Analytical and Bioanalytical Chemistry

, Volume 403, Issue 1, pp 203–213 | Cite as

Serum metabolomics reveals the deregulation of fatty acids metabolism in hepatocellular carcinoma and chronic liver diseases

  • Lina Zhou
  • Quancai Wang
  • Peiyuan Yin
  • Wenbin Xing
  • Zeming Wu
  • Shili Chen
  • Xin Lu
  • Yong Zhang
  • Xiaohui Lin
  • Guowang Xu
Original Paper


Patients with chronic liver diseases (CLD) including chronic hepatitis B and hepatic cirrhosis (CIR) are the major high-risk population of hepatocellular carcinoma (HCC). The differential diagnosis between CLD and HCC is a challenge. This work aims to study the related metabolic deregulations in HCC and CLD to promote the discovery of the differential metabolites for distinguishing the different liver diseases. Serum metabolic profiling analysis from patients with CLD and HCC was performed using a liquid chromatography–mass spectrometry system. The acquired large amount of metabolic information was processed with the random forest–recursive feature elimination method to discover important metabolic changes. It was found that long-chain acylcarnitines accumulated, whereas free carnitine, medium and short-chain acylcarnitines decreased with the severity of the non-malignant liver diseases, accompanied with corresponding alterations of enzyme activities. However, the general changing extent was smaller in HCC than in CIR, possibly due to the special energy-consumption mechanism of tumor cells. These observations may help to understand the mechanism of HCC occurrence and progression on the metabolic level and provide information for the identification of early and differential metabolic markers for HCC.


Hepatocellular carcinoma Metabolomic profiling Random forest–recursive feature elimination Acylcarnitine Fatty acid oxidation 



Quadrupole time-of-flight




Alanine transaminase


Aspartate transaminase


Carcinoma embryonic antigen


Chronic hepatitis B


Hepatic cirrhosis


Chronic liver diseases




Coenzyme A


Carnitine palmitoyl transferase 1


Carnitine palmitoyl transferase 2


Fatty acid oxidation


Glycocholic acid


Chenodeoxycholic acid glycine conjugate


Hepatitis B surface antigen


Hepatitis B virus


Hepatocellular carcinoma


Indoleamine 2,3-dioxygenase


Liquid chromatography–mass spectrometry system


Healthy controls




Quality control


Random forest


Recursive feature elimination


Random forest–recursive feature elimination


Rapid-resolution liquid chromatography


Stearoyl-CoA desaturase


Type 2 diabetes


Tricarboxylic acid


γ-glutamyl transpeptidase



The study has been supported by the State Key Science and Technology Project for Infectious Diseases (2008ZX10002-017 and 2008ZX10002-019) from State Ministry of Science and Technology of China, and the key foundation (no. 20835006) and the creative research group project (no. 21021004) from National Natural Science Foundation of China.


