Serum Metabolic Profiling Study of Hepatocellular Carcinoma Infected

Sep 4, 2012 - cause of cancer deaths in China.2 Chronic infection with hepatitis B or C virus (HBV, HCV) is a major risk factor for development of cir...
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Serum Metabolic Profiling Study of Hepatocellular Carcinoma Infected with Hepatitis B or Hepatitis C Virus by Using Liquid Chromatography−Mass Spectrometry Lina Zhou,‡,† Lili Ding,‡,§ Peiyuan Yin,† Xin Lu,† Xiaomei Wang,§ Junqi Niu,§ Pujun Gao,*,§ and Guowang Xu*,† †

CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China § Department of Hepatology, First Hospital, Jilin University, Changchun, 130021, China S Supporting Information *

ABSTRACT: The objective of the present study was to explore the common and specific metabolic alterations of hepatocellular carcinoma (HCC) infected with hepatitis B virus (HBV) or hepatitis C virus (HCV). Serum profiling data revealed that the two HCC groups shared a mainly similar metabolic profile, providing a basis for investigating their common tumor pathogenesis mechanism and early diagnosis biomarkers. Arachidonic acid as a pro-inflammatory precursor increased significantly in the HCC group compared to the cirrhosis and healthy control. And the lysophosphatidylcholines (lysoPCs) with polyunsaturated fatty acid acyl chain with potent antiinflammatory activity significantly decreased in the HCC and cirrhosis groups compared to those in the healthy control group, which may partly contribute to maintaining chronic inflammation and benefit the initiation and progression of the malignant hepatic tumor. The decreased ratios of polyunsaturated lysoPCs to saturated lysoPCs in HCC groups compared to chronic liver diseases infected with HBV or HCV and healthy control further demonstrated that a malignant liver tumor exerts profound influences independent of virus infection. Especially, serum endocannabinoids anandamide (AEA) and palmitylethanolamide (PEA) were found significantly elevated in HCC groups compared to healthy control, and in HCC with HCV compared to corresponding chronic liver diseases. AEA, PEA, or their combination showed better sensitivity, specificity, and the area under the curve for distinguishing HCC from chronic liver diseases, showing they are potential biomarkers to distinguish the HCC from cirrhosis infected with HCV. KEYWORDS: metabolic profiling, hepatocellular carcinoma, hepatitis B, hepatitis C, liver cirrhosis, arachidonic acid, lysophosphatidylcholine

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malignant lession from cirrhotic nodules.6 Therefore, it is important to unravel the HCC related pathological mechanism and explore effective clinical interventions to stop neoplastic formation and progression, especially the identification of early HCC discriminators. Metabolomics can monitor small molecular disturbance comprehensively and provide a systematic view of metabolic deregulations.7,8 Serum biochemical perturbations in HCC have been reported to be involved in lipid metabolism, increased protein degradation, perturbations of energy cycle, cholesterol metabolism, etc.9,10 Many potential serum biomarkers have been reported to discriminate HCC from chronic liver diseases including γ-glutamyl dipeptides,11 canavaninosuccinate and glycochenodeoxycholic acid12 and 1-methyladenosine.13 However, the performance of the discovered discriminators in identifying HCC from cirrhosis still needs validation with a large amount of samples. On the other hand,

INTRODUCTION Hepatocellular carcinoma (HCC) is the third most common cause of death from cancer worldwide1 and the second major cause of cancer deaths in China.2 Chronic infection with hepatitis B or C virus (HBV, HCV) is a major risk factor for development of cirrhosis and following HCC.3 The current trends in HCC rates in developing countries will not stop when the expected increases in life expectancy and growth of population are taken into account, although there is a decline in liver cancer risk factors.2 However, the underlying mechanism of HCC occurrence and development is still largely unknown and there are no effective clinical treatment measures. In addition, early discrimination of HCC from high-risk cirrhosis populations can significantly improve the survival through curative therapies like surgical resection, liver transplantation, etc.4 However, present prognosis marker αfetoprotein (AFP) predicts HCC without satisfactory sensitivity or specificity, especially when the tumor is smaller than three centimeters.5 And ultrasonography, as the most often used detection measure, is not sensitive enough to spot small © 2012 American Chemical Society

Received: July 23, 2012 Published: September 4, 2012 5433

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Table 1. Characteristics of Enrolled Population in the Metabolic Profiling Studya characteristics

N (n = 31)

Age Sex (Male/Female) AFP (ng/mL) AST (U/L) ALT (U/L) ALP (U/L) γ- GT (U/L) TBIL(umol/L) ALB (g/L) CHE (U/L)

25/6

CIR (n = 28)

HCC-B (n = 38)

HCC-C (n = 31)

normal range

53.4 ± 2.1 16/12 40.6 ± 27.82 99.2 ± 32.65 76.9 ± 24.48 98.2 ± 11.6 65.2 ± 10.11 54.1 ± 19.38 31.6 ± 1.23 3927.6 ± 353.4

51.7 ± 1.59 33/5 1272.1a ± 575.86 101.6 ± 12.11 92.6 ± 17.46 152.6a ± 16.94 159.2a ± 24.1 40.1 ± 8.76 38.4a ± 2.5 4262.4 ± 369.4

59.9a,b ± 2.21 23/8 10106.4a ± 4928.79 112.8 ± 21.4 71.7 ± 13.61 149.1a ± 12.53 163.8a ± 35.02 53.2 ± 13.26 50.6a,b ± 3.29 3314.4 ± 365.44

0−20 0−40 0−50 40−150 7−50 3.4−20 35−53 3,930−11,500

a

N, healthy control; CIR, cirrhosis; HCC-B, HCC infected with HBV; HCC-C HCC infected with HCV; AFP, alpha-fetoprotein; AST, aspartate transaminase; ALT, alanine aminotransferase; ALP, alkaline phosphatase; r-GT, γ-glutamyl transpeptidase; T-BIL, total bilirubin; ALB, albumin; CHE, cholinesterase. All data are presented as mean ± SE. aSignificantly (p < 0.05) elevated compared to cirrhosis. bSignificantly (p < 0.05) elevated compared to HCC with HBV infection.

