自发性腹膜炎的代谢组学研究
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摘要
背景：肝硬化是常见的内科疾病，约60%的失代偿期肝硬化患者10年内出现腹水，在此基础上常并发自发性腹膜炎。肝硬化失代偿期并发自发性腹膜炎(SBP)是在腹腔及邻近组织无感染源(如腹腔脓肿、肝癌、急性胰腺炎、胆囊炎、肠穿孔等)情况下发生的腹腔感染[1]。一经诊断，其死亡率为90%，但早期诊断及治疗可降低到大约20%。但由于大多数自发性腹膜炎患者的临床表现不典型，与无并发症腹水患者鉴别较困难，且目前生化指标多缺乏特异性、灵敏度又差，甚易漏诊。因此寻找新的诊断自发性腹膜炎的方法，对降低肝硬化患者的病死率临床价值较大。代谢组学是一门新兴的学科，作为一项全新的研究手段，可以从代谢的角度对肝病进行系统研究。目前代谢组学已广泛应用于发现疾病机理、发现跟疾病相关的生物标志物（biomarker）、开展临床药理学、以及毒理学的研究、环境科学等领域[2,3]。鉴于肝脏在代谢方面的重要地位，应用代谢组学对肝硬化病人的腹水进行研究分析，对自发性腹膜炎患者的早期诊断及预后具有指导意义。
目的：利用代谢组学分析方法比较自发性腹膜炎与肝硬化无并发症腹水患者腹水标本之间代谢轮廓的差别，发现可作为自发性腹膜炎诊断及预后的潜在生物学标志物，证明代谢组学方法可作为肝硬化并发自发性腹膜炎患者的新的诊疗方法。
方法：利用高效液相色谱-串联质谱联用平台对30例肝硬化腹水患者及26例肝硬化并发自发性腹膜炎的患者的腹水进行分析，原始数据经Markerview软件进行预处理得到二维矩阵形式，然后导入Simca-P软件(Umetrics AB, Umea,Sweden)，运用主成分分析(PCA)及正交偏最小二乘法(OPLS)分析，通过得分图、S图挖掘潜在的生物学标志物。
结果：研究共发现6种物质在两组间差异较大。其中，仅棕榈酰胺1种物质出现在SBP组，在肝硬化无并发症组为零；5种物质在肝硬化无并发症组高于SBP组，分别是肌酐、单酰甘油酯、溶血磷脂酰胆碱(18:0)、溶血磷脂酰胆碱（18:1）、维生素E的水溶性代谢物。溶血磷脂酰胆碱(18:0)在SBP组中几乎为零。棕榈酰胺和溶血磷脂酰胆碱(18:0)成为标志物的可能性更大。
结论：
（1）建立了以HPLC-MS/MS为基础的腹水代谢组学研究方法；
（2）研究共发现6种物质可作为自发性腹膜炎的潜在标志物。分别是：棕榈酰胺、肌酐、单酰甘油酯、溶血磷脂酰胆碱(18:0)、溶血磷脂酰胆碱（18:1）、维生素E的水溶性代谢物。棕榈酰胺和溶血磷脂酰胆碱(18:0)成为标志物的可能性更大。
Background: Ascites is the most common complication of cirrhosis, and60%of patients with compensated cirrhosis develop ascites within10yearsduring the course of their disease.Spontaneous bacterial peritonitis（SBP）is avery common bacterial infection in patients with cirrhosis and ascites.Whenfirst described, its mortality exceeded90%but it has been reduced toapproximately20%with early diagnosis and treatment. Since theidentification between SBP and uncomplicated ascites is complex and difficult,many patients miss the best treatment window．Therefore，utilizing newtechnologies to look for new biomarkers of SBP has a wide clinical applicationprospect in reducing the mortality, metabolomics is an emerging discipline.Asan new method, it can carry out systematic research on liverdisease from a metabolic point of view. Metabonomics has been established asan extremely powerful analytical tool and hence found successful applicationsin many research areas including molecular pathology and physiology，drugefficacy and toxicity， discovery of biomarkers related to disease， andenvironmental sciences．Since the important function in the metabolism ofliver,this study，by analyzing the uncomplicated ascites and ascites with SBPpatients ascites metabolite profile，has certain guiding significance in earlydiagnosis and prognosis of SBP．
Objective: Our study used a metabolomics research platform to analyzethe different metabolic substances in the ascite of cirrhosis and SBP,aimed tofind potential biomarkers,and finally demonstrated metabolomics can be aneffective method to diagnose SBP.
