Analysis of Urinary Metabolic Signatures of Early Hepatocellular

Jul 9, 2012 - The preoperative urinary samples were obtained the next morning after ...... Tomono , M.; Crilly , K. S.; Kiss , Z. Synergistic potentia...
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Analysis of Urinary Metabolic Signatures of Early Hepatocellular Carcinoma Recurrence after Surgical Removal Using Gas Chromatography−Mass Spectrometry Guozhu Ye,†,# Bin Zhu,‡,# Zhenzhen Yao,§,# Peiyuan Yin,† Xin Lu,† Hongwei Kong,† Fei Fan,‡ Binghua Jiao,*,§ and Guowang Xu*,† †

CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 116023, Dalian, China ‡ The Second Department of Biliary Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, 200438, Shanghai, China § Department of Biochemistry & Molecular Biology, Second Military Medical University, 200433, Shanghai, China S Supporting Information *

ABSTRACT: The objective of present study was to offer insights into the metabolic responses of hepatocellular carcinoma (HCC) to surgical resection and the metabolic signatures latent in early HCC recurrence (one year after operation). Urinary metabolic profiling employing gas chromatography time-of-flight mass spectrometry (GC-TOF MS) was utilized to investigate the complex physiopathologic regulations in HCC after operational intervention. It was revealed that an intricate series of metabolic regulations including energy metabolism, amino acid metabolism, nucleoside metabolism, tricarboxylic acid (TCA) cycle, gut floral metabolism, etc., principally leading to the direction of biomass synthesis, could be observed after tumor surgical removal. Moreover, metabolic differences between recurrent and nonrecurrent patients had emerged 7 days after initial operation. The metabolic signatures of HCC recurrence principally comprised notable up-regulations of lactate excretion, succinate production, purine and pyrimidine nucleosides turnover, glycine, serine and threonine metabolism, aromatic amino acid turnover, cysteine and methionine metabolism, and glyoxylate metabolism, similar to metabolic behaviors of HCC burden. Sixteen metabolites were found to be significantly increased in the recurrent patients compared with those in nonrecurrent patients and healthy controls. Five metabolites (ethanolamine, lactic acid, acotinic acid, phenylalanine and ribose) were further defined; they were favorable to the prediction of early recurrence. KEYWORDS: urine, metabolomics, hepatocellular carcinoma, GC−MS, surgery, metabolic signature, recurrence

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INTRODUCTION

broadly employed for intensive investigations of metabolic abnormalities in HCC and the potential biomarker discovery, besides the increasingly important roles in prognosis of cancer relapse and metastasis.6−15 Taurocholic acid, lysophosphatidylcholine 22:5 and lysophosphoethanolamine were discovered as marker metabolites for different stages of hepatocarcinogenesis by applying nontarget metabolomics to the investigation of rat HCC. Furthermore, it was revealed that patients with small liver tumor could be effectively distinguished by the marker metabolites.7 Serum metabolic profiles by ultra performance liquid chromatography−mass spectrometry (UPLC−MS) revealed that canavaninosuccinate and glycochenodeoxycholic acid were important molecules for HCC diagnosis and prognosis.6 In addition, investigations on HCC relapse and metastasis in rats using gas chromatography time-of-flight mass spectrometry

Hepatocellular carcinoma (HCC) is one of the most prevalent neoplasms worldwide especially in the eastern Asia and subSaharan Africa.1 It is characterized by a high mortality and a low 5-year survival rate, primarily due to high recurrence and metastasis rates.2 Prevention of both recurrence and metastasis could improve the efficacy of current treatment modalities and enhance survival rates. TNM staging of HCC, estimated using tumor size, tumor multiplicity, vessel invasion, lymph node metastasis, and distant metastasis is viewed as the most reliable indicator for early recurrence. However, TNM staging is often insufficient, even when combined with other established indicators.3 Therefore, discovery of biomarkers more reliably indicating the risk of recurrence is of the utmost importance. Metabolomics, as an emerging omics technique, pursues the comprehensive projection of dynamic multiple metabolic responses to biological disturbances or gene manipulations in living systems.4,5 Hence, metabolomic approach has been © 2012 American Chemical Society

Received: June 5, 2012 Published: July 9, 2012 4361

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Table 1. Clinical and Biological Information of the Subjectsa Post/Prec variables

b

TB (μmol/L) DB (μmol/L) TP (g/L) ALB (g/L) PA (mg/L) ALT (U/L) AST (U/L) γ-GT (U/L) AKP (U/L)

NRPost/NRPre

RPost/RPre

Rpost/NRPost

ratio

p

ratio

p

ratio

p

ratio

p

1.52 2.19 0.87 0.82 0.47 3.03 1.26 1.09 1.28

0.005 0.000 0.001 0.000 0.000 0.000 0.140 0.872 0.129

1.71 2.34 0.87 0.80 0.44 3.20 1.51 1.06 1.49

0.082 0.020 0.014 0.000 0.011 0.002 0.123 0.718 0.200

1.41 2.19 0.83 0.82 0.52 2.41 1.05 0.75 1.06

0.064 0.013 0.018 0.013 0.013 0.002 0.949 0.655 0.565

0.93 1.02 0.95 1.02 0.96 0.89 0.84 1.05 0.84

0.751 0.892 0.298 0.389 0.922 0.342 0.441 0.390 0.113

a Gender, male; age, 50.5 yr (median, range: 35−68 yr); maximum of tumor diameter before surgery, 93 mm (median, range: 21−180 mm); 3 patients were multinodular; 5 patients had tumor thrombus; 10 and 9 patients were in TNM stage II and III, respectively. bTB, total bilirubin; DB, direct bilirubin; TP, total protein; ALB, albumin; PA, prealbumin; ALT, alanine aminotransferase; AST, aspartate aminotransferase; γ-GT, γ-glutamyl transpeptidase; AKP, alkaline phosphatase; median was employed for comparisons. cPost, postoperative samples; Pre, preoperative samples.