recruited for the healthy control. Fasting sera were collected from each subject and stored at −80 °C. The basic characteristics of enrolled patients and healthy volunteers are listed in Table 1. Each group was basically matched in age and sex. All of the HCC and cirrhosis patients were confirmed with CT or magnetic resonance imaging or following biopsy results. All of the healthy volunteers tested negative for hepatitis B surface antigen. Fourteen of them took serum alanine transaminase (ALT) tests and their values were within the normal range except three people, whose ALTs were 51, 54, and 55 U/L. Liver function tests were performed for patients. There was no difference in ALT or aspartate transaminase among three liver disease groups, indicating their similar liver injury extent, whereas γ-glutamyl transpeptidase and alkaline phosphatas were significantly elevated in two HCC groups compared to the cirrhosis group. Albumin in the HCC group with HCV infection was significantly upregulated compared to that in the HCC group with HBV infection; in the meantime, it was also significantly increased in two HCC groups compared to the cirrhosis group. The serum AFP measurement was performed on the 23 cirrhosis and all HCC patients. The AFP levels were significantly lower in cirrhosis than in HCC with HBV or HCC with HCV infection (p = 0.01), while there was no significant difference in the serum AFP levels between the two HCC groups. Only one case infected with HBV had AFP > 200 in the cirrhosis group. Twenty-seven cases had their serum AFP larger than 200 in HCC groups (12/38 HCC cases with HBV infection and 15/ 31 HCC cases with HCV infection). For the multiple reaction monitoring (MRM) detection of endocannabinoids anandamide (AEA), palmitylethanolamide (PEA) and lysophosphatidylcholines (lysoPCs), another 179 fasting serum samples from the same hospital were further collected and investigated together with 39 HCC samples previously for metabolic profile study (20 HCC patients infected with HBV and 19 HCC patients infected with HCV). The 179 serum samples included 120 patients with chronic liver diseases (each 30 chronic hepatitis cases infected with HBV or HCV, each 30 cirrhosis cases infected with HBV or HCV), 18 HCC cases infected with HBV and 11 HCC cases infected with HCV, and 30 samples from healthy volunteers.

when coming to the HCC cases with different infected viruses, Shariff et al.14 employed proton nuclear magnetic resonance spectroscopy for urine profiling of a group of mainly HBV infected Nigerian HCC patients and a cohort of HCV infected Egyptian subjects and identified similar alterations in glycine, trimethylamine-N-oxide, hippurate, citrate, creatinine, creatine and carnitine, suggesting that their commonly altered metabolites are independent of infected virus. However, no one has studied the blood metabolic profiling of these two types of HCC diseases. Furthermore, it is reviewed that Asian patients with chronic hepatitis C may have a substantially higher risk of liver cancer than those with chronic hepatitis B.15 Thus, there is an urgent need to identify common and unique mechanisms responsible for HCC development through investigating metabolic similarities and differences in HCC infected with these two different viruses. In this study, serum samples from two HCC cohorts respectively infected with HBV or HCV were collected and profiled based on the ultra high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-qTOF-MS). Univariate and multivariate statistical analysis methods were performed on the acquired metabolome data to identify common and specific metabolic features of HCC cases with two different infected factors, especially aimed at the exploration of important common differential metabolites and corresponding metabolic pathways related to HCC pathogenesis and early diagnosis.

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MATERIALS AND METHODS

Reagents and Chemicals

HPLC-grade acetonitrile and formic acid were purchased from Merck (USA) and Sigma-Aldrich (St. Louis, MO), respectively, to prepare mobile phases. Milli-Q water was filtered using a Milli-Q system (Millipore, Bedford, MA). Chemical standards for structure validation were purchased from Sigma-Aldrich. Serum Specimen Collection

Ninety-seven patients attending the affiliated hospital of the medical school of Jilin University were recruited in the metabolic profiling study. Written informed consent was obtained from each enrolled patient. Among the 97 cases, 28 subjects had cirrhotic liver disease, with 22 and 6 cases respectively infected with HBV and HCV, and 69 subjects had HCC, including 38 cases with HBV infection and 31 cases with HCV infection. Thirty-one healthy volunteers were also

LC−MS-based Serum Metabolic Profiling

For serum preparation, 150 μL serum from each sample was mixed with 600 μL acetonitrile for protein precipitation and then the mixture was centrifuged at 15 000× g for 10 min at 4 5434

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°C. A 600 μL aliquot of supernatant was drawn and lyophilized. Before analysis, the residue was reconstituted in 100 μL 20% acetonitrile in water (in volume). Equal aliquots of serum from each sample were pooled and mixed by vortex for 1 min and used as the quality control (QC) sample. The pretreatment of QC samples was the same as the above real samples. After every 10 real samples, a QC sample was run. These inserted QC samples were used to evaluate the repeatability of sample pretreatment and monitor the stability of the LC−MS system during sequence analysis. For serum LC−MS profiling, a modified UPLC-qTOF-MS approach16 was applied. A 5 μL aliquot of the reconstituted solution was injected into an ACQUITY-UPLC system (Waters Corp, Milford, MA) for chromatographic separation, followed by MS signal acquisition through a qTOF mass spectrometry (Micromass, Manchester, U.K.) equipped with an electrospray source operated in positive ion mode. A BEH C18 column (100 mm × 2.1 mm, 1.7 μm) (Waters) was used at 35 °C with elution speed at 0.35 mL/min for separation of small molecular compounds. The initial gradient was set at 90% A (0.1% formic acid in water (V/V)). At 0.5 min, the elution strength was increased to 100% B (acetonitrile) linearly within 24 min and held for 5 min. The total run time was 30 min including equilibration of 1 min. For the MS signal acquisition,17 the m/z scan range was set from 80 to 1000.

m/z and retention time and their corresponding peak intensities were exported to an Excel table. Each peak area was normalized to the total peak area in a column before the following univariate and multivariate statistical analysis. During the multivariate analysis, the principal component analysis (PCA) and the partial least squares-discriminant analysis (PLS-DA) were performed on the prepared data using SIMCA-P 11.0 (Umetrics AB, Umea, Sweden). The data were unit variance scaled for PCA to give an overview of the repeatability of QC samples. The data were Pareto scaled for PLS-DA to see the performance of the classification models and find the responsible variables for the corresponding model. All of the chosen differential expression ions were imported into SPSS 13.0 (SPSS, Chicago, IL) for the univariate analysis. Student’s t-test analysis was performed with significance considered at p < 0.05, and further hierarchical cluster analysis (HCA) was exerted on all chosen differential expression ions with Multi Experiment Viewer (http://www.tm4.org).