Methods: Our study applied high perform liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS) to analyze the ascite sampleof56cirrhosis patients, of which30samples were obtained from patiens withuncomplicated ascites and26SBP. The raw date was converted to NetCDFformat by Markerview.To reflect the differences between uncomplicated ascitesand SBP，a multivariate statistical analysis was performed using SIMCA-Psoftware version12.0(Umetrics AB, Umea,Sweden). Principle componentanalysis (PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) models were constructed with RPLCdata. Protential biomarkerswere selected according to Variable importance Project (VIP) in the value, theloading plot and the Score plot.
Results:6biomarkers were found in total. Palmitic amide had a higherlevel in SBP patients,while the rest biomarkers had a higher level inuncomplicated ascites groups than in SBP groups, they are Creatinine,MG(20:5),LysoPC(18:1),LysoPC(18:1),Alpha-CEHC.Palm ceramide and lysophosphatidylcholine (18:0) are more likely to be known as the markers.
Conclusion:
(1) The study reports a metabolomic methods based on HPLC-MS/MS inthe study of ascites specimen；
(2)6biomarkers were found in total. They are Palmitic amide,Creatinine,MG(20:5),LysoPC(18:1),LysoPC(18:1),Alpha-CEHC.Palm ceramide and lysophosphatidylcholine (18:0) are more likely to be known as the markers.
目的：利用代谢组学分析方法比较自发性腹膜炎与肝硬化无并发症腹水患者腹水标本之间代谢轮廓的差别，发现可作为自发性腹膜炎诊断及预后的潜在生物学标志物，证明代谢组学方法可作为肝硬化并发自发性腹膜炎患者的新的诊疗方法。
方法：利用高效液相色谱-串联质谱联用平台对30例肝硬化腹水患者及26例肝硬化并发自发性腹膜炎的患者的腹水进行分析，原始数据经Markerview软件进行预处理得到二维矩阵形式，然后导入Simca-P软件(Umetrics AB, Umea,Sweden)，运用主成分分析(PCA)及正交偏最小二乘法(OPLS)分析，通过得分图、S图挖掘潜在的生物学标志物。
结果：研究共发现6种物质在两组间差异较大。其中，仅棕榈酰胺1种物质出现在SBP组，在肝硬化无并发症组为零；5种物质在肝硬化无并发症组高于SBP组，分别是肌酐、单酰甘油酯、溶血磷脂酰胆碱(18:0)、溶血磷脂酰胆碱（18:1）、维生素E的水溶性代谢物。溶血磷脂酰胆碱(18:0)在SBP组中几乎为零。棕榈酰胺和溶血磷脂酰胆碱(18:0)成为标志物的可能性更大。
结论：
（1）建立了以HPLC-MS/MS为基础的腹水代谢组学研究方法；
（2）研究共发现6种物质可作为自发性腹膜炎的潜在标志物。分别是：棕榈酰胺、肌酐、单酰甘油酯、溶血磷脂酰胆碱(18:0)、溶血磷脂酰胆碱（18:1）、维生素E的水溶性代谢物。棕榈酰胺和溶血磷脂酰胆碱(18:0)成为标志物的可能性更大。
Background: Ascites is the most common complication of cirrhosis, and60%of patients with compensated cirrhosis develop ascites within10yearsduring the course of their disease.Spontaneous bacterial peritonitis（SBP）is avery common bacterial infection in patients with cirrhosis and ascites.Whenfirst described, its mortality exceeded90%but it has been reduced toapproximately20%with early diagnosis and treatment. Since theidentification between SBP and uncomplicated ascites is complex and difficult,many patients miss the best treatment window．Therefore，utilizing newtechnologies to look for new biomarkers of SBP has a wide clinical applicationprospect in reducing the mortality, metabolomics is an emerging discipline.Asan new method, it can carry out systematic research on liverdisease from a metabolic point of view. Metabonomics has been established asan extremely powerful analytical tool and hence found successful applicationsin many research areas including molecular pathology and physiology，drugefficacy and toxicity， discovery of biomarkers related to disease， andenvironmental sciences．Since the important function in the metabolism ofliver,this study，by analyzing the uncomplicated ascites and ascites with SBPpatients ascites metabolite profile，has certain guiding significance in earlydiagnosis and prognosis of SBP．
Objective: Our study used a metabolomics research platform to analyzethe different metabolic substances in the ascite of cirrhosis and SBP,aimed tofind potential biomarkers,and finally demonstrated metabolomics can be aneffective method to diagnose SBP.