all HPLC grade and obtained from Tedia Company Inc. (USA). Urease (Type III) from Canavalia ensiformis, pyridine (anhydrous), tridecanoic acid, methoxyamine hydrochloride and Nmethyl-N-(trimethylsilyl)-trifluoroacetamide (MSTFA, for GC derivatization) were all purchased from Sigma-Aldrich China Inc. (Shanghai, China). Citric acid, D-fructose, D-mannose, xylitol, Dmannitol, L-threonic acid hemicalcium salt, D-(+)-glucose, cellobiose, 2-ketoglutaric acid, L-serine, L-threonine, L-cysteine, L-phenylalanine, DL-tyrosine, 3-hydroxyphenylacetic acid, kynurenic acid, 3-hydroxy-3-methylglutaric acid, 3-hydroxyisovaleric acid, L-pyroglutamic acid, creatinine, hippuric acid, stearic acid, orotic acid, catechol, L-tryptophan, D-tagatose, D-(−)-tartaric acid, D-gluconic acid, L-arabinitol, glycolic acid, myo-inositol, ethanolamine, aconitic acid, pyruvic acid, meso-erythritol and succinic acid were obtained from Alfa Aesar China (Tianjin) Co., Ltd. (Tianjin, China). Furoylglycine, L-glyceric acid hemicalcium salt monohydrate, oxalic acid, DL-isocitric acid trisodium, hypoxanthine, xanthine, D-(+)-chiro-inositol, D-(+)-galacturonic acid monohydrate, palmitic acid, adenine, DL-lactic acid, and phydroxyphenylacetic acid were the products of Sigma-Aldrich China Inc. (Shanghai, China). L-(−)-Fucose and D-ribose were purchased from J&K Scientific Ltd. (Beijing, China), and Nacetyl-D-mannosamine monohydrate, DL-vanillomandelic acid were supplied by Ta Chem International, Inc. (Shanghai, China).

(GC-TOF MS) demonstrated the elevated glycolysis, metabolism of amino acids, nucleic acids and glucuronic acid in HCC with lung metastasis.14 On the other hand, it is inspiring from metabolomic study of recurrent breast cancer that 55% of the recurrent patients could be correctly predicted on average 13 months before the clinical diagnosis, displaying a great advantage over the current monitoring indicator of breast cancer (CA 27.29).15 Among the commonly adopted technologies, GC-TOF MS has a high efficiency of separation, which is reinforced by the deconvolution feature from ChromaTOF software, and there are various informative reference libraries available for metabolite identification. In addition, more metabolites exist in urine than in other biofluids, and most of them are hydrophilic (e.g., sugar and the analogues, amino acids, organic acids, especially short-chain metabolites), giving rise to weak retentions on the reversedphase liquid column. The hydrophilic compounds can be separated efficiently using GC capillary columns after derivatization and identified definitely on the grounds of the special mass spectral features (e.g., −COOH, −CO, −OH, polyalcohol, carbon chain), library search and reference standards verification. Moreover, the hydrophilic metabolites, such as lactate, pyruvate, alanine, glycine, serine, myo-inositol, glucose and ethanolamine are often associated with tumorigenesis.16−20 Accordingly, urinary metabolic profiling using GC-TOF MS has a great potential to study the metabolic disorders related to tumors. To the best of our knowledge, metabolomic investigations on cancer relapse were seldom, let alone HCC relapse. In this work, GC-TOF MS was applied to exhibit the metabolic responses of HCC to surgical intervention and the metabolic signatures associated with early HCC relapse (one year after operation). In order to project a visualization of the global metabolic alterations in response to surgical intervention, comparative investigations of urinary metabolic profiling were performed among preoperative, postoperative and normal subjects. In addition, differential metabolites related to HCC recurrence were discovered by pairwise comparison among subgroups of preoperative and postoperative subjects classified on the basis of recurrence information.

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Sample Collection and Preparation

Related investigations were conducted under the approval of Ethics Committee of Eastern Hepatobiliary Surgery Hospital. Afterward, 19 pairs of matched preoperative and postoperative urinary samples of patients with HCC were collected from Eastern Hepatobiliary Surgery Hospital. The preoperative urinary samples were obtained the next morning after the patients were hospitalized, and the postoperative ones were sampled the last morning before being discharged, which always happened on seventh day after the operation when the liver function was recovered. Twenty urinary samples from healthy volunteers (called normal samples) were gained at the same time. All the samples were collected in the morning under fasting overnight and stored at −80 °C immediately after collection for follow-up biochemical tests. All of the patients with HCC were diagnosed pathologically, and it was ensured that the healthy controls showed normal ultrasoinc imaging and serum biochemical parameters. The enrolled patients received the first recurrent examination one month after the hepatectomy, and then one corresponding examination

MATERIALS AND METHODS

Materials and Chemicals

Ultrapure water was produced by Milli-Q water purification system (Millipore, USA). Methanol and dichloromethane were 4362

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at intervals of 3 months. Ultimately, the time from surgery to the last recurrence diagnosis was 17−21 months. It was found that there were 7 recurrent patients, 11 nonrecurrent patients, 1 patient without clear information on whether to recur. All of the recurrent patients relapsed within 4 months. According to the recurrence information, the samples were further classified into 4 types: NRPre (preoperative patients who were nonrecurrent after operation), NRPost (postoperative patients who were nonrecurrent after operation), RPre (preoperative patients who were recurrent after operation) and RPost (postoperative patients who were recurrent after operation) for further discovery of metabolic characteristics of HCC relapse and the effects of surgical intervention. Other clinical information is given in Table 1. After urinary samples were thawed at room temperature, 150 μL of urease solution (10 mg/mL, dissolved in ultrapure water) was added to 100 μL of urine, and vortex-mixed for 10 s, then held in the water bath at 37 °C for 15 min. Afterward, 1000 μL of cold methanol (containing 40 μg/mL tridecanoic acid, working as the internal standard) was added to the urinary incubation solution to inactivate the urease and extract metabolites, and then the solution was vortex-mixed for 30 s. Thereafter the solution was centrifuged in the conditions of 15000g, 4 °C for 15 min. After that, 1000 μL of supernatant was transferred to a new Eppendorf tube for following vacuum-dried in CentriVap centrifugal vacuum concentrators (Labconco, USA). The dried sample was dissolved in 100 μL of methoxyamine solution (20 mg/mL in pyridine), and then under ultrasound treatment for 15 min at room temperature to dissolve as many metabolites as possible. Subsequently, the sample was placed in water bath at 40 °C for 2 h oximation reaction followed by silylation reaction with 80 μL of MSTFA also in water bath at 40 °C for 1 h. Ultimately, the derivatized samples were centrifuged again at 15000g for 15 min at 4 °C to discard the compounds insoluble in the derivatization solution, avoiding producing unrepeatable results and making the syringe for GC injection blocked. Then the supernatant was transferred to a conical insert in 2-mL glass vial for subsequent GC-TOF MS analysis.

repeatability and reproducibility of the methods. The samples were randomly coded and processed alternately according to the sample types. A QC sample was run every 9 samples to monitor the reproducibility and stability of the method. On the other hand, a blank sample (dichloromethane solution) at intervals of 10 samples (QC samples included) was run as well for the elution of residual analytes and impurities primarily from the glass liner and the capillary column. All the derivatized samples were analyzed within 24 h. A light diesel sample was injected to obtain the retention times of n-alkanes for calculating the Kovat’s retention index of metabolites instantly after running all the samples. Data Processing