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RESULTS AND DISCUSSION

Serum Metabolic Profiling

A typical base peak chromatogram detected by MS is shown in Figure 1. After aligning peaks, 384 variables were obtained in

Endocannabinoid and lysoPC MRM Analysis

Fifty microliters of serum from each sample was mixed with 200 μL acetonitrile containing lysoPC(19:0) as internal standard and then the mixture was centrifuged as above. Two hundred microliters supernatant was lyophilized and reconstituted in 40 μL 20% acetonitrile in water. The detailed LCMS parameters were as follows: A 5 μL aliquot of the reconstituted solution was injected into an Agilent LC 1290 (Agilent Technologies, USA) for chromatographic separation, followed by MRM signal acquisition through a Agilent 6460 Triple Quadruple mass spectrometry (Agilent Technologies) equipped with an electrospray source operated in positive ion mode. A HSS T3 column (100 mm × 2.1 mm, 1.7 μm) (Waters, Milford, MA) was used with column temperature, mobile phases and elution speed the same as above. The initial gradient was set at 50% B, at 8.0 min the elution strength was increased to 65% B linearly, then to 100% B within 6 min, held for 4 min, and back to initial gradient at 19 min. The total run time was 23 min including equilibration of 4 min. And the acquired precursor and product ion pairs were as follows: AEA (348→62), PEA (300→62), lysoPC(14:0) (468→184), lysoPC(16:1) (494→184), lysoPC(16:0) (496→184), lysoPC(18:3) (518→184), lysoPC(18:2) (520→184), lysoPC(18:1) (522→184), lysoPC(18:0) (524→184), lysoPC(20:5) (542→184) and lysoPC(20:4) (544→184), lysoPC(20:3) (546→184), lysoPC(20:2) (548→184), lysoPC(20:1) (550→184), lysoPC(20:0) (552→184), lysoPC(22:6) (568→184), lysoPC(22:5) (570→184), lysoPC(22:4) (572→184).

Figure 1. Typical base peak chromatogram of serum metabolic profiling based on LC−MS.

the positive mode. Four ions (m/z 274.27, 302.30, 318.30 and 346.33) and their isotopes that existed in blank were further removed, and the remaining 376 variables were used for multivariate analysis or univariate analysis. The QC samples were used to monitor the robustness of serum sample pretreatment and the LC−MS stability during the sequence running. Unsupervised PCA was performed on the matrix of 376 remaining variables × 142 observations (128 real samples and 14 QC samples). It can be observed that the QC samples locate in the center and cluster together tightly on the scores plot (Figure 2A), which ensures the repeatability of the profiled information.19 Furthermore, the healthy control samples locate together apart from other samples with liver diseases. There is also a discrimination trend between HCC and cirrhosis groups.

Data Analysis

Raw data was imported into Databridge (Waters, U.K.) for data format transformation. And then the peak extraction and alignment were done on the obtained NetCDF files using XCMS software.18 The alignment parameters were set as follows: full width at half-maximum (fwhm) of 14 and the retention time window (bw) of 7 were entered. Other parameters were set as default. Then the peaks with paired

Metabolic Profile Comparison of HCC Patients with HBV Infection and HCV Infection

The metabolic profiling data provide an overview of metabolic information of HCC patients with HBV infection versus HCC patients with HCV infection. On the scores plot of PCA 5435

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Figure 2. Multivariate statistical analysis on serum profiling data. (A) PCA scores plot with all variables unit variance scaled. QC (orange ▲), healthy control (red ■), cirrhosis (black ◊), HCC infected with HBV (blue ●), and HCC infected with HCV (green ●). (B) PLS-DA scores plot of HCC group (blue ●) versus cirrhosis group (black ◊) and healthy control (red ■). (C) Cross-validation plot with a permutation test repeated 200 times. (D) PLS-DA loading plot with responsible variables marked with red empty squares.

Differential Metabolites in HCC and Cirrhosis from Healthy Control

(Figure 2A), the individuals of HCC with HBV infection scatter among the individuals of HCC with HCV. One PLS component was obtained when the supervised multivariate analysis method PLS-DA was performed on the two HCC groups. The poor model parameters were R2Y = 0.33, Q2 = 0.08. The model was overfitting if a next component was calculated, that is to say that the two different hepatitis virus infected HCC groups have only minor differences in metabolic profiles. This result indicates that the malignant tumor is the more influential factors toward the overall metabolic alteration compared to these two epidemiology pathogenic factors. Furthermore, in order to view the influences on HCC metabolome aroused by the different hepatitis virus infections, a Student’s t-test was exerted on the 376 variables. In total, 68 variables had significant differences between the two HCC groups, accounting for 18.1% of the total ions and 23.1% of the total peak area. Among these differential ions, only 28 ions (taking up 7.4% of the total ions and 8.9% of the total area) had reverse alteration trends in these two HCC groups compared to healthy control; they took up less than 10% both in ion number and peak area, indicating a relative small influence of different hepatitis virus infection on HCC metabolic profiles. The results from both multivariate and univariate analysis show that the infection factors exert a relative smaller effect on the serum metabolome of HCC; this finding is similar to the urine metabolic profiles that were obtained through proton nuclear magnetic resonance spectroscopy on a group of mainly HBV infected Nigerian HCC patients and a cohort of HCV infected Egyptian subjects.14 Thus, for the following investigation of HCC related metabolic alteration and diagnosis biomarker, the two HCC groups infected with HBV or HCV, respectively, were combined into one HCC group.

Most of HCCs progress from cirrhotic livers, but it is hard to distinguish early HCC from cirrhosis cases in clinical practice. The existence of large scale cirrhosis nodules may tremendously shrink the metabolic changes derived from small tumors. Thus it is difficult to discover important differential metabolites or related enzymes specific to the occurrence and development of HCC. PLS-DA was performed to see the classification situations of the combined HCC group compared to cirrhosis and healthy control. A permutation test was repeated 200 times to validate the models. The scores plot was produced based on the first two calculated components. On the scores plot of PLSDA (Figure 2B), the combined HCC group and cirrhosis group depart from the healthy group along the first PLS component, and the combined HCC group departs from the cirrhosis group along the second PLS component. The intercepts (R2 = 0.17, Q2 = −0.38) on the cross-validation plot (Figure 2C) indicate that the PLS-DA model 1 for discriminating the combined HCC group from healthy control is valid without overfitting. The model quality is shown by the following two parameters: R2Y = 0.80, Q2 = 0.69 with first three components calculated. The intercepts indicating no model overfitting are as follows: R2 = 0.16 and Q2 = −0.35, calculated by the permutation tests repeated 200 times in the PLS-DA model 2 for discriminating cirrhosis group from healthy control and R2 = 0.25 and Q2 = −0.43 in the PLS-DA model 3 for discriminating the combined HCC group from cirrhosis group. To identify important metabolites related to HCC, differential variables responsible for classification were spotted on the loading plot (Figure 2D) and further evaluated by VIP (variable importance in the projection) values. The first two components contribute 0.55 (R2Y (cum)) to explain the X matrix variation and 0.52 (Q2 5436