  1. 1.
    Feitelson M (1992) Hepatitis-B virus-infection and primary hepatocellular-carcinoma. Clin Microbiol Rev 5:275–301Google Scholar
  2. 2.
    Villanueva A, Newell P, Chiang DY, Friedman SL, Llovet JM (2007) Genomics and signaling pathways in hepatocellular carcinoma. Semin Liver Dis 27:55–76CrossRefGoogle Scholar
  3. 3.
    Farazi PA, DePinho RA (2006) Hepatocellular carcinoma pathogenesis: from genes to environment. Nat Rev Cancer 6:674–687CrossRefGoogle Scholar
  4. 4.
    Feitelson MA, Duan LX (1997) Hepatitis B virus× antigen in the pathogenesis of chronic infections and the development of hepatocellular carcinoma. Am J Pathol 150:1141–1157Google Scholar
  5. 5.
    Kao JH, Chen PJ, Lai MY, Chen DS (2003) Basal core promoter mutations of hepatitis B virus increase the risk of hepatocellular carcinoma in hepatitis B carriers. Gastroenterology 124:327–334CrossRefGoogle Scholar
  6. 6.
    Thorgeirsson SS, Grisham JW (2002) Molecular pathogenesis of human hepatocellular carcinoma. Nat Genet 31:339–346CrossRefGoogle Scholar
  7. 7.
    Wang XW, Hussain SP, Huo TI, Wu CG, Forgues M, Hofseth LJ, Brechot C, Harris CC (2002) Molecular pathogenesis of human hepatocellular carcinoma. Toxicology 181:43–47CrossRefGoogle Scholar
  8. 8.
    Brechot C (2004) Pathogenesis of hepatitis B virus-related hepatocellular carcinoma: old and new paradigms. Gastroenterology 127:S56–S61CrossRefGoogle Scholar
  9. 9.
    Marchio A, Meddeb M, Pineau P, Danglot G, Tiollais P, Bernheim A, Dejean A (1997) Recurrent chromosomal abnormalities in hepatocellular carcinoma detected by comparative genomic hybridization. Genes Chromosomes Cancer 18:59–65CrossRefGoogle Scholar
  10. 10.
    Yamashita T, Honda M, Takatori H, Nishino R, Minato H, Takamura H, Ohta T, Kaneko S (2009) Activation of lipogenic pathway correlates with cell proliferation and poor prognosis in hepatocellular carcinoma. J Hepatol 50:100–110CrossRefGoogle Scholar
  11. 11.
    Nicholson JK, Lindon JC, Holmes E (1999) ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29:1181–1189CrossRefGoogle Scholar
  12. 12.
    van der Greef J, Stroobant P, van der Heijden R (2004) The role of analytical sciences medical systems biology. Curr Opin Chem Biol 8:559–565CrossRefGoogle Scholar
  13. 13.
    Cobbold JFL, Patel JH, Goldin RD, North BV, Crossey MME, Fitzpatrick J, Wylezinska M, Thomas HC, Cox IJ, Taylor-Robinson SD (2010) Hepatic lipid profiling in chronic hepatitis C: an in vitro and in vivo proton magnetic resonance spectroscopy study. J Hepatol 52:16–24CrossRefGoogle Scholar
  14. 14.
    Shariff MIF, Gomaa AI, Cox IJ, Patel M, Williams HRT, Crossey MME, Thillainayagam AV, Thomas HC, Waked I, Khan SA, Taylor-Robinson SD (2011) Urinary metabolic biomarkers of hepatocellular carcinoma in an Egyptian population: a validation study. J Proteome Res 10:1828–1836CrossRefGoogle Scholar
  15. 15.
    Soga T, Sugimoto M, Honma M, Mori M, Igarashi K, Kashikura K, Ikeda S, Hirayama A, Yamamoto T, Yoshida H, Otsuka M, Tsuji S, Yatomi Y, Sakuragawa T, Watanabe H, Nihei K, Saito T, Kawata S, Suzuki H, Tomita M, Suematsu M (2011) Serum metabolomics reveals γ-glutamyl dipeptides as biomarkers for discrimination among different forms of liver disease. J Hepatol. doi: 10.1016/j.jhep.2011.01.031
  16. 16.
    Gao HC, Lu Q, Liu X, Cong H, Zhao LC, Wang HM, Lin DH (2009) Application of H-1 NMR-based metabonomics in the study of metabolic profiling of human hepatocellular carcinoma and liver cirrhosis. Cancer Sci 100:782–785CrossRefGoogle Scholar
  17. 17.
    Yin PY, Wan DF, Zhao CX, Chen J, Zhao XJ, Wang WZ, Lu X, Yang SL, Gu JR, Xu GW (2009) A metabonomic study of hepatitis B-induced liver cirrhosis and hepatocellular carcinoma by using RP-LC and HILIC coupled with mass spectrometry. Mol Biosyst 5:868–876CrossRefGoogle Scholar