The acquired MS data were first transformed into netCDF format by ChromaTOF 3.25 prior to data pretreatment using XCMS for ion peaks filtration and identification, peaks matching, retention time alignment, filling missing peaks, and so on.22 A data set containing 3870 ion peaks was generated from XCMS processing data of urinary metabolic profiling. Thereafter, the ion peak areas of the metabolites were divided by total ion peak areas, multiplied by 1 × 107, and then the data were applied for further data processing. Artifactual ion peaks, known from column bleed, derivatization solvents or reagents, and peaks with RSD (relative standard deviation) of the area in 7 QC samples higher than 30% were all removed from the data set. Moreover, among the ion peaks originated from the same GC chromatographic peak, only the unique ion peak provided by ChromaTOF 3.25 was retained. As a result, there were 233 ions were kept in the data set. At the point, one ion denoted a unique GC chromatographic peak after deconvolution processing. Nonparametric test (Mann−Whitney U test) was employed for the comparison analysis of different groups of samples using PASW Statistics 18 (SPSS Inc., Chicago, USA). The differential ions were defined on the basis of p value (bilateral asymptotic significance, p < 0.05), and further mined for the postoperative pathophysiologcial alterations of HCC and the metabolic signatures related to recurrence. Binary logistic regression was also carried out employing PASW Statistics 18, and forward selection (conditional) and enter method were used for variable selection in the model. Heat map analysis was conducted using MultiExperiment Viewer v.4.7.4.23,24 Metabolomic data normalization was usually necessary and had been done prior to statistical analysis and pathway analysis by means of MetaboAnalyst (a web service for metabolomic data analysis).25 There are 2 major types of data normalization provided by the MetaboAnalyst to make the data follow the Gaussian distribution as closely as possible. The parameters of data normalization could be adjusted timely, since the results of data normalization can be visualized with kernel density plots and box plots of the data distributions. Row-wise normalization (samples in row) aims to reduce systematic bias from samples, while column-wise normalization (variables in column) attempts to make each variable comparable to others from the same sample.26 Principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), correlation analysis, and pathway analysis were performed via MetaboAnalyst. In PLS-DA model, metabolite ions with VIP (variable importance in the projection) > 1.0 were selected as differential ions beneficial to the phenotype classification in the study, and a 1000 permutation test was implemented to validate the reliability of the model because of its propensity to overestimation of the separation performance, which could be inspected by permutation tests, but not always by

GC-TOF MS Analysis

The procedures and parameters of GC-TOF MS were similar to our previous study with a few modifications.21 Briefly, 1 μL of the derivatization sample was injected by Agilent 7683B autosampler (Agilent Palo Alto, CA, USA) followed by the analysis employing Agilent 6890N GC coupled with Pegasus 4D TOF MS (Leco Corporation, St. Joseph, MI, USA). Helium (99.9995%) was used as the carrier gas in the constant flow rate of 1.2 mL/min through the column, and the split ratio was 10:1. The separation of metabolites was achieved via a DB-5 MS capillary column (30 m × 250 μm × 0.25 μm) obtained from J&W Scientific Inc. (Folsom, CA, USA). The temperatures of inlet, transfer interface and ion source were set at 300, 280, and 230 °C, respectively. The oven temperature program was set as follows: initially kept at 70 °C for 3 min, increased at the rate of 4 °C/min to 220 °C, then ramped to 300 at 8 °C/min, held for 10 min. The voltage of detector was set at 1600 V, and the ionization mode of metabolites was electron impaction (EI, −70 eV). Mass signals were acquired by ChromaTOF software 3.25 (Leco Corporation, USA) in full scan mode (m/z: 33−600) after 400 s solvent delay at a scan rate of 5 spectra/s. The quality control (QC) samples were prepared by mixing equal aliquots of all the 58 analytical samples, then treated and analyzed in the same way as other samples to investigate the 4363

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Figure 1. PCA and pathway analysis of the samples. (A) PCA plot of all the samples based on 233 metabolic features in ultimate data set, and 95% confidence ellipses of the sample distributions were displayed; (B) pathway analysis of preoperative and postoperative samples based on 71 pathwaymatched metabolites; (C) PCA plot of the samples based on 71 pathway-matched metabolites. Pre, preoperative samples; post, postoperative samples; normal, normal samples; black circle, the patient without clear recurrence information in postoperative status.

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cross-validation.26,27 In addition, Correlation analysis was executed against defined sample types for screening the metabolites with specific alterations for postoperative physiology, where p-value threshold was set to 0.05. On the other hand, pathway analysis combining the modules of pathway enrichment analysis with pathway topology analysis was employed to discover the most biologically vital and correlative pathways involved in the metabolic modulations under investigation. Identification of metabolites was completed in the light of our previous work.21,28 Shortly, the differential ions were identified by the library search (consisting of NIST05, Wiley, Replib and Fiehn) and the available standard confirmation based on the information of mass spectra, retention time and Kovat’s retention index. Moreover, the identified metabolites were retrieved in HMDB (Human Metabolome Database, http://www.hmdb.ca/ ) for auxiliary confirmation of structures and the biological functions.

RESULTS AND DISCUSSION

Analytical Performance of Urinary Metabolic Profiling

A derivatized QC sample was initially prepared and run 5 times successively to balance and stabilize the instrumental system prior to the beginning of analytical sample runs. It was manifested that peak area RSD of 3503 ion peaks (3624 peaks in all) in the 5 repeated injections of the same QC sample was below 30%. Therefore, there was a confidence in good performance and high suitability of GC-TOF MS analytical system. It was clear that 7 QC samples were located closely to each other, nearly in the center of the analytical samples (Figure 1A). Moreover, RSDs of the contents (normalized to total peak areas) of 3561 ion peaks among the 3870 peaks generated from XCMS were less than 30%. Hence, the reproducibility and stability of global experimental performances were high and acceptable, and that the variances from the samples were likely the real reflections of metabolic differences underlying the biological systems rather than the artifactual biases from sample pretreatment and/or instrumental analysis.29 4364

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Figure 2. Heat map of 96 differential metabolites listed in Table S1 (Supporting Information). The ratio of metabolite in the subject to average of those in normal samples was first calculated, and then the metabolic alteration was demonstrated as log10 (ratio). Most of the metabolites in panel A were down-regulated after surgery compared with the tumor condition, while a majority of metabolites in panel B were up-regulated after surgery in comparison to the preoperative status. Furthermore, the downregulations of metabolites in panel C and upregulations of metabolites in panel B in preoperative status relative to healthy controls were more evident after surgical resection.