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Table 2. Differential Metabolite Ions in the Combined HCC Group Compared to Healthy Controls (N)a VIP[1]

VIP[2]

metabolites

m/z

tR (min)

0.99 1.41 2.08 4.65 1.27 3.14 0.53 1.84 2.02 2.11 1.46 1.87 0.58 1.24 2.09 1.44 2.21 1.84 3.56 1.08 1.36 1.99 3.52 7.03 2.11 1.04 2.51 5.97 2.24 1.57 1.67 1.46 1.44 1.72 1.69 2.29 4.69 2.47 0.9 1.71 1.25 0.94

1.26 1.73 1.69 3.89 1.64 3.01 1.9 1.51 1.88 2.01 1.51 1.7 1.24 2.86 4.83 1.41 2.1 1.7 3.32 1.16 1.38 1.52 2.98 5.92 1.61 2.88 1.93 4.38 1.65 1.15 1.4 2.54 1.88 1.59 1.25 2.33 4.81 3.19 1.23 2.17 1.28 1.32

UN UN L-Phenylalanine UN L-tryptophan UN UN UN GCA GCA GCA UN Conjugated Bilirubin Conjugated Bilirubin Conjugated Bilirubin GDCA GDCA GDCA GDCA UN LysoPE(18:2) LysoPC(22:6) LysoPC(18:2) LysoPC(18:2) LysoPC(20:4) LysoPC(16:0) LysoPC(16:0) LysoPC(16:0) UN UN C18:2-CN LysoPC(18:1) UN C18:1-CN UN LysoPC(18:0) LysoPC(18:0) Palmitic amide Arachidonic acid Stearamide Biliverdin Bilirubin

203 316.1 120.2 100.3 188.1 652.4 592.4 246.2 430.3 412.3 466.3 464.3 587.3 586.3 585.3 450.3 432.3 415.3 414.3 362.3 478.3 568.3 521.3 520.3 544.3 496.4 497.3 496.3 330.3 374.4 424.4 522.4 508.4 426.4 358.4 524.4 525.4 256.2 305.2 284.3 583.3 299.1

0.69 0.87 1.08 1.16 1.68 4.19 6.42 8.46 8.75 8.75 8.75 8.83 9.09 9.09 9.09 10.52 10.52 10.52 10.52 11.11 14.26 14.29 14.34 14.34 14.37 14.78 15.23 15.23 15.37 15.45 15.5 15.76 16.24 16.85 17.48 17.6 17.6 19.08 19.64 21.46 22.42 22.42

ion forms

Fragment Fragment

Fragment Fragment [M + H]+ Fragment isotope Fragment isotope Fragment [M + H]+ Fragment Fragment isotope Fragment [M + H]+ [M + H]+ Isotope [M+H]+ [M + H]+ [M + H]+ Isotope [M + H]+

[M + H]+ [M + H]+ [M + H]+ [M + H]+ Isotope [M + H]+ [M + H]+ [M + H]+ [M + H]+ Fragment

CIR/N

HCC/N

0.96 0.9 1.84c 0.4c 1.03 482.26c 2796.69c 98.83c 30.04c 30.16c 62.13c 26.16c 6.98c 7.47c 7.87c 31.05c 8.83c 5.99c 6.64c 126.72c 0.87 0.37c 0.79c 0.78c 0.66c 0.67c 0.83c 0.84c 817.61c 1790.85c 1.9c 0.84c 1.17 1.77c 61.35c 0.71c 0.72c 0.82 1.02 0.91 3.01 1.91

0.39c 0.19c 1.58c 0.13c 0.79c 85.24c 580.05c 128.12c 22.54c 22.14c 44.66c 21.29c 3.17c 3.22c 3.35c 25.22c 7.17c 4.81c 5.32c 218.94c 0.51c 0.45c 0.58c 0.57c 0.64c 0.21c 0.61c 0.19c 20503.11c 3671.22c 1.05c 0.07b 7.52c 2.61c 70.54c 0.02c 0.16c 5.01c 91.81c 0.22c 11.71c 7.1b

p(HCC vs CIR) 5.65 2.94 3.65 1.68 3.93 9.02 3.43 8.39 1.12 8.29 1.22 3.93 4.96 1.19 1.37 4.02 2.75 1.36 1.58 2.09 2.81 1.23 1.58 3.24 7.79 7.61 5.38 1.22 7.29 3.94 1.02 1.53 9.10 5.28 9.27 1.07 1.33 6.72 1.34 2.60 8.22 7.51

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

10−11 10−9 10−2 10−9 10−4 10−19 10−16 10−3 10−1 10−2 10−1 10−1 10−4 10−3 10−3 10−1 10−1 10−1 10−1 10−6 10−6 10−1 10−6 10−6 10−1 10−4 10−1 10−1 10−1 10−1 10−2 10−1 10−7 10−3 10−1 10−1 10−1 10−4 10−6 10−3 10−1 10−1

a

UN, unknown compounds or ions; GCA, glycocholic acid; GDCA, deoxycholic acid glycine conjugate; CN, acylcarnitine; LysoPC, lysophosphatidylcholine; LysoPE, lysophosphatidylethanolamine. bSignificant difference level of p < 0.05 between corresponding two groups. c Significant difference level of p < 0.01 between corresponding two groups.

