions to verify these novel biomarkers.

by using metabolic markers in the validation set. Thus, all results demonstrated that this metabolic “serum biomarker model” (Trp, Gln, and 2-hydroxybutyric acid) has the promising potential to diagnose HCC, already at in early stage. Moreover, the use of metabolic markers plus AFP can effectively improve the diagnostic performance. The “serum biomarker model” serves as a supplementary tool of AFP for the early diagnosis of HCC. The variations of these three biomarkers were also subjected to investigation over the cancer staging. A cancer staging system of TNM classification is used to define the extent of malignant tumors.27 To describe the size of the original (primary) tumor and whether it has invaded nearby tissue, we obtained T values of the TNM system from the validation set. As illustrated in Figure S4 in the Supporting Information, no obvious changes in the levels of biomarkers were observed over the staging in HCC patients, except for Gln.

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DISCUSSION

Potential Diagnostic Model for HCC

Currently, the discovery of potential biomarkers with clinical practicability is still a great challenge for the early detection of HCC. Because of the inherent limitation of traditional analytical platforms of GC−MS and LC−MS,8 acquired information on polar metabolites was always absent or insufficient. Considering the increasing attention to polar metabolites in tumorigenesis and suppression, a well-designed strategy for HCC metabolomics study based on novel CE−TOF/MS was therefore performed to obtain a global view of the polar metabolome alterations and discover potential biomarkers. Clinical information was taken into consideration in the determination of markers to improve the practicability of metabolic biomarkers. The study was divided into three parts, including discovery, validation, and biomarker model establishment (Figure 1). A total of 183 human serum specimens consisting of 77 specimens in the discovery set and 105 specimens in the validation set were enrolled in this study. To control and minimize the possible source of bias in biomarker discovery: (i) We formulated and followed a standard protocol for sample collection and storage based on our previous study.21 (ii) In the discovery set, each group was well matched in age and sex, and the distribution of tumor diameter was balanced, which would be beneficial to discover potential biomarkers. In the external validation set, serum samples with a wider range of tumor diameter were collected. More subjects with small tumor were particularly enrolled in this phase. (iii) Samples were randomized with respect to run to avoid batch effects. (iv) The influence of different medical centers on three biomarkers was investigated. (v) A stepwise external validation was performed, aiming to verify the robustness of metabolic markers as well as their potential for early diagnosis. A comprehensive workflow was applied to explore novel metabolic markers. The combination of multivariable analysis and univariate judgment was first used to screen valuable differential metabolites. On the basis of distribution evaluation (z-score plot), FDR correction, and relevance analysis with AFP (HCA), candidates were further refined to reduce the risk of false-postives. Finally, six metabolites were retained in the candidate pool, and five metabolites were successfully validated for all HCC samples. Fortunately, it was found that four of them (Trp, Gln, Arg, and 2-hydroxybutyric acid) are still

Related Pathways of Differential Metabolites

To have a better understanding of the defined biomarkers, we studied an overview of systematic metabolome changes based on pathway analysis. HCC-induced metabolic perturbation was analyzed from the perspective of pathway enrichment analysis combined with the topology analysis. Figure 5A reveals the most relevant pathways on the basis of all significantly differential metabolites (Table S1 in the Supporting Information). The perturbation of glycine, serine, and threonine metabolism; arginine and proline metabolism; alanine, aspartate, and glutamate metabolism, TCA cycle; and so on was thus considered to be highly responsible for HCC. More importantly, insights into the underlying pathogenesis of HCC could be provided from pathways regarding defined biomarkers. These metabolic anomalies were found to be primarily involved in (i) energy metabolism and biosynthesis, (ii) immunosuppression influence, and (iii) oxidative stress, which are discussed in detail later. As the representative glucose metabolism product, lactic acid was observed with a significant accumulation in HCC group (p < 0.05), indicating the enhanced metabolic activity of glycolysis. This unique feature of glucose metabolism is consistent with the abnormal propensity of most cancer cells to take up glucose avidly and produce energy by glycolysis rather than oxidative phosphorylation (TCA cycle), despite available oxygen (i.e., Warburg effect).28 Compared with healthy and cirrhosis subjects, serum Gln was presented with a significant down-regulation in HCC (p < 0.05), suggesting a great depletion of Gln in the proliferation of tumor cells via the supply of bloodstream. Gln is recognized as the preferred amino acid for the energy generation of cancer cells.29 A high rate of Gln metabolism (glutaminolysis) can catabolize Gln to generate ATP and lactic acid.30 Meanwhile, Gln provides a secondary source of carbon to support fatty acid and lipid synthesis, which is required for tumorigenesis18,30 (Figure 5B). Actually, Gln and glucose were reported to be two of the most abundant and 3428



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Figure 5. Pathway analysis. (A) On the basis of all differential metabolites (Table S1 in the Supporting Information), global metabolic disorders of the most relevant pathways induced by HCC were revealed using the MetaboAnalyst. (B−D) Metabolism of Gln, Trp and 2-hydroxybutyric acid. GCN2, kinase; AHR, aryl hydrocarbon receptor.