Differential Metabolite Discovery and Identification

were identified and verified on the basis of the method described above. Eventually, 96 of 164 differential ion peaks obtained from 6 pairwise comparisons of the samples (p < 0.05) were identified and listed in Table S1 (Supporting Information), among them, 58 derivatized peaks from 53 metabolites were verified by reference standards, and 77 derivatized peaks from 71

The typical total ion current chromatograms of normal, RPre, RPost, NRPre and NRPost samples are presented in Figure S1 (Supporting Information). On the basis of the metabolic profiling, pairwise comparisons of different groups were carried out to define the differential metabolites. Differential metabolites 4365

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Figure 3. Box plots of metabolites in carbohydrate metabolism, TCA cycle, and gut floral metabolism. (A) Lactic acid, (B) galactose, (C) ribose, (D) gluconic acid, (E) fucose, (F) mannitol, (G) tagatose, (H) cellobiose, (I) citric acid, (J) isocitric acid, (K) aconitic acid, (L) succinic acid, (M) hippuric acid, (N) 3-hydroxyhippuric acid, (O) 4-hydroxyhippuric acid, (P) p-cresol.

Global Metabolic Regulations of HCC in Response to Surgical Intervention

metabolites were compared and matched with the compounds in the pathway libraries of MetaboAnalyst, which was necessary for following pathway analysis. Because of the existence of isomers and the structural similarities among saccharides, 11 saccharide (e.g., sugars, saccharic acids, sugar alcohols, sugar lactones, amino sugars, deoxysugars and cyclitols, labeled with saccharides 1−11) derivatives could be initially identified well by library matches (similarity ≥750). Additionally, each of L-(−)-fucose, D(+)-galacturonic acid, D-fructose, L-threonine, uric acid and Nacetyl-D-mannosamine had two derivative GC peaks (labeled with 1 or 2).

It was evident in PLS-DA model that postoperative samples could be separated from preoperative samples and normal samples, respectively (Figure S2, Supporting Information). Moreover, it was obvious that B/W ratio (ratio of sum of squares between groups to that within group) of the original classes (pointed out by red arrow) was markedly different from the distribution of permuted data (p < 0.05, Figure S2, Supporting Information), indicating that the good separation performance was achieved by analyzing real metabolic signals but not random noises, and the results of cross validation were reliable. Furthermore, it was found from pathway analysis that 4366

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Figure 4. Box plots of metabolites in nucleoside metabolism, glycine, serine and threonine metabolism, cysteine and glutathione metabolism, and others. (A) Adenine; (B) hypoxanthine; (C) xanthine; (D) uric acid; (E) orotic acid; (F) glycine; (G) cysteine; (H) pyroglutamic acid; (I) serine; (J) threonine; (K) creatinine; (L) ethanolamine.

hand, a subet of metabolites in zone A (zone C), mainly involved in TCA cycle, intestinal floral metabolism, etc., were all decreased in preoperative status compared with the controls, and the downregulations were more evident after operation. It might be concluded from the metabolic alterations of metabolites in key pathways and the metabolic behaviors of HCC that anabolism was still vigorous 7 days after surgical intervention, which could be attributed to de novo synthesis of biomass for the repair of biological systems to prevent further trauma, initiation of liver regeneration, apart from the aggressively biosynthetic behavior of newly formed cancer cells in patients with recurrence.

metabolic regulations owing to surgical intervention were chiefly involved in glycine, serine and threonine metabolism, tricarboxylic acid (TCA) cycle, glyoxylate and dicarboxylate metabolism, aromatic amino acid metabolism, cysteine and methionine metabolism, pyruvate metabolism, purine metabolism and carbohydrate metabolism (Figure 1B). Metabolic alterations of 96 identified metabolites after operational intervention were presented in Figure 2 and Table S1 (Supporting Information); their related pathways could be referred to Figures S3 and S4 (Supporting Information). According to the clustering result of 96 differential metabolites, the distribution of metabolite clustering could be visually divided into zone A and B, where metabolites were arranged regularly after surgery. It could be observed that a large proportion of metabolites in zone A were reduced after surgery in comparison to the preoperative status; additionally, most of metabolites in zone B were increased after operation compared with the preoperative status (Figure 2). Furthermore, it was clear that most of the metabolites in zone B (principally involved in glycine, serine and threonine metabolism, cysteine and methionine metabolism, aromatic amino acid metabolism, purine metabolism, pyruvate metabolism, and so on) were all elevated in the preoperative conditions in comparison to healthy controls, and the up-regulations were more obvious after surgery. On the other

Metabolic Signatures Associated with HCC Recurrence

From PCA score plot of 71 pathway-matched metabolites, which were utilized for pathway analysis above, it can be seen that the metabolic profiling of recurrent patients differed from that of nonrecurrent patients (Figure 1C). Subsequently, metabolic signatures related with HCC recurrence were exploited in more detail in following sections. Carbohydrate Metabolism and Nucleoside Metabolism. Carbohydrate metabolism (galactose metabolism, inositol phosphate metabolism, pentose phosphate pathway, fructose and mannose metabolism, starch and sucrose metabolism, etc.), playing a vital role in meeting the biosynthetic and bioenergetic demands of tumor cells, was involved in the key pathways 4367

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Figure 5. Box plots of aromatic amino acids and the products, and organic acids. (A) Phenylalanine, (B) tyrosine, (C) sum of phenylalanine and tyrosine, (D) ratio of tyrosine to phenylalanine, (E) tryptophan, (F) vanylglycol, (G) vanillylmandelic acid, (H) p-hydroxyphenylacetic acid, (I) 3hydroxyphenylacetic acid, (J) 3-hydroxyisovaleric acid, (K) 3-hydroxy-3-methylglutarte, (L) 3-methyl-2-oxovaleric acid, (M) 2,4-dihydroxybutanoic acid, (N) 3,4-dihydroxybutanoic acid, (O) oxalic acid, (P) tartaric acid.