(cum)) to predict classifications, VIP values of these first two components were both considered. A variable was kept if its VIP value of either component was larger than one. Forty-three variables were obtained for the next Student’s t-test analysis. One of them was removed because of no significant differences in HCC groups compared to healthy control. Our previous protocol20 was referred for the identification of these remaining 42 ions. The identification results are listed in Table 2, including phenylalanine, tryptophan, glycocholic acid (GCA), deoxycholic acid glycine conjugate (GDCA), conjugated bilirubin, free bilirubin and biliverdin, two long-chain acyl carnitines (acyl-CNs), palmitic amide, stearamide, arachidonic acid (AA), lysoPE(18:2) and six lysoPCs. These compounds may relate liver diseases or malignant liver tumor

to amino acid metabolism, cholesterol metabolism, fatty acid oxidation, inflammation and membrane generation, etc. Important Biological Alteration Related to Liver Diseases

In order to visualize the relationship among the 19 altered metabolites, HCA was performed. The metabolites were arranged according to the Pearson correlation coefficients calculated based on their relative abundance across samples. As is shown on the heat map (Figure 3), two bile acids, three bile pigments, two long-chain acyl-CNs, two primary fatty acid amides (PFAMs) and two aromatic amino acids locate nearer according to their species. LysoPCs cluster together; of note, the polyunsaturated lysoPC(20:4), and lysoPC(22:6) locate nearer relative to saturated lysoPC(16:0) and lysoPC(18:0). These clustering results, especially the clustering situation of 5437

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Figure 3. Heat map showing relative abundance of differential ions in liver disease groups relative to healthy control. CIR and HCC-B and HCC-C stand for cirrhosis infected with HBV or HCV, and HCC infected with HBV, HCC infected with HCV, respectively.

Figure 4. MRM detection of lysoPCs in chronic liver diseases and HCC groups infected with hepatitis virus B or C. The lysoPCs in chronic liver diseases and HCC with each hepatitis virus were compared internally and to healthy control. * means the ratio in corresponding liver disease group was significantly different from that in the control (p < 0.05); + means the ratio in cirrhosis or HCC was significantly different from that in chronic hepatitis; # means the ratio in HCC was significantly different from that in cirrhosis. N, CH-B(C), CIR-B(C), HCC-B(C) stand for healthy control, chronic hepatitis, cirrhosis, HCC infected with HBV (or HCV), respectively.

cirrhosis and the combined HCC in comparison with that in the healthy control. The relative elevated trends in conjugated bilirubin and the two concerned long-chain acyl-CNs in the HCC and cirrhosis groups are consistent with our previous reports.10,26 Six important serum lysoPCs, lysoPC(18:1), lysoPC(18:2), saturated lysoPC(18:0) and lysoPC(16:0), polyunsaturated lysoPC(20:4) and lysoPC(22:6), are decreased in cirrhosis and the combined HCC. Their down-regulations may mainly result from rapid membrane PC turnover during the liver injury or malignant regeneration.27−29 AA is significantly elevated in the combined HCC group in comparison to cirrhosis and healthy control, it is known as a precursor of pro-inflammatory

lysoPCs with different acyl chains, may help to identify mainly altered metabolic pathways or underlying action mechanisms involved in cancer initiation and progression. Among the perturbed metabolites, tryptophan is significantly lower in the combined HCC group than in cirrhosis and healthy control, which is explained controversially by the active organism immune response to resist malignant tumor21 or due to the immune escape mechanism of tumor through tryptophan depletion.22−25 Conjugated bilirubin is significantly upregulated in the combined HCC group and cirrhosis, it also has a significant elevation in cirrhosis compared to that in the combined HCC group. C18:1-CN and C18:2-CN are significantly higher in 5438

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Figure 5. Relative ratios in chronic liver diseases and HCC infected with HBV or HCV. (A) Ratios of polyunsaturated lysoPCs to saturated lysoPCs; (B) ratios of lysoPC(18:1) to lysoPC(18:0). The ratios in chronic liver diseases and HCC with each hepatitis virus were compared internally and to healthy control. * means the ratio in corresponding liver disease group was significantly different from that in the control (p < 0.05); + means the ratio in cirrhosis or HCC was significantly different from that in chronic hepatitis; # means the ratio in HCC was significantly different from that in cirrhosis. N, CH-B(C), CIR-B(C), HCC-B(C) stand for healthy control, chronic hepatitis, cirrhosis, HCC infected with HBV (or HCV), respectively.

Figure 6. Relative serum levels of (A) AEA and (B) PEA in each group. The serum levels of AEA and PEA in chronic liver diseases and HCC with each hepatitis virus were compared internally and to healthy control. * means the ratio in corresponding liver disease group was significantly different from that in the control (p < 0.05) ; + means the ratio in cirrhosis or HCC was significantly different from that in chronic hepatitis; # means the ratio in HCC was significantly different from that in cirrhosis. N, CH-B(C), CIR-B(C), HCC-B(C) stand for healthy control, chronic hepatitis, cirrhosis, HCC infected with HBV (or HCV), respectively.

lowest lysoPCs can be found in HCC group infected with HBV or HCV which may mainly result from more rapid membrane PC turnover, reflecting the influence of malignant liver tumor. Compared with healthy control, all lysoPCs significantly decrease in chronic liver diseases infected with HBV, whereas lysoPCs have relative minor decreases in chronic liver diseases infected with HCV. Furthermore, there are continuous decreasing trends in lysoPCs with disease process in liver diseases with HBV infection compared to the steeper decreases of lysoPCs in HCC infected with HCV. These indicate the different inflammation interactions caused by different hepatitis virus infection. The ratios of polyunsaturated lysoPCs to saturated lysoPCs are decreased in both HCC groups compared not only to healthy control but also to other two chronic liver diseases infected with HBV or HCV (Figure 5A), reflecting the higher anti-inflammation activation due to the existence of a malignant tumor. This phenomenon further demonstrates the profound influence of a malignant liver tumor on the serum metabolome. Furthermore, stearoyl-CoA desaturase (SCD) with its main isoform SCD1 expressed in liver43 is activated in HCC cohorts and cirrhosis (not significantly in HCC subjects infected with HCV) compared to healthy subjects reflected by the increased ratios of serum monounsaturated lysoPCs to saturated lysoPCs (Figure S1, Supporting Information), which has similar trends to our previous serum free fatty acid content investigation.26 And the ratios of lysoPC(18:1) to lysoPC(18:0) are significantly elevated in HCC and cirrhosis compared to healthy control and chronic hepatitis infected with HBV or HCV (Figure 5B). SCD1 activation may suppress pro-inflammatory signaling by

prostaglandins and leukotrienes; a minor elevation in AA level can induce profound downstream pro-inflammation consequences.30,31 Nevertheless, the released polyunsaturated lysoPCs will play an antagonistic role against AA through conversions to their downstream anti-inflammatory products.32 These conflicting inflammation-promoting and inflammationantagonizing actions may be helpful to maintain chronic inflammation; more details can be seen from the recent review on the relationships of infection and chronic inflammation and their molecular mechanisms in inducing cancer and promoting tumor development.33,34 Comparison of Serum lysoPCs in Chronic Liver Diseases and HCC Infected with HBV and HCV