Methods: Our study applied high perform liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS) to analyze the ascite sampleof56cirrhosis patients, of which30samples were obtained from patiens withuncomplicated ascites and26SBP. The raw date was converted to NetCDFformat by Markerview.To reflect the differences between uncomplicated ascitesand SBP，a multivariate statistical analysis was performed using SIMCA-Psoftware version12.0(Umetrics AB, Umea,Sweden). Principle componentanalysis (PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) models were constructed with RPLCdata. Protential biomarkerswere selected according to Variable importance Project (VIP) in the value, theloading plot and the Score plot.
Results:6biomarkers were found in total. Palmitic amide had a higherlevel in SBP patients,while the rest biomarkers had a higher level inuncomplicated ascites groups than in SBP groups, they are Creatinine,MG(20:5),LysoPC(18:1),LysoPC(18:1),Alpha-CEHC.Palm ceramide and lysophosphatidylcholine (18:0) are more likely to be known as the markers.
Conclusion:
(1) The study reports a metabolomic methods based on HPLC-MS/MS inthe study of ascites specimen；
(2)6biomarkers were found in total. They are Palmitic amide,Creatinine,MG(20:5),LysoPC(18:1),LysoPC(18:1),Alpha-CEHC.Palm ceramide and lysophosphatidylcholine (18:0) are more likely to be known as the markers.
引文
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[2] Hellmold H, Nilsson C B, Schuppe-Koistinen I, et al.Identification of endpoints relevant to detection of potentially adverse drug reactions[J].Toxicology Letters,2002,127(1-3):239-243.
[3] Nicholson JK,Lindon JC.Systems biology: Metabonomics[J]. Nature,2008,455(7216):1054-1056.
[4] Filik L,Unal S．Clinical and laboratory features of spontaneous bacterialperitonitis[J]．East Afr Med J,2004,81(9):474-479．
[5] European Association for the Study of the Liver.EASL clinical practiceguidelines on the management of ascites, spontaneous bacterial peritonitis,and hepatorenal syndrome in cirrhosis [J].J Hepatol,2010,53(3)：397-417.
[6] Nicholson J K, Lindon J C,Holmes E.“Metabonomics”: Understanding themetabolic responses of living systems to pathophy siological stimuli viamultivariate statistical analysis of biological NMR spectroscopic data[J].Xenobiotica,1999,29(11):1181-1189.
[7] Fiehn O, Kopka J, Dormann P, et al.Metabolite profiling for plant functionalgenomics[J].Nat Biotechnol,2000,18(11):1157-1161.
[8] Taylor J, King R D, Altmann T, et a1. Application of metabolomics to plantgenotype discrimination using statistics and machine learning[J].Bioinf-ornutics,2002,l8(suppI2):241-248.
[9] Gullberg J,Jonsson P,Nordstrom A,et al.Design of experiments: an efficientstrategy to identify factors influencing extraction and derivatization ofArabidopsis thaliana samples in metabolomic studies with gas chroma-tography/mass spectrometry[J].Anal Biochem,2004,331(2):283-295.
[10]Boernsen KO，Gatzek S，Imbert G.Controlled protein precipitation incombination with chip-based nanospray infusion mass spectrometry. Anapproach for metabolomics profiling of plasma[J].Anal Chem,2005,77(22):7255-7264.
[11]Dettmer K, Ar onov PA, Hammock BD.Mass spectr ometry–basedmetabolomics[J].Mass Spectr om Rev,2007,26(1):51-78.
[12]Villas-Boas SG, Mas S, Akess on M, et al.Mass s pectrometry inmetabolome analysis [J].Mass Spectrom Rev,2005,24(5):613-646.
[13]Goodacre R, Vaidyanathan S, Dunn WB, et al.Metabolomics bynumbers:acquiring and understanding global metabolite data[J].TrendsBiotechnol,2004,22(5):245-252.