growth and proliferation of cancer cells in recurrent patients,17 and the acidic microenvironment led by lactic acid excretion would in turn promote the invasive behaviors of cancer cells, as demonstrated in human melanoma cells.30 TCA Cycle. Citric acid, isocitric acid and aconitic acid (Figure 3) all decreased markedly in nonrecurrent patients after operation compared with the recurrent patients (NRPost vs RPost, p < 0.05), also went down in nonrecurrent patients after surgery in comparison to the preoperative status (NRPost vs NRPre), but went up in recurrent patients (RPost vs RPre). Moreover, succinic acid (Figure 3) was significantly higher in recurrent patients after surgery (RPost vs RPre, p = 0.046), while

induced by surgical intervention. Notable increases of ribose, gluconic acid, adenine, hypoxanthine and orotic acid in recurrent patients after surgery compared with those in nonrecurrent patients (RPost vs NRPost, p < 0.05, Figures 3 and 4) were probably related with enhanced de novo synthesis of nucleotide and other biomass for aggressively proliferative tumor cells in patients with recurrence. In addition, the increase of lactic acid (p < 0.05) and decrease of glucose in recurrent patients (RPost vs NRPost, Figure 3 and Table S1, Supporting Information) were significant, which could be attributed to the high-rate exploitation of glucose to lactic acid in glycolysis instead of oxidative phosphorylation to provide sufficient energy for rapid 4368

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occurred in recurrent patients after surgery (RPost vs RPre, p = 0.027, 0.059 and 0.046, respectively), but did not happen in nonrecurrent patients (NRPost vs NRPre, p = 0.491, 0.250 and 0.082, separately), which might indicate that obvious increases of aromatic amino acids in the postoperative status had a relationship with relapse, and it is notable that tyrosine and tryptophan could be metabolized to hormones and neurotransmitters, such as seronine, noradrenaline, dopamine, catecholamines, and adrenaline. On the other hand, ratio of tyrosine to phenylalanine (Figure 5) was markedly lower in recurrent patients (RPost vs RPre, p = 0.009), while higher in nonrecurrent patients (NRPost vs NRPre), which demonstrated the significant decrease of phenylalanine hydroxylase activity and/or the increased degradation of tyrosine in recurrent patients. Besides, remarkable increments of vanylglycol, vanillylmandelic acid and p-hydroxyphenylacetic acid in recurrent patients (RPost vs NRPost, p < 0.05) aided the demonstration of up-regulated breakdown of tyrosine in patients with relapse. Moreover, enhancements of 3-hydroxyisovaleric acid (from leucine), 3-hydroxy-3-methylglutarte (from leucine) and 3methyl-2-oxovaleric acid (the precursor of isoleucine) in recurrent patients after surgery (Figure 5) revealed enhanced metabolism of branched-chain amino acids (BCAAs) in relapsed patients. The ratio of BCAAs (leucine, valine and isoleucine) to aromatic amino acids (phenylalanine and tyrosine) correlates with liver metabolic status, dysfunction and functional reserve.35 Taken together, alterations of above-mentioned amino acids and the catabolites indicated not only higher turnover of amino acids, but also more materials for the replenishment of TCA cycle substrates in the form of acetyl-COA, succinyl-COA, fumarate or other metabolites in patients with recurrence. Glyoxylate Metabolism. Ethanolamine and oxalate were remarkably up-regulated in recurrent patients after surgery compared with nonrecurrent patients (RPost vs NRPost, p < 0.05, Figures 4 and 5). Moreover, ethanolamine was markedly increased in recurrent patients (RPost vs RPre, p = 0.016), while decreased in nonrecurrent patients (NRPost vs NRPre, p = 0.450). 4-Hydroxyproline metabolism, generated from collagen renewal and/or dietary sources, supports the production of glycolate, oxalate and glycine, and performs roles on the inhibitions of cell metabolic activity and proliferation.36−38 Obvious up-regulations of oxalate and glycolate in recurrent patients after surgery (RPost vs NRPost, p = 0.042 and 0.077, respectively, Figure 5 and Table S1, Supporting Information) evinced the perturbation of 4-hydroxyproline metabolism and higher risk of calcium oxalate stones in recurrent condition, apart from the enhanced purine metabolism. On the other hand, ethanolamine is a basic component of phosphatidylethanolamine, which is a typical and abundant phospholipid in mammalian cell membrane, thus exerting profound effects on the conformation and function of membranes.18,39 Elevation of ethanolamine, often occurring in tumors and carcinogen-treated cells,40 suggested the accelerative turnover of cell membrane in the case of relapse. Intestinal Floral Metabolism. Hippurate, 3-hydroxyhippurate, 4-hydroxyhippurate, p-cresol and catechol, related to intestinal floral metabolism, all went down in postoperative patients compared with the preoperative status. The phenomenon could be attributed to the suppression of intestinal floral metabolism by surgical trauma and/or oral antimicrobial drugs, and the later factor had a great effect on the metabotype of gut flora.41 Moreover, it should be noted that reductions of

there was no prominent differences in nonrecurrent patients (NRPost vs NRPre, p = 0.577). Remarkable decrements of citric acid, isocitric acid and aconitic acid in nonrecurrent patients after surgery illustrated that the energy supply system in patients without recurrence was recovered to some degree, as it was more dependent on TCA cycle in nonrecurrent patients than in patients with recurrence for bioenergy, in addition to biomaterials of macromolecules for the biological repair after operation. The notable accumulation of succinate in recurrent patients, probably due to succinate dehydrogenase (SDH) inhibition, could repress the activity of hypoxia-inducible factor (HIF) prolyl hydroxylases, thus giving rise to the stabilization and activation of HIF-1α, which was involved in the activation of various genes regulating cell survival, invasion, angiogenesis, glycolysis, tumor formation and growth.31−33 Glycine, Serine and Threonine Metabolism and Related Pathways. It was observed in Figure 4 that glycine and threonine in recurrent patients increased significantly after operation (RPost vs RPre, p = 0.004 and 0.003, respectively), but the increases in nonrecurrent patients were not significant (NRPost vs NRPre, p = 0.818 and 0.108, separately). Meanwhile, noteworthy increases of glycine, cysteine and pyroglutamic acid were observed in recurrent patients after surgery compared with those in nonrecurrent patients (RPost vs NRPost, p = 0.052, 0.042 and 0.026, respectively). In addition, serine was obviously up-regulated in nonrecurrent patients after surgery (NRPost vs NRPre, p = 0.053), but there was no obvious up-regulation in recurrent patients (RPost vs RPre, p = 0.208). Serine, synthesized from 3-phosphoglycerate in glycolytic pathway, can be utilized for biosynthesis of other intermediates in glycine, serine and threonine metabolism (glycine, threonine, tryptophan, and cysteine), protein, sphingolipid, purine and pyrimidine.19,20 Additionally, it was reported that serine synthesis accounted for approximately 50% of the total replenishments for TCA cycle intermediates by virtue of converting glutamate into 2-oxoglutarate in breast cancer cells with high expression of phosphoglycerate dehydrogenase.19 Thus, it could be inferred that up-regulations of intermediates in glycine, serine and threonine metabolism were highly correlated with enhanced glycolysis and replenishment of TCA cycle intermediates, and that increased levels of glycine and serine were in accordance with notable increments of purine and pyrimidine nucleoside renewals occurring in patients with relapse. Despite the fact that glycine, serine and threonine metabolism was greatly disturbed, the direct and significant impacts of tumor on serine and the downstream sarcosine in urine were not observed, which deserved further investigation. On the other hand, glycine and cysteine are essential amino acids for glutathione synthesis; nevertheless, pyroglutamic acid serves as a catalyst for catabolism of glutathione into the component amino acids (glycine, cysteine and glutamic acid) via γ-glutamyl cycle where homeostasis of glutathione was modulated, and glutathione played pivotal roles in maintaining the redox state, regulating the immune system, and removing carcinogens.34 Accordingly, the remarkable increases of glycine, cysteine and pyroglutamic acid in recurrent patients compared with nonrecurrent patients reflected the reinforced renewal of glutathione, indicative of more oxidative stress in patients with relapse. Aromatic Amino Acid, Branched-Chain Amino Acid Metabolism. It was noteworthy in Figure 5 that evident upregulations of phenylalanine, tyrosine and tryptophan only 4369