LysoPCs are very sensitive toward pathophysiological stimuli and are often identified as biomarkers in the studies of metabolomics or lipidomics.27,35−38 They can play an important role in membrane remolding,39 contradictory inflammatory mediator biogenesis and anti-inflammation actions respectively with saturated and polyunsaturated acyl chains,32,40−42 etc. To further explore lysoPCs with different acyl chains related to cirrhosis and HCC, and also to compare their response to different hepatitis virus infection and their ended malignant liver tumors, a further MRM investigation was performed and the results are summarized in Figure 4. The similar down-regulation trends of saturated or polyunsaturated lysoPCs in cirrhosis and HCC groups can be found in the profiling and MRM analyses. When comparing comprehensive chronic liver diseases and HCC infected with HBV and HCV, there is a total decreasing trend of the lysoPCs in chronic liver diseases and HCC, the 5439

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further MRM experiment of chronic liver diseases and HCC with different hepatitis virus infection, though there is a total decreasing trend in lysoPCs in chronic liver diseases and HCC groups with either hepatitis virus infection, their decreases are sharper from chronic liver diseases toward HCC infected with HCV. Of note, the ratios of polyunsaturated lysoPCs to saturated lysoPCs are decreased in both HCC groups compared not only to healthy control but also to other two chronic liver diseases infected with HBV or HCV, reflecting the higher anti-inflammation activation caused by the existence of malignant tumor. AEA and PEA are found increased in both HCC groups, with a significant elevation in HCC with HCV versus corresponding chronic liver diseases. These results prove that LC−MS-based metabolomics is a powerful tool in spotting disease-associated biochemical disturbances and their corresponding underlying mechanisms. However, further study is still needed to validate the observed metabolic disturbances and explore their corresponding influences and mechanisms related to serious cirrhosis and the initiation and progression of malignant liver tumor.

converting saturated fatty acids to monounsaturated fatty acids;35 some studies have also proposed that lysoPC(18:1) does not induce the release of pro-inflammatory cytokines from immune cells44,45 and might in turn attenuate the proinflammation action induced by saturated lysoPCs.46 AEA and PEA in Chronic Liver Diseases and HCC Infected with HBV and HCV

In the Student’s t-test, we found PFAMs elevated in HCC infected with HCV compared to those in cirrhosis and healthy control; the information prompted us to further investigate their related metabolic perturbations. Oleamide has been reported to protect endogenous cannabinoid receptor 1 (CB1) agonist, AEA, from hydrolysis by fatty acid amide hydrolase.47 CB1 has been demonstrated present in the livers of patients with chronic hepatitis C with strong positive association with HCV viral load, suggesting a direct viral effect. Compared to that in healthy control, mild chronic hepatitis B with no steatosis, CB1 expression has been found significantly higher in patients with mild chronic hepatitis C without steatosis48 and a hyperactivation of the biosynthesis of AEA in the regenerating liver has been spotted;49 thus, endocannabinoid AEA and PEA were investigated in the serum of chronic liver diseases and HCC infected with HBV or HCV (Figure 6A and B). It is found that there is no significant difference in serum AEA and PEA between two HCC groups respectively infected with HBV and HCV, and their serum contents are significantly elevated in both HCCs compared to healthy control. For HBV infection, AEA and PEA are significantly upregulated in chronic liver diseases compared to healthy control, but have no significant elevation in HCC versus corresponding two chronic liver diseases indicating a more gentle change with disease development. For HCV infection, the upregulations of AEA and PEA are not significant in chronic liver disease compared to those in the healthy control, but they both significantly increase in HCC compared to those in two chronic liver diseases. And the sharply synthesized AEA in HCC infected with HCV can activate the large amount of existing CB1 inhibiting tumor cell proliferation.50 The receiver operator characteristic curves were plotted based on AEA, PEA and their combination for discriminating HCC infected with HCV respectively from healthy control (Figure S2A, Supporting Information), cirrhosis infected with HCV (Figure S2B) and chronic liver diseases infected with HCV (Figure S2C). And the combination of AEA and PEA shows better sensitivity and specificity (0.84 and 0.90, respectively, for distinguishing HCC from healthy control with area under the curve (AUC) of 0.94; 0.87 and 0.90, respectively, for distinguishing HCC from cirrhosis with the AUC of 0.88; 0.81 and 0.92, respectively, for distinguishing HCC from chronic liver diseases with the AUC of 0.89). These data show AEA and PEA are valuable for further study to evaluate their potential in distinguishing HCC infected with HCV.

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ASSOCIATED CONTENT

S Supporting Information *

Figures S1 and S2. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*G.X., Tel./ Fax: +86-411-84379530. E-mail: [email protected]. P.G., Tel./ Fax: +86-411-84379530. E-mail: pujun-gao@163. com. Author Contributions ‡

These authors contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study has been supported by the State Key Science & Technology Project for Infectious Diseases (2012ZX10002011) and the Key Foundation (No.s 20835006, 21175132) and the Creative Research Group Project (No. 21021004) from National Natural Science Foundation of China.

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REFERENCES

(1) Parkin, D. M.; Bray, F.; Ferlay, J.; Pisani, P. Global cancer statistics, 2002. CA. Cancer J. Clin. 2005, 55 (2), 74−108. (2) Chen, J. G.; Zhang, S. W. Liver cancer epidemic in China: past, present and future. Semin. Cancer Biol. 2011, 21 (1), 59−69. (3) Kuper, H.; Adami, H. O.; Trichopoulos, D. Infections as a major preventable cause of human cancer. J. Intern. Med. 2000, 248 (3), 171−183. (4) El-Serag, H. B.; Marrero, J. A.; Rudolph, L.; Reddy, K. R. Diagnosis and treatment of hepatocellular carcinoma. Gastroenterology 2008, 134 (6), 1752−1763. (5) Chen, L.; Ho, D. W. Y.; Lee, N. P. Y.; Sun, S.; Lam, B.; Wong, K.F.; Yi, X.; Lau, G. K.; Ng, E. W. Y.; Poon, T. C. W.; Lai, P. B. S.; Cai, Z.; Peng, J.; Leng, X.; Poon, R. T. P.; Luk, J. M. Enhanced detection of early hepatocellular carcinoma by serum SELDI-TOF proteomic signature combined with alpha-fetoprotein marker. Ann. Surg. Oncol. 2010, 17 (9), 2518−2525. (6) Colli, A.; Fraquelli, M.; Casazza, G.; Massironi, S.; Colucci, A.; Conte, D.; Duca, P. Accuracy of ultrasonography, spiral CT, magnetic resonance, and alpha-fetoprotein in diagnosing hepatocellular