[14]Brown M, Dunn WB, Ellis D I, et al.A metabolome pipeline: from conceptto data to knowledge [J].Metabolomics,2005,1(1):39-51.
[15]Yang J, Xu G, Zheng Y, et al.Strategy for metabonomics research based onhigh-performance liquid chromatography and liquid chromat ographycoupled with tandem mass spectrometry[J].J Chromatogr A,2005,1084(1-2):214-221.
[16]Smith CA, Want EJ, O′Maille G, et al1XCMS: processing massspectrometry data for metabolite profiling using non linear peak alignment,matching, and identification [J].Anal Chem,2006,78(3):779-787.
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[19]Glinski M, Weckwerth W. The role of mass spectrometry in plant systemsbiology [J]. Mass Spectrom Rev,2006,25(2):173-214.
[20]Denkert C, Budczies J, Kind T, et al. Mass spectrometry based metabolicprofiling reveals different metabolite patterns in invasive ovariancarcinomas and ovarian borderline tumors [J].Cancer Res,2006,66(22):10795-10804.
[21]Styczynski MP, Moxley JF, Tong LV, et al. Systematic identification ofconserved metabolites in GC/MS data for metabolomics and biomarkerdiscovery[J]. J Anal Chem2007,79(3):966-973.
[22]Lenz EM, Wilson ID. Analytical strategies in metabonomics[J]. ProteomeRes,2007,6(2):443-458.
[23]Katajamaa M, Oresic M. Data processing for mass spectrometry-basedmetabolomics[J]. J Chromatography A,2007,1158(1-2):318-328.
[24]Hastings CA, Norton SM, Roy S. New algorithms for processing and peakdetection in liquid chromatography/mass spectrometry data[J]. RapidCommun Mass Spectrom,2002,16(5):462-467.
[25]Weckwerth W, Morgenthal K. Metabolomics: from pattern recognition tobiological interpretation[J].Drug Discov Today,2005,10(22):1551-1558.
[26]Waters NJ, Holmes E, Williams A, Waterfield CJ, Farrant RD, NicholsonJK. NMR and pattern recognition studies on the time-related metaboliceffects of alpha-naphthylisothiocyanate on liver, urine, and plasma in therat: an integrative metabonomic approach[J].Chem Res Tox icol,2001,14(10):1401-1412.
[27]Azmi J, Griffin JL, Antti H, et al. Metabolic trajectory characterisation ofxenobiotic-induced hepatotoxic lesions using statistical batch processingof NMR data[J]. Analyst,2002,127(2):271-276.
[28]Nicholls AW, Holmes E, Lindon JC, et al. Metabonomic investigations intohydrazine toxicity in the rat[J]. Chem Res Toxicol,2001,14(8):975-987.
[29]Heijne WH, Lamers RJ, van Bladeren PJ, et al. Profiles of metabolites andgene expression in rats with chemically induced hepaticnecrosis[J].Toxicol Pathol,2005,33(4):425-433.
[30]Kumar BS, Chung BC, Kwon OS, et al. Discovery of common urinarybiomarkers for hepatotoxicity induced by carbon tetrachloride, acetamin-ophen and methotrexate by mass spectrometry-based metabolomics[J].JAppl Toxicol,2012,32(7):505-520.
[31]Bjornsson E, Angulo P.Non-alcoholic fatty liver disease[J].Scand JGastroenterol,2007,42(9):1023-1030.
[32]Barr J, Vazquez-Chantada M, Alonso C, et al. Liquid chromatography-massspectrometry-based parallel metabolic profiling of human and mousemodel serum reveals putative biomarkers associated with the progressionof nonalcoholic fatty liver disease[J].J Proteome Res,2010,9(9):4501-4512.
[33]Li H, Wang L, Yan X,et al.A proton nuclear magnetic resonancemetabonomics approach for biomarker discovery in nonalcoholic fattyliver disease [J]. J Proteome Res,2011,10(6):2797-2806.
[34]Yin PY,Wang DF,Zhang CX,etal. A metabonomic study of hepatitisB-induced liver cirrhosis and hepatocellular carcinoma by using RP-LCand HILIC coupled with mass spectrometry[J].MolBiosyst,2009,5(8):868-876.