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Figure 6. Metabolic signatures associated with recurrence. (A) Pathways related with recurrence. Red color, significantly up-regulated in RPost compared with NRPost and the control; □, differential metabolites listed in Table S1 (Supporting Information). (B), (C), and (D) Early prediction of recurrence employing binary logistic regression based on 16 metablites listed in (A) (RPost vs NRPost). Sixteen metabolites were all enrolled in (B) using enter method for varibale selection. The significance level for variable entry and removal was 0.15 and 0.10 in (C) and 0.20 and 0.15 in (D), respectively. Cutoff value was set at 0.5.

hippurate, 3-hydroxyhippurate, 4-hydroxyhippurate and p-cresol (Figure 3) were marked in nonrecurrent patients (NRPost vs NRPre, p = 0.003, 0.001, 0.003, and 0.033, respectively), while notable differences in recurrent patients were not found (RPost vs RPre, p = 0.093, 0.345, 0.462 and 0.294, separately). Protein, aromatic amino acids and dietary polyphenols could be metabolized by intestinal flora.42−44 Thereby, it could be concluded from variances on down-regulations of metabolites relative to gut floral metabolism between patients with recurrence and those without recurrence that there were discrepancies on utilizations of protein and aromatic amino acids between recurrent and nonrecurrent patients.

patients before surgery, and then changed toward normal levels after the effective surgical intervention. Metabolite Combination with the Potential for Predicting Early HCC Recurrence

An overview of the metabolic signatures 7 days after the hepatectomy related to recurrence was projected in Figure 6A and Figure S5 (Supporting Information). In the case of relapse, glycolysis, glycine, serine and threonine metabolism, nucleoside turnover, cysteine and methionine metabolism, glyoxylate metabolism, aromatic amino acid metabolism, and succinate secretion are evidently up-regulated, similar to the metabolic alterations associated with HCC proliferation. Among the metabolites related to relapse, 16 metabolites (red font in Figure 6A) were found to be enhanced remarkably in recurrent patients after surgery compared with nonrecurrent patients (RPost vs NRPost, p < 0.05) and the controls (RPost vs normal, p < 0.05), which would benefit the diagnosis and early prediction of HCC recurrence. Binary logistic regression, an effective tool for the discrimination of HCC from other liver diseases,7 was introduced for the early prediction of HCC recurrence. It was proved that 16 metabolites were favorable to

Surgical Effects

Because of the tremendous impacts of surgical intervention on the physiopathology of HCC 7 days after the operation, it is difficult to obtain a direct and comprehensive projection of the positively surgical effects. However, it was encouraging that adenine, hypoxanthine, orotate, ribose, gluconic acid, ethanolamine, vanylglycol, vanillylamadelic acid and 3-hydroxy-3methylglutarte (Figures 3−5) were all increased in nonrecurrent 4370

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Author Contributions

the diagnosis of HCC recurrence, recurrent and nonrecurrent patients were all accurately diagnosed (Figure 6B). For the clinical application, 16 biomarkers are too many; therefore, the significance levels for variable entry and removal were further adjusted to reduce the number of potential biomarkers. When a significance level of 0.15 and 0.10 was set for varibale entry and removal, respectively, in the model of binary logistic regression, lactic acid and acotinic acid were selected for early prediction of recurrence, which indicated that 1 of 7 recurrent patients was misclassified, and 11 nonrecurrent patients were all correctly classified (Figure 6C). Furthermore, when the significance level for variable entry and removal were respectively adjusted to 0.20 and 0.15, ethanolamine, lactic acid, acotinic acid, phenylalanine and ribose were defined as the combinational biomarkers, and recurrent and nonrecurrent patients were all correctly recognized (Figure 6D). They can be utilized for diagnosis and prediction of early HCC relapse in the study and have the potential to be generalized.

#

These authors contributed equally to this work.

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 (2012ZX10002011, 2012ZX10002009), the Key Foundation (No. 20835006), and the creative research group project (No. 21021004) from National Natural Science Foundation of China. This study has also been supported by a grant from the Natural Science Foundation of Shanghai (09ZR1400900).

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CONCLUSIONS A complicated series of metabolic regulations (energy metabolism, amino acid metabolism, nucleoside metabolism, redox homeostasis, etc.) were stimulated by surgical intervention, which could be visualized by urinary metabolic profiling employing GC-TOF MS. It was revealed from urinary metabolic profiling of preoperative and postoperative patients that anabolism was still vigorous 7 days after the hepatectomy, and that the metabolic status of postoperative patients could be effectively identified by the metabolites in key pathways. Moreover, metabolic status of recurrent patients 7 days after surgery differed from that of postoperative patients without recurrence. The metabolic signatures of recurrence were characterized by significant up-regulations of glycolysis, nucleoside turnover, glycine, serine and threonine metabolism, aromatic amino acid turnover, cysteine and methionine metabolism, glyoxylate metabolism, and succinate accumulation. Additionally, accelerated utilization of TCA cycle intermediates and obviously less production of metabolites in gut floral metabolism were also observed in postoperative status whether or not with recurrence compared with healthy controls. Among the metabolites concerned with relapse, 16 metabolites could be mined for the reflection of metabolic differences between recurrent and nonrecurrent patients and the evaluation of surgical effects as well. Five metabolites (ethanolamine, lactic acid, acotinic acid, phenylalanine and ribose) were defined as the combinational biomarkers to recognize recurrence and nonrecurrence on the basis of their urinary contents 7 days after the hepatectomy, although further large sample validation is needed.

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ASSOCIATED CONTENT

S Supporting Information *

Table S1 and Figures S1−S5. This material is available free of charge via the Internet at http://pubs.acs.org.