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CONCLUSIONS This serum profiling study with both multivariate and univariate statistical analysis provides a systematical view of the major similarity in serum metabolome of HCC cases infected with HBV and HCV, showing the profound influence of malignant liver tumor on serum metabolome. The decreased polyunsaturated lysoPCs and elevated AA may be mainly related to the initiation and progression of malignant hepatic tumor. In the 5440

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Journal of Proteome Research

Article

carcinoma: a systematic review. Am. J. Gastroenterol. 2006, 101 (3), 513−523. (7) 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. (8) van der Greef, J.; Stroobant, P.; van der Heijden, R. The role of analytical sciences medical systems biology. Curr. Opin. Chem. Biol. 2004, 8 (5), 559−565. (9) Gao, H. C.; Lu, Q.; Liu, X.; Cong, H.; Zhao, L. C.; Wang, H. M.; Lin, D. H. Application of H-1 NMR-based metabonomics in the study of metabolic profiling of human hepatocellular carcinoma and liver cirrhosis. Cancer Sci. 2009, 100 (4), 782−785. (10) Yin, P.; Wan, D.; Zhao, C.; Chen, J.; Zhao, X.; Wang, W.; Lu, X.; Yang, S.; Gu, J.; Xu, G. A metabonomic study of hepatitis B-induced liver cirrhosis and hepatocellular carcinoma by using RP-LC and HILIC coupled with mass spectrometry. Mol. BioSyst. 2009, 5 (8), 868. (11) Soga, T.; Sugimoto, M.; Honma, M.; Mori, M.; Igarashi, K.; Kashikura, K.; Ikeda, S.; Hirayama, A.; Yamamoto, T.; Yoshida, H. Serum metabolomics reveals γ-glutamyl dipeptides as biomarkers for discrimination among different forms of liver disease. J. Hepatol. 2011, 55 (4), 896−905. (12) 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. (13) Chen, F.; Xue, J.; Zhou, L.; Wu, S.; Chen, Z. Identification of serum biomarkers of hepatocarcinoma through liquid chromatography/mass spectrometry-based metabonomic method. Anal. Bioanal. Chem. 2011, 401 (6), 1899−1904. (14) 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. (15) Nguyen, M. H.; Keeffe, E. B. Chronic hepatitis B and hepatitis C in Asian Americans. Rev. Gastroenterol. Disord. 2003, 3 (3), 125−34. (16) Zhao, X.; Fritsche, J.; Wang, J.; Chen, J.; Rittig, K.; SchmittKopplin, P.; Fritsche, A.; Häring, H.-U.; Schleicher, E. D.; Xu, G.; Lehmann, R. Metabonomic fingerprints of fasting plasma and spot urine reveal human pre-diabetic metabolic traits. Metabolomics 2010, 6 (3), 362−374. (17) Chen, J.; Zhang, X.; Cao, R.; Lu, X.; Zhao, S.; Fekete, A.; Huang, Q.; Schmitt-Kopplin, P.; Wang, Y.; Xu, Z.; Wan, X.; Wu, X.; Zhao, N.; Xu, C.; Xu, G. Serum 27-nor-5β-cholestane-3,7,12,24,25 pentol glucuronide discovered by metabolomics as potential diagnostic biomarker for epithelium ovarian cancer. J. Proteome Res. 2011, 10 (5), 2625−2632. (18) 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. (19) 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. (20) Chen, J.; Zhao, X.; Fritsche, J.; Yin, P.; Schmitt-Kopplin, P.; Wang, W.; Lu, X.; Häring, H.-U.; Schleicher, E. D.; Lehmann, R.; Xu, G. Practical approach for the identification and isomer elucidation of biomarkers detected in a metabonomic study for the discovery of individuals at risk for diabetes by integrating the chromatographic and mass spectrometric information. Anal. Chem. 2008, 80 (4), 1280− 1289. (21) Takikawa, O.; Kuroiwa, T.; Yamazaki, F.; Kido, R. Mechanism of interferon-gamma action-characterization of indoleamine 2, 3dioxygenase in cultured human cells induced by interferon-gamma and