  18. 18.
    Handrick V, Vogt T, Frolov A (2010) Profiling of hydroxycinnamic acid amides in Arabidopsis thaliana pollen by tandem mass spectrometry. Anal Bioanal Chem 398:2789–2801CrossRefGoogle Scholar
  19. 19.
    Yin P, Zhao X, Li Q, Wang J, Li J, Xu G (2006) Metabonomics study of intestinal fistulas based on ultraperformance liquid chromatography coupled with Q-TOF mass spectrometry (UPLC/Q-TOF MS). J Proteome Res 5:2135–2143CrossRefGoogle Scholar
  20. 20.
    Zhang X, Wei D, Yap Y, Li L, Guo S, Chen F (2007) Mass spectrometry-based “omics” technologies in cancer diagnostics. Mass Spectrom Rev 26:403–431CrossRefGoogle Scholar
  21. 21.
    Dunn WB, Bailey NJC, Johnson HE (2005) Measuring the metabolome: current analytical technologies. Analyst 130:606–625CrossRefGoogle Scholar
  22. 22.
    Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRefGoogle Scholar
  23. 23.
    Xu P, Jelinek F (2007) Random forests and the data sparseness problem in language modeling. Comput Speech Lang 21:105–152CrossRefGoogle Scholar
  24. 24.
    Pang H, Lin A, Holford M, Enerson BE, Lu B, Lawton MP, Floyd E, Zhao H (2006) Pathway analysis using random forests classification and regression. Bioinformatics 22:2028–2036CrossRefGoogle Scholar
  25. 25.
    Truong Y, Lin X, Beecher C (2004) Learning a complex metabolomic dataset using random forests and support vector machines. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, Seattle, WA, USAGoogle Scholar
  26. 26.
    Menze BH, Kelm BM, Masuch R, Himmelreich U, Bachert P, Petrich W, Hamprecht FA (2009) A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics 10:213–228CrossRefGoogle Scholar
  27. 27.
    Strobl C, Boulesteix A-L, Zeileis A, Hothorn T (2007) Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics 8:25–45CrossRefGoogle Scholar
  28. 28.
    Granitto P, Furlanello C, Biasioli F, Gasperi F (2006) Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemom Intell Lab Syst 83:83–90CrossRefGoogle Scholar
  29. 29.
    Gika HG, Theodoridis GA, Wingate JE, Wilson ID (2007) Within-day reproducibility of an HPLC-MS-based method for metabonomic analysis: application to human urine. J Proteome Res 6:3291–3303CrossRefGoogle Scholar
  30. 30.
    Smilde AK, van der Werf MJ, Bijlsma S, van der Werff-van-der Vat BJC, Jellema RH (2005) Fusion of mass spectrometry-based metabolomics data. Anal Chem 77:6729–6736CrossRefGoogle Scholar
  31. 31.
    Chandra B, Kothari R, Paul P (2010) A new node splitting measure for decision tree construction. Pattern Recogn 43:2725–2731CrossRefGoogle Scholar
  32. 32.
    Chen J, Zhao X, Fritsche J, Yin P, Schmitt-Kopplin P, Wang W, Lu X, Haring HU, Schleicher ED, Lehmann R, Xu G (2008) 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 80:1280–1289CrossRefGoogle Scholar
  33. 33.
    Bordner AJ, Gorin AA (2008) Comprehensive inventory of protein complexes in the Protein Data Bank from consistent classification of interfaces. BMC Bioinformatics 9:234–244CrossRefGoogle Scholar
  34. 34.
    Moroni F (1999) Tryptophan metabolism and brain function: focus on kynurenine and other indole metabolites. Eur J Pharmacol 375:87–100CrossRefGoogle Scholar
  35. 35.
    Wolf H (1974) Studies on tryptophan metabolism in man—effect of hormones and vitamin-B6 on urinary-excretion of metabolites of kynurenine pathway. Scand J Clin Lab Invest 33:1664–1667Google Scholar
  36. 36.
    Taylor MW, Feng GS (1991) Relationship between interferon-gamma, indoleamine 2,3-dioxygenase, and tryptophan catabolism. FASEB J 5:2516–2522Google Scholar
  37. 37.
    Yoshida R, Urade Y, Tokuda M, Hayaishi O (1979) Induction of indoleamine 2,3-dioxygenase in mouse lung during virus-infection. Proc Natl Acad Sci USA 76:4084–4086CrossRefGoogle Scholar