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REFERENCES

(1) El-Serag, H. B.; Rudolph, L. Hepatocellular carcinoma: epidemiology and molecular carcinogenesis. Gastroenterology 2007, 132 (7), 2557−2576. (2) Forner, A.; Llovet, J. M.; Bruix, J. Hepatocellular carcinoma. Lancet 2012, 379 (9822), 1245−1255. (3) Minguez, B.; Lachenmayer, A. Diagnostic and prognostic molecular markers in hepatocellular carcinoma. Dis. Markers 2011, 31 (3), 181−190. (4) Nicholson, J. K.; Lindon, J. C.; Holmes, E. “Metabonomics”: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999, 29 (11), 1181− 1189. (5) Chan, E. C. Y.; Pasikanti, K. K.; Nicholson, J. K. Global urinary metabolic profiling procedures using gas chromatography-mass spectrometry. Nat. Protoc. 2011, 6 (10), 1483−1499. (6) Wang, B.; Chen, D.; Chen, Y.; Hu, Z.; Cao, M.; Xie, Q.; Chen, Y.; Xu, J.; Zheng, S.; Li, L. Metabonomic profiles discriminate hepatocellular carcinoma from liver cirrhosis by ultraperformance liquid chromatography-mass spectrometry. J. Proteome Res. 2012, 11 (2), 1217−1227. (7) Tan, Y.; Yin, P.; Tang, L.; Xing, W.; Huang, Q.; Cao, D.; Zhao, X.; Wang, W.; Lu, X.; Xu, Z.; Wang, H.; Xu, G. Metabolomics study of stepwise hepatocarcinogenesis from the model rats to patients: potential biomarkers effective for small hepatocellular carcinoma diagnosis. Mol. Cell. Proteomics 2012, 11 (2), M111.010694. (8) Shariff, M. I. F.; Gomaa, A. I.; Cox, I. J.; Patel, M.; Williams, H. R. T.; Crossey, M. M. E.; Thillainayagam, A. V.; Thomas, H. C.; Waked, I.; Khan, S. A.; Taylor-Robinson, S. D. Urinary metabolic biomarkers of hepatocellular carcinoma in an Egyptian population: a validation study. J. Proteome Res. 2011, 10 (4), 1828−1836. (9) Patterson, A. D.; Maurhofer, O.; Beyoglu, D.; Lanz, C.; Krausz, K. W.; Pabst, T.; Gonzalez, F. J.; Dufour, J.-F.; Idle, J. R. Aberrant lipid metabolism in hepatocellular carcinoma revealed by plasma metabolomics and lipid profiling. Cancer Res. 2011, 71 (21), 6590−6600. (10) Chen, T.; Xie, G.; Wang, X.; Fan, J.; Qiu, Y.; Zheng, X.; Qi, X.; Cao, Y.; Su, M.; Wang, X.; Xu, L. X.; Yen, Y.; Liu, P.; Jia, W. Serum and urine metabolite profiling reveals potential biomarkers of human hepatocellular carcinoma. Mol. Cell. Proteomics 2011, 10 (7), M110.004945. (11) Shariff, M. I. F.; Ladep, N. G.; Cox, I. J.; Williams, H. R. T.; Okeke, E.; Malu, A.; Thillainayagam, A. V.; Crossey, M. M. E.; Khan, S. A.; Thomas, H. C.; Taylor-Robinson, S. D. Characterization of urinary biomarkers of hepatocellular carcinoma using magnetic resonance spectroscopy in a Nigerian population. J. Proteome Res. 2010, 9 (2), 1096−1103. (12) Wu, H.; Xue, R. Y.; Dong, L.; Liu, T. T.; Deng, C. H.; Zeng, H. Z.; Shen, X. Z. Metabolomic profiling of human urine in hepatocellular carcinoma patients using gas chromatography/mass spectrometry. Anal. Chim. Acta 2009, 648 (1), 98−104. (13) Wang, J.; Zhang, S.; Li, Z.; Yang, J.; Huang, C.; Liang, R.; Liu, Z.; Zhou, R. (1)H-NMR-based metabolomics of tumor tissue for the

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*(G.X.) Tel./Fax: +86-411-84379530. E-mail: [email protected]. (B.J.) Tel.: +86-21-65493936. Fax: 0086-21-65334344. E-mail: [email protected]. 4371