[35]Chen J, Wang WZ, Lv S,etal. Metabonomics study of liver cancer basedon ultra performance liquid chromatography coupled to mass spectrometrywith HILIC and RPLC separations[J]. Analytica ChimicaActa,2009,650(1):3-9.
[36]Chen T, Xie G, Wang X,etal. Serum and urine metabolite profiling revealspotential biomarkers of human hepatocellular carcinoma[J].Mol CellProteomics,2011,10(7):
[37]高洁.肝细胞肝癌患者腹水潜在代谢标志物的研究[D].天津：天津医科大学，2011.
[38]Yang J, Zhao X, Liu X, et al. High performance liquid chromatography-mass spectrometry for metabonomics: potential biomarkers for acutedeterioration of liver function in chronic hepatitis B[J]. J ProteomeRes,2006,5(3):554-561.
[39]郝绍瑞.人工肝治疗慢加急（亚急）性乙肝肝衰竭的代谢组学研究[D].浙江：浙江大学,2011.
[40]Wiklund S, Johansson E, Sj str m L,et al.Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interes-ting compounds using OPLS class models[J].Anal Chem,2008,80(1)：115-122.
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[2] Hellmold H, Nilsson C B, Schuppe-Koistinen I, et al.Identification of endpoints relevant to detection of potentially adverse drug reactions[J].Toxicology Letters,2002,127(1-3):239-243.
[3] Nicholson JK,Lindon JC.Systems biology: Metabonomics[J]. Nature,2008,455(7216):1054-1056.
[4] Filik L,Unal S．Clinical and laboratory features of spontaneous bacterialperitonitis[J]．East Afr Med J,2004,81(9):474-479．
[5] European Association for the Study of the Liver.EASL clinical practiceguidelines on the management of ascites, spontaneous bacterial peritonitis,and hepatorenal syndrome in cirrhosis [J].J Hepatol,2010,53(3)：397-417.
[6] Nicholson J K, Lindon J C,Holmes E.“Metabonomics”: Understanding themetabolic responses of living systems to pathophy siological stimuli viamultivariate statistical analysis of biological NMR spectroscopic data[J].Xenobiotica,1999,29(11):1181-1189.
[7] Fiehn O, Kopka J, Dormann P, et al.Metabolite profiling for plant functionalgenomics[J].Nat Biotechnol,2000,18(11):1157-1161.
[8] Taylor J, King R D, Altmann T, et a1. Application of metabolomics to plantgenotype discrimination using statistics and machine learning[J].Bioinf-ornutics,2002,l8(suppI2):241-248.
[9] Gullberg J,Jonsson P,Nordstrom A,et al.Design of experiments: an efficientstrategy to identify factors influencing extraction and derivatization ofArabidopsis thaliana samples in metabolomic studies with gas chroma-tography/mass spectrometry[J].Anal Biochem,2004,331(2):283-295.
[10]Boernsen KO，Gatzek S，Imbert G.Controlled protein precipitation incombination with chip-based nanospray infusion mass spectrometry. Anapproach for metabolomics profiling of plasma[J].Anal Chem,2005,77(22):7255-7264.
[11]Dettmer K, Ar onov PA, Hammock BD.Mass spectr ometry–basedmetabolomics[J].Mass Spectr om Rev,2007,26(1):51-78.
[12]Villas-Boas SG, Mas S, Akess on M, et al.Mass s pectrometry inmetabolome analysis [J].Mass Spectrom Rev,2005,24(5):613-646.
[13]Goodacre R, Vaidyanathan S, Dunn WB, et al.Metabolomics bynumbers:acquiring and understanding global metabolite data[J].TrendsBiotechnol,2004,22(5):245-252.
[14]Brown M, Dunn WB, Ellis D I, et al.A metabolome pipeline: from conceptto data to knowledge [J].Metabolomics,2005,1(1):39-51.
[15]Yang J, Xu G, Zheng Y, et al.Strategy for metabonomics research based onhigh-performance liquid chromatography and liquid chromat ographycoupled with tandem mass spectrometry[J].J Chromatogr A,2005,1084(1-2):214-221.
[16]Smith CA, Want EJ, O′Maille G, et al1XCMS: processing massspectrometry data for metabolite profiling using non linear peak alignment,matching, and identification [J].Anal Chem,2006,78(3):779-787.