  38. 38.
    Heyes MP, Saito K, Lackner A, Wiley CA, Achim CL, Markey SP (1998) Sources of the neurotoxin quinolinic acid in the brain of HIV-1-infected patients and retrovirus-infected macaques. FASEB J 12:881–896Google Scholar
  39. 39.
    Takikawa O, Kuroiwa T, Yamazaki F, Kido R (1988) Mechanism of interferon-gamma action-characterization of indoleamine 2,3-dioxygenase in cultured human-cells induced by interferon-gamma and evaluation of the enzyme-mediated tryptophan degradation in its anticellular activity. J Biol Chem 263:2041–2048Google Scholar
  40. 40.
    Eyigun CP, Dayan S, Sengul A, Ozguven V, Alga OH, Hacibetkasoglu A (1995) Sera cortisol levels in patients with chronic hepatitis B virus infection and its prognostic significance. Mikrobiyol Bul 29:388–396Google Scholar
  41. 41.
    Gaillard MC, Song E, Nogueira CM, Kilroesmith TA (1995) Elastase binding-capacity of alpha(2)-macroglobulin and its association with glucocorticoid concentration in southern African black patients with hepatocellular-carcinoma. Clin Chim Acta 240:179–185CrossRefGoogle Scholar
  42. 42.
    Longo N, Filippo CAD, Pasquali M (2006) Disorders of carnitine transport and the carnitine cycle. Am J Med Genet C Semin Med Genet 142C:77–85CrossRefGoogle Scholar
  43. 43.
    Mihalik SJ, Goodpaster BH, Kelley DE, Chace DH, Vockley J, Toledo FGS, DeLany JP (2010) Increased levels of plasma acylcarnitines in obesity and type 2 diabetes and identification of a marker of glucolipotoxicity. Obesity 18:1695–1700CrossRefGoogle Scholar
  44. 44.
    Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144:646–674CrossRefGoogle Scholar
  45. 45.
    Cao HM, Gerhold K, Mayers JR, Wiest MM, Watkins SM, Hotamisligil GS (2008) Identification of a lipokine, a lipid hormone linking adipose tissue to systemic metabolism. Cell 134:933–944CrossRefGoogle Scholar
  46. 46.
    Dobrzyn P, Dobrzyn A (2006) Stearoyl-CoA desaturase: a new therapeutic target of liver steatosis. Drug Dev Res 67:643–650CrossRefGoogle Scholar
  47. 47.
    Louet JF, Chatelain F, Decaux JF, Park EA, Kohl C, Pineau T, Girard J, Pegorier JP (2001) Long-chain fatty acids regulate liver carnitine palmitoyltransferase I gene (L-CPT I) expression through a peroxisome-proliferator-activated receptor alpha (PPAR alpha)-independent pathway. Biochem J 354:189–197CrossRefGoogle Scholar
  48. 48.
    Seelaender MCL, Curi R, Colquhoun A, Williams JF, Zammitt VA (1998) Carnitine palmitoyltransferase II activity is decreased in liver mitochondria of cachectic rats bearing the Walker 256 carcinosarcoma: effect of indomethacin treatment. Biochem Mol Biol Int 44:185–193Google Scholar
  49. 49.
    Makowski L, Noland RC, Koves TR, Xing WB, Ilkayeva OR, Muehlbauer MJ, Stevens RD, Muoio DM (2009) Metabolic profiling of PPAR alpha(−/−) mice reveals defects in carnitine and amino acid homeostasis that are partially reversed by oral carnitine supplementation. FASEB J 23:586–604CrossRefGoogle Scholar
  50. 50.
    Chang BJ, Nishikawa M, Nishiguchi S, Inoue M (2005) l-carnitine inhibits hepatocarcinogenesis via protection of mitochondria. Int J Cancer 113:719–729CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Lina Zhou
    • 1
  • Quancai Wang
    • 2
  • Peiyuan Yin
    • 1
  • Wenbin Xing
    • 3
  • Zeming Wu
    • 1
  • Shili Chen
    • 1
  • Xin Lu
    • 1
  • Yong Zhang
    • 3
  • Xiaohui Lin
    • 2
  • Guowang Xu
    • 1
  1. 1.CAS Key Laboratory of Separation Science for Analytical ChemistryDalian Institute of Chemical Physics, Chinese Academy of SciencesDalianChina
  2. 2.School of Computer Science and TechnologyDalian University of TechnologyDalianChina
  3. 3.The Sixth People’s HospitalDalianChina

Personalised recommendations