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metabolic characterization of rat hepatocellular carcinoma formation and metastasis. Tumor Biol. 2011, 32 (1), 223−231. (14) Li, Z.-F.; Wang, J.; Huang, C.; Zhang, S.; Yang, J.; Jiang, A.; Zhou, R.; Pan, D. Gas chromatography/time-of-flight mass spectrometrybased metabonomics of hepatocarcinoma in rats with lung metastasis: elucidation of the metabolic characteristics of hepatocarcinoma at formation and metastasis. Rapid Commun. Mass Spectrom. 2010, 24 (18), 2765−2775. (15) Asiago, V. M.; Alvarado, L. Z.; Shanaiah, N.; Gowda, G. A. N.; Owusu-Sarfo, K.; Ballas, R. A.; Raftery, D. Early detection of recurrent breast cancer using metabolite profiling. Cancer Res. 2010, 70 (21), 8309−8318. (16) Griffin, J. L.; Shockcor, J. P. Metabolic profiles of cancer cells. Nat. Rev. Cancer 2004, 4 (7), 551−561. (17) Vander Heiden, M. G.; Cantley, L. C.; Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 2009, 324 (5930), 1029−1033. (18) Mintz, A.; Wang, L.; Ponde, D. E. Comparison of radiolabeled choline and ethanolamine as probe for cancer detection. Cancer Biol. Ther. 2008, 7 (5), 742−747. (19) Possemato, R.; Marks, K. M.; Shaul, Y. D.; Pacold, M. E.; Kim, D.; Birsoy, K.; Sethumadhavan, S.; Woo, H.-K.; Jang, H. G.; Jha, A. K.; Chen, W. W.; Barrett, F. G.; Stransky, N.; Tsun, Z.-Y.; Cowley, G. S.; Barretina, J.; Kalaany, N. Y.; Hsu, P. P.; Ottina, K.; Chan, A. M.; Yuan, B.; Garraway, L. A.; Root, D. E.; Mino-Kenudson, M.; Brachtel, E. F.; Driggers, E. M.; Sabatini, D. M. Functional genomics reveal that the serine synthesis pathway is essential in breast cancer. Nature 2011, 476 (7360), 346−350. (20) Fu, T. F.; Rife, J. P.; Schirch, V. The role of serine hydroxymethyltransferase isozymes in one-carbon metabolism in MCF-7 cells as determined by C-13 NMR. Arch. Biochem. Biophys. 2001, 393 (1), 42−50. (21) Lin, X.; Zhang, Y.; Ye, G.; Li, X.; Yin, P.; Ruan, Q.; Xu, G. Classification and differential metabolite discovery of liver diseases based on plasma metabolic profiling and support vector machines. J. Sep. Sci. 2011, 34 (21), 3029−3036. (22) Smith, C. A.; Want, E. J.; O’Maille, G.; Abagyan, R.; Siuzdak, G. XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 2006, 78 (3), 779−787. (23) Saeed, A. I.; Hagabati, N. K.; Braisted, J. C.; Liang, W.; Sharov, V.; Howe, E. A.; Li, J.; Thiagarajan, M.; White, J. A.; Quackenbush, J. TM4 microarray software suite. Method Enzymol 2006, 411, 134−193. (24) Saeed, A. I.; Sharov, V.; White, J.; Li, J.; Liang, W.; Bhagabati, N.; Braisted, J.; Klapa, M.; Currier, T.; Thiagarajan, M.; Sturn, A.; Snuffin, M.; Rezantsev, A.; Popov, D.; Ryltsov, A.; Kostukovich, E.; Borisovsky, I.; Liu, Z.; Vinsavich, A.; Trush, V.; Quackenbush, J. TM4: a free, opensource system for microarray data management and analysis. BioTechniques 2003, 34 (2), 374−378. (25) Xia, J.; Psychogios, N.; Young, N.; Wishart, D. S. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res. 2009, 37, W652−W660. (26) Xia, J.; Wishart, D. S. Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat. Protoc. 2011, 6 (6), 743−760. (27) Westerhuis, J. A.; Hoefsloot, H. C. J.; Smit, S.; Vis, D. J.; Smilde, A. K.; van Velzen, E. J. J.; van Duijnhoven, J. P. M.; van Dorsten, F. A. Assessment of PLSDA cross validation. Metabolomics 2008, 4 (1), 81− 89. (28) Li, X.; Xu, Z.; Lu, X.; Yang, X.; Yin, P.; Kong, H.; Yu, Y.; Xu, G. Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry for metabonomics: Biomarker discovery for diabetes mellitus. Anal. Chim. Acta 2009, 633 (2), 257−262. (29) Gika, H. G.; Theodoridis, G. A.; Wingate, J. E.; Wilson, I. D. Within-day reproducibility of an HPLC−MS-based method for metabonomic analysis: application to human urine. J. Proteome Res. 2007, 6 (8), 3291−3303.

(30) MartinezZaguilan, R.; Seftor, E. A.; Seftor, R. E. B.; Chu, Y. W.; Gillies, R. J.; Hendrix, M. J. C. Acidic pH enhances the invasive behavior of human melanoma cells. Clin. Exp. Metastasis 1996, 14 (2), 176−186. (31) Briere, J.-J.; Favier, J.; Gimenez-Roqueplo, A.-P.; Rustin, P. Tricarboxylic acid cycle dysfunction as a cause of human diseases and tumor formation. Am. J. Physiol.: Cell Physiol. 2006, 291 (6), C1114− C1120. (32) Maxwell, P. H.; Dachs, G. U.; Gleadle, J. M.; Nicholls, L. G.; Harris, A. L.; Stratford, I. J.; Hankinson, O.; Pugh, C. W.; Ratcliffe, P. J. Hypoxia-inducible factor-1 modulates gene expression in solid tumors and influences both angiogenesis and tumor growth. Proc. Natl. Acad. Sci. U. S. A. 1997, 94 (15), 8104−8109. (33) Semenza, G. L. Targeting HIF-1 for cancer therapy. Nat. Rev. Cancer 2003, 3 (10), 721−732. (34) Balendiran, G. K.; Dabur, R.; Fraser, D. The role of glutathione in cancer. Cell Biochem. Funct. 2004, 22 (6), 343−352. (35) Ishikawa, T. Branched-chain amino acids to tyrosine ratio value as a potential prognostic factor for hepatocellular carcinoma. World J. Gastroenterol. 2012, 18 (17), 2005−2008. (36) Riedel, T. J.; Johnson, L. C.; Knight, J.; Hantgan, R. R.; Holmes, R. P.; Lowther, W. T. Structural and biochemical studies of human 4hydroxy-2-oxoglutarate aldolase: implications for hydroxyproline metabolism in primary hyperoxaluria. PLoS One 2011, 6 (10), 1−15. (37) Knight, J.; Jiang, J.; Assimos, D. G.; Holmes, R. P. Hydroxyproline ingestion and urinary oxalate and glycolate excretion. Kidney Int. 2006, 70 (11), 1929−1934. (38) Coulter-Mackie, M. B. 4-Hydroxyproline metabolism and glyoxylate production: a target for substrate depletion in primary hyperoxaluria? Kidney Int. 2006, 70 (11), 1891−1893. (39) Gibellini, F.; Smith, T. K. The kennedy pathway-de novo synthesis of phosphatidylethanolamine and phosphatidylcholine. IUBMB Life 2010, 62 (6), 414−428. (40) Tomono, M.; Crilly, K. S.; Kiss, Z. Synergistic potentiating effects of choline-phosphate and ethanolamine on insulin-induced DNAsynthesis in NIH 3T3 fibroblasts. Biochem. Biophys. Res. Commun. 1995, 213 (3), 980−985. (41) Swann, J. R.; Tuohy, K. M.; Lindfors, P.; Brown, D. T.; Gibson, G. R.; Wilson, I. D.; Sidaway, J.; Nicholson, J. K.; Holmes, E. Variation in antibiotic-induced microbial recolonization impacts on the host metabolic phenotypes of rats. J. Proteome Res. 2011, 10 (8), 3590−3603. (42) Diaz, E.; Ferrandez, A.; Prieto, M. A.; Garcia, J. L. Biodegradation of aromatic compounds by Escherichia coli. Microbiol. Mol. Biol. Rev. 2001, 65 (4), 523−569. (43) Rechner, A. R.; Kuhnle, G.; Bremner, P.; Hubbard, G. P.; Moore, K. P.; Rice-Evans, C. A. The metabolic fate of dietary polyphenols in humans. Free Radical Biol. Med. 2002, 33 (2), 220−235. (44) Kinross, J. M.; Alkhamesi, N.; Barton, R. H.; Silk, D. B.; Yap, I. K. S.; Darzi, A. W.; Holmes, E.; Nicholson, J. K. Global metabolic phenotyping in an experimental laparotomy model of surgical trauma. J. Proteome Res. 2011, 10 (1), 277−287.

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