[17]Lindon JC, Nichols on JK, Holmes E, et al.Summary recommendations forstandardization and reporting of metabolic analyses[J].NatBiotechnol,2005,23(7):833–838.
[18]杨军,宋硕林, Castr o-Perez J,等.代谢组学及其应用[J].生物工程学报,2005,21(1):1-5.
[19]Glinski M, Weckwerth W. The role of mass spectrometry in plant systemsbiology [J]. Mass Spectrom Rev,2006,25(2):173-214.
[20]Denkert C, Budczies J, Kind T, et al. Mass spectrometry based metabolicprofiling reveals different metabolite patterns in invasive ovariancarcinomas and ovarian borderline tumors [J].Cancer Res,2006,66(22):10795-10804.
[21]Styczynski MP, Moxley JF, Tong LV, et al. Systematic identification ofconserved metabolites in GC/MS data for metabolomics and biomarkerdiscovery[J]. J Anal Chem2007,79(3):966-973.
[22]Lenz EM, Wilson ID. Analytical strategies in metabonomics[J]. ProteomeRes,2007,6(2):443-458.
[23]Katajamaa M, Oresic M. Data processing for mass spectrometry-basedmetabolomics[J]. J Chromatography A,2007,1158(1-2):318-328.
[24]Hastings CA, Norton SM, Roy S. New algorithms for processing and peakdetection in liquid chromatography/mass spectrometry data[J]. RapidCommun Mass Spectrom,2002,16(5):462-467.
[25]Weckwerth W, Morgenthal K. Metabolomics: from pattern recognition tobiological interpretation[J].Drug Discov Today,2005,10(22):1551-1558.
[26]Waters NJ, Holmes E, Williams A, Waterfield CJ, Farrant RD, NicholsonJK. NMR and pattern recognition studies on the time-related metaboliceffects of alpha-naphthylisothiocyanate on liver, urine, and plasma in therat: an integrative metabonomic approach[J].Chem Res Tox icol,2001,14(10):1401-1412.
[27]Azmi J, Griffin JL, Antti H, et al. Metabolic trajectory characterisation ofxenobiotic-induced hepatotoxic lesions using statistical batch processingof NMR data[J]. Analyst,2002,127(2):271-276.
[28]Nicholls AW, Holmes E, Lindon JC, et al. Metabonomic investigations intohydrazine toxicity in the rat[J]. Chem Res Toxicol,2001,14(8):975-987.
[29]Heijne WH, Lamers RJ, van Bladeren PJ, et al. Profiles of metabolites andgene expression in rats with chemically induced hepaticnecrosis[J].Toxicol Pathol,2005,33(4):425-433.
[30]Kumar BS, Chung BC, Kwon OS, et al. Discovery of common urinarybiomarkers for hepatotoxicity induced by carbon tetrachloride, acetamin-ophen and methotrexate by mass spectrometry-based metabolomics[J].JAppl Toxicol,2012,32(7):505-520.
[31]Bjornsson E, Angulo P.Non-alcoholic fatty liver disease[J].Scand JGastroenterol,2007,42(9):1023-1030.
[32]Barr J, Vazquez-Chantada M, Alonso C, et al. Liquid chromatography-massspectrometry-based parallel metabolic profiling of human and mousemodel serum reveals putative biomarkers associated with the progressionof nonalcoholic fatty liver disease[J].J Proteome Res,2010,9(9):4501-4512.
[33]Li H, Wang L, Yan X,et al.A proton nuclear magnetic resonancemetabonomics approach for biomarker discovery in nonalcoholic fattyliver disease [J]. J Proteome Res,2011,10(6):2797-2806.
[34]Yin PY,Wang DF,Zhang CX,etal. A metabonomic study of hepatitisB-induced liver cirrhosis and hepatocellular carcinoma by using RP-LCand HILIC coupled with mass spectrometry[J].MolBiosyst,2009,5(8):868-876.
[35]Chen J, Wang WZ, Lv S,etal. Metabonomics study of liver cancer basedon ultra performance liquid chromatography coupled to mass spectrometrywith HILIC and RPLC separations[J]. Analytica ChimicaActa,2009,650(1):3-9.
[36]Chen T, Xie G, Wang X,etal. Serum and urine metabolite profiling revealspotential biomarkers of human hepatocellular carcinoma[J].Mol CellProteomics,2011,10(7):
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