evaluation of the enzyme-mediated tryptophan degradation in its anticellular activity. J. Biol. Chem. 1988, 263 (4), 2041−2048. (22) Hwu, P.; Du, M. X.; Lapointe, R.; Do, M.; Taylor, M. W.; Young, H. A. Indoleamine 2, 3-dioxygenase production by human dendritic cells results in the inhibition of T cell proliferation. J. Immunol. 2000, 164 (7), 3596−3599. (23) Uyttenhove, C.; Pilotte, L.; Théate, I.; Stroobant, V.; Colau, D.; Parmentier, N.; Boon, T.; Van den Eynde, B. J. Evidence for a tumoral immune resistance mechanism based on tryptophan degradation by indoleamine 2, 3-dioxygenase. Nat. Med. 2003, 9 (10), 1269−1274. (24) Kudo, Y.; Boyd, C.; Sargent, I.; Redman, C. Tryptophan degradation by human placental indoleamine 2,3-dioxygenase regulates lymphocyte proliferation. J. Physiol. 2001, 535 (1), 207−215. (25) Frumento, G.; Rotondo, R.; Tonetti, M.; Ferrara, G. B. T cell proliferation is blocked by indoleamine 2,3-dioxygenase. Transplant. Proc. 2001, 33 (1), 428−430. (26) Zhou, L.; Wang, Q.; Yin, P.; Xing, W.; Wu, Z.; Chen, S.; Lu, X.; Zhang, Y.; Lin, X.; Xu, G. Serum metabolomics reveals the deregulation of fatty acids metabolism in hepatocellular carcinoma and chronic liver diseases. Anal. Bioanal. Chem. 2012, 403 (1), 203− 213. (27) Taylor, L. A.; Arends, J.; Hodina, A. K.; Unger, C.; Massing, U. Plasma lyso-phosphatidylcholine concentration is decreased in cancer patients with weight loss and activated inflammatory status. Lipids Health Dis. 2007, 6, 17 DOI: 10.1186/1476-511X-6-17. (28) Lands, W. E. M. Metabolism of glycerolipids. 2. Enzymatic acylation of lysolecithin. J. Biol. Chem. 1960, 235 (8), 2233−2237. (29) Macdonald, J. I. S.; Sprecher, H. Phospholipid fatty-acid remodeling in mammalian-cells. Biochim. Biophys. Acta 1991, 1084 (2), 105−121. (30) Eberhart, C. E.; Dubois, R. N. Eicosanoids and the gastrointestinal-tract. Gastroenterology 1995, 109 (1), 285−301. (31) Clarke, G.; Fitzgerald, P.; Hennessy, A. A.; Cassidy, E. M.; Quigley, E. M. M.; Ross, P.; Stanton, C.; Cryan, J. F.; Dinan, T. G. Marked elevations in pro-inflammatory polyunsaturated fatty acid metabolites in females with irritable bowel syndrome. J. Lipid Res. 2009, 51 (5), 1186−1192. (32) Hung, N. D.; Kim, M. R.; Sok, D.-E. Anti-inflammatory action of arachidonoyl lysophosphatidylcholine or 15-hydroperoxy derivative in zymosan A-induced peritonitis. Prostaglandins Other Lipid Mediat. 2009, 90 (3−4), 105−111. (33) Karin, M.; Greten, F. R. NF - kappa B: linking inflammation and immunity to cancer development and progression. Nat. Rev. Immunol. 2005, 5 (10), 749−759. (34) Karin, M.; Lawrence, T.; Nizet, V. Innate immunity gone awry: linking microbial infections to chronic inflammation and cancer. Cell 2006, 124 (4), 823−835. (35) Chen, C.; Shah, Y. M.; Morimura, K.; Krausz, K. W.; Miyazaki, M.; Richardson, T. A.; Morgan, E. T.; Ntambi, J. M.; Idle, J. R.; Gonzalez, F. J. Metabolomics reveals that hepatic stearoyl-CoA desaturase 1 downregulation exacerbates inflammation and acute colitis. Cell Metab. 2008, 7 (2), 135−147. (36) Dong, J.; Cai, X.; Zhao, L.; Xue, X.; Zou, L.; Zhang, X.; Liang, X. Lysophosphatidylcholine profiling of plasma: discrimination of isomers and discovery of lung cancer biomarkers. Metabolomics 2010, 6 (4), 478−488. (37) Zhao, Z.; Xiao, Y.; Elson, P.; Tan, H.; Plummer, S. J.; Berk, M.; Aung, P. P.; Lavery, I. C.; Achkar, J. P.; Li, L.; Casey, G.; Xu, Y. Plasma lysophosphatidylcholine levels: potential biomarkers for colorectal cancer. J. Clin. Oncol. 2007, 25 (19), 2696−2701. (38) 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. (39) Hung, N. D.; Sok, D.-E.; Kim, M. R. Prevention of 1-palmitoyl lysophosphatidylcholine-induced inflammation by polyunsaturated acyl lysophosphatidylcholine. Inflamm. Res. 2012, 61 (5), 473−383. (40) Mehta, D. Lysophosphatidylcholine: an enigmatic lysolipid. Am. J. Physiol. Lung Cell. Mol. Physiol. 2005, 289 (2), L174−L175. 5441

dx.doi.org/10.1021/pr300683a | J. Proteome Res. 2012, 11, 5433−5442

Journal of Proteome Research

Article

(41) Ojala, P. J.; Hirvonen, T. E.; Hermansson, M.; Somerharju, P.; Parkkinen, J. Acyl chain-dependent effect of lysophosphatidylcholine on human neutrophils. J. Leukocyte Biol. 2007, 82 (6), 1501−1509. (42) Huang, L. S.; Hung, N. D.; Sok, D.-E.; Kim, M. R. Lysophosphatidylcholine containing docosahexaenoic acid at the sn1 position is anti-inflammatory. Lipids 2010, 45 (3), 225−236. (43) Dobrzyn, P.; Dobrzyn, A. Stearoyl-CoA desaturase: a new therapeutic target of liver steatosis. Drug Dev. Res. 2006, 67 (8), 643− 650. (44) Stock, C.; Schilling, T.; Schwab, A.; Eder, C. Lysophosphatidylcholine stimulates IL-1 beta release from microglia via a P2X(7) receptor-independent mechanism. J. Immunol. 2006, 177 (12), 8560− 8568. (45) Liu-Wu, Y.; Hurt-Camejo, E.; Wiklund, O. Lysophosphatidylcholine induces the production of IL-1 beta by human monocytes. Atherosclerosis 1998, 137 (2), 351−357. (46) Lum, H.; Qiao, J.; Walter, R. J.; Huang, F.; Subbaiah, P. V.; Kim, K. S.; Holian, O. Inflammatory stress increases receptor for lysophosphatidylcholine in human microvascular endothelial cells. Am. J. Physiol. Heart Circ. Physiol 2003, 285 (4), H1786−H1789. (47) Hiley, C. R.; Hoi, P. M. Oleamide: a fatty acid amide signaling molecule in the cardiovascular system? Cardiovasc. Drug Rev. 2007, 25 (1), 46−60. (48) van der Poorten, D.; Shahidi, M.; Tay, E.; Sesha, J.; Tran, K.; McLeod, D.; Milliken, J. S.; Ho, V.; Hebbard, L. W.; Douglas, M. W.; George, J. Hepatitis C virus induces the cannabinoid receptor 1. PLoS One 2010, 5 (9), e12841. (49) Mukhopadhyay, B.; Cinar, R.; Yin, S.; Liu, J.; Tam, J.; Godlewski, G.; Harvey-White, J.; Mordi, I.; Cravatt, B. F.; Lotersztajn, S.; Gao, B.; Yuan, Q.; Schuebel, K.; Goldman, D.; Kunos, G. Hyperactivation of anandamide synthesis and regulation of cell-cycle progression via cannabinoid type 1 (CB(1)) receptors in the regenerating liver. Proc. Natl. Acad. Sci. U.S.A. 2011, 108 (15), 6323−6328. (50) Xie, C.; Liu, G.; Liu, J.; Huang, Z.; Wang, F.; Lei, X.; Wu, X.; Huang, S.; Zhong, D.; Xu, X. Anti-proliferative effects of anandamide in human hepatocellular carcinoma cells. Oncol. Lett. 2012, 4, 403− 407.

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dx.doi.org/10.1021/pr300683a | J. Proteome Res. 2012, 11, 5433−5442