of tumors can be promoted based on Trp depletion. Therefore, the control of Trp depletion can be of great value for therapeutic intervention.39 It was pointed out in previous study that the monitoring of serum Trp level has considerable potential to act as a biomarker,36 which is supported in our present study from the perspective of metabolomics. Besides, 2-hydroxybutyric acid, a small-molecule organic acid produced in mammalian hepatic tissues that synthesizes glutathione, was also observed at a higher serum level in the HCC group compared with those in healthy and cirrhosis groups. This significant change (p < 0.05) of 2-hydroxybutyric acid may contribute to the oxidative stress40 for HCC patients. Under the oxidative stress condition, hepatic glutathione synthesis would dramatically increase. 2-Hydroxybutyric acid is therefore increasingly released as a byproduct during the formation of glutathione (Figure 5D). Taken together, it is still a great challenge to obtain the systematic metabolome changes because of the complexity and individual difference. Nevertheless, these important differential metabolites give us valuable clues of metabolic anomalies for mechanism exploration and determination of potential therapeutic targets. It is worth noting that from this perspective of physiology the defined biomarker model is more informative to reveal

important nutrients and account for most carbon and nitrogen metabolism in tumor cells.30 As one of the defined biomarkers, Gln targets the disturbance of energy metabolism and biosynthesis for tumor to satisfy the requirement of rapid proliferation. Another key nutrient Arg31 was also observed to be supplied for tumor growth via the bloodstream. However, as described in a number of reports,32−35 HCC is often auxotrophic for Arg due to the lack expression of argininosuccinate synthetase. This particular dependence on exogenous Arg in HCC20 may make it unsuitable to be HCC biomarker considering the possible influence of exogenous factor (e.g., drugs). Trp, one of the defined biomarkers, was significantly decreased (p < 0.05) in HCC subjects relative to healthy controls and cirrhosis subjects. Despite the fact that the function of Trp is not fully revealed, Trp metabolism is increasingly being recognized as a key microenvironmental factor that suppresses antitumor immune response based on strong evidence.36,37 The depletion of Trp, inducing T cells energy and apoptosis, creates an immunosuppressive microenvironment in tumors.38 Meanwhile, Trp catabolites can also suppress antitumor immune responses through the activation of aryl hydrocarbon receptor (AHR)19 (Figure 5C). It has been confirmed that the survival and motility 3429



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γ-glutamyl transpeptidase; AKP, alkaline phosphatase; NMR, nuclear magnetic resonance; GC−MS, gas chromatography− mass spectrometry; LC−MS, liquid chromatography−mass spectrometry; ISS, internal standard solution; ROC, receiver operating characteristic curve; AUC, area under the curve; CI, confidence interval; CE−MS, capillary electrophoresis-mass spectrometry; TOF, time-of-flight mass spectrometry; HILIC, hydrophilic interaction chromatography; PLS-DA, partial leastsquares discriminant analysis; PCs, principal components; FDR, false discovery rate; HCA, hierarchical cluster analysis; VIP, variable importance in the projection; Tyr, tyrosine; Trp, tryptophan; Gln, glutamine; Arg, Arginine; Met, methionine; Ser, serine; Gly−Gly, glycine−glycine; Pro, proline; Phe, phenylalanine; Asp, aspartic acid; Asn, asparagine; Kyn, kynurenine; CP, cation-positive; AN, anion-negative

different aspects of HCC metabolism, which reconfirms the advantage of biomarker combination compared with single metabolite. Further studies involving cell biology are still required to comprehensively reveal the potential pathological mechanism of HCC.

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CONCLUSIONS In summary, a CE−TOF/MS-based metabolomics method was employed in this study. Compared with limited reports of polar metabolome study about HCC, a novel serum biomarker model consisting of Trp, Gln, and 2-hydroxybutyric acid was finally determined based on the comprehensive screening and validation workflow. In both the discovery and external validation sets, this biomarker model was evaluated as an effective tool for diagnosing HCC from non-HCC (health and cirrhosis). Furthermore, this model was able to discriminate small HCC from precancer cirrhosis with better ROC results than AFP, implying the promising potential of early diagnosis. It is also effective for those AFP false-negative and false-positive subjects in distinguishing HCC from cirrhosis. These results demonstrate that this novel metabolic “serum biomarker model” has complementary functionality respect to the conventional AFP as HCC markers for future clinical diagnosis, and CE−TOF/MS-based metabolomics study contributes to providing novel insights into the complicated biological process of tumor metabolism.

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ASSOCIATED CONTENT

S Supporting Information *

Details of methods of two CE−TOF/MS modes and sampling bias evaluation. Statistical information of all most significantly different metabolites (p < 0.05). Binary logistic regression results based on the Forward Stepwise (Wald) method. Evaluation of analytical characteristics. PLS-DA model validation. Heat map representing the hierarchical clustering of all significant differential metabolites in discovery set. Variations of three biomarkers over the TNM cancer staging. This material is available free of charge via the Internet at http://pubs.acs.org.

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REFERENCES

AUTHOR INFORMATION

Corresponding Authors

*H.W.: Phone: 86 (21) 81875361. E-mail: [email protected]. *G.X.: Tel/Fax: 0086-411-84379530. E-mail: [email protected]. Author Contributions §

J.Z. and P.Y. contributed equally.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The study has been supported by the State Key Science & Technology Project for Infectious Diseases (2012ZX10002-011, 2012ZX10002-009); the foundation (no. 21175132); and the creative research group project (no. 21321064) from National Natural Science Foundation of China, Program of Shanghai Municipal Commision of Health (XBR2013090). We thank Human Metabolome Technologies Inc. (HMT, Japan) and Agilent Technologies Inc. (USA) for their technical support.

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ABBREVIATIONS HCC, hepatocellular carcinoma; AFP, a-fetoprotein; ALT, alanine aminotransferase; AST, aspartate transaminase; γ-GT, 3430



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Metabolomics Study of Hepatocellular Carcinoma: Discovery and

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