A weighted relative difference accumulation algorithm for dynamic metabolomics data: Long-term elevated bile acids are risk factors for hepatocellular carcinoma

Scientific Reports

Volumn 5, Issue , 2015, Pages

  Zhang, Weijian ^a   Zhou, Lina ^b   Yin, Peiyuan ^b   Wang, Jinbing ^c   Lu, Xin ^b   Wang, Xiaomei ^a   Chen, Jianguo ^c   Lin, Xiaohui ^a   Xu, Guowang ^b  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

^b DALIAN INSTITUTE OF CHEMICAL PHYSICS   (China)

^c QIDONG LIVER CANCER INSTITUTE   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BILE ACID;

ALGORITHM; ANIMAL; BIOLOGICAL MODEL; CARCINOMA, HEPATOCELLULAR; DISEASE MODEL; HUMAN; LIVER NEOPLASMS; METABOLISM; METABOLOMICS; PROCEDURES; RAT; RISK FACTOR;

ALGORITHMS; ANIMALS; BILE ACIDS AND SALTS; CARCINOMA, HEPATOCELLULAR; DISEASE MODELS, ANIMAL; HUMANS; LIVER NEOPLASMS; METABOLOMICS; MODELS, BIOLOGICAL; RATS; RISK FACTORS;

EID: 84924778239     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep08984     Document Type: Article

References (61)

1. Kim, K. et al. Urine Metabolomic Analysis Identifies Potential Biomarkers and Pathogenic Pathways in Kidney Cancer. Omics 15, 293-303 (2011).
2. Caudle, W. M., Bammler, T. K., Lin, Y., Pan, S. & Zhang, J. Using 'omics' to define pathogenesis and biomarkers of Parkinson's disease. Expert Rev. Neurother 10, 925-942 (2010).
3. Nicholson, J. K., Connelly, J., Lindon, J. C. & Holmes, E. Metabonomics: a platform for studying drug toxicity and gene function. Nat. Rev. Drug Discov. 1, 153-161 (2002).
4. Patti, G. J., Yanes, O. & Siuzdak, G. Innovation: Metabolomics: the apogee of the omics trilogy. Nat. Rev. Mol. Cell Biol. 13, 263-269 (2012).
5. Fu, T.-c. A review on time series data mining. Eng. Appl. Artif. Intell. 24, 164-181 (2011).
6. Smilde, A. et al. Dynamic metabolomic data analysis: a tutorial review. Metabolomics 6, 3-17 (2010).
7. Berk, M., Ebbels, T. & Montana, G. A statistical framework for biomarker discovery in metabolomic time course data. Bioinformatics 27, 1979-1985 (2011).
8. Ghandhi, S. A., Sinha, A., Markatou, M. & Amundson, S. A. Time-series clustering of gene expression in irradiated and bystander fibroblasts: an application of FBPA clustering. BMC Genomics, doi:10.1186/1471-2164-12-2 (2011).
9. Spegel, P. et al. Time-resolved metabolomics analysis of beta-cells implicates the pentose phosphate pathway in the control of insulin release. Biochem. J. 450, 595-605 (2013).
10. Rantalainen, M. et al. Piecewise multivariate modelling of sequential metabolic profiling data. BMC bioinformatics 9 doi:10.1186/1471-2105-9-105 (2008).
11. Keun, H. C. et al. Geometric trajectory analysis of metabolic responses to toxicity can define treatment specific profiles. Chem. Res. Toxicol. 17, 579-587 (2004).
12. de Haan, J. R. et al. Interpretation of ANOVA models for microarray data using PCA. Bioinformatics 23, 184-190 (2007).
13. Sun, X. & Weckwerth, W. COVAIN: a toolbox for uni-and multivariate statistics, time-series and correlation network analysis and inverse estimation of the differential Jacobian from metabolomics covariance data. Metabolomics 8, 81-93 (2012).
14. Pan, L. et al. An optimized procedure for metabonomic analysis of rat liver tissue using gas chromatography/time-of-flight mass spectrometry. J. Pharm. Biomed. Anal. 52, 589-596 (2010).
15. Harshman, R. A. & Lundy, M. E. PARAFAC: Parallel factor analysis. Computational Statistics & Data Analysis 18, 39-72 (1994).
16. Rubingh, C. M. et al. Analyzing Longitudinal Microbial Metabolomics Data. J. Proteome Res. 8, 4319-4327 (2009).
17. Kiers, H. A. L. A three-step algorithm for CANDECOMP/PARAFAC analysis of large data sets with multicollinearity. J. Chemom. 12, 155-171 (1998).
18. Jiang, J. H., Wu, H. L., Li, Y. & Yu, R. Q. Three-way data resolution by alternating slice-wise diagonalization (ASD) method. J. Chemom. 14, 15-36 (2000).
19. Wang, K., Ng, S. K. & McLachlan, G. J. Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects. BMC bioinformatics 13 doi:10.1186/1471-2105-13-300 (2012).
20. Inoue, L. Y. T., Neira, M., Nelson, C., Gleave, M. & Etzioni, R. Cluster-based network model for time-course gene expression data. Biostatistics 8, 507-525 (2007).
21. Wang, J., Liu, P., She, M. F. H., Nahavandi, S. & Kouzani, A. Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model. Comput. Methods Programs Biomed. 111, 629-641 (2013).
22. Wu, F.-X., Zhang, W. J.&Kusalik, A. J. Dynamicmodel-based clustering for time-course gene expression data. J. Bioinform. Comput. Biol. 3, 821-836 (2005).
23. Son, Y. S. & Baek, J. A modified correlation coefficient based similarity measure for clustering time-course gene expression data. Pattern Recognit. Lett. 29, 232-242 (2008).
24. Ernst, J., Nau, G. J. & Bar-Joseph, Z. Clustering short time series gene expression data. Bioinformatics 21 Suppl 1, i159-168 (2005).
25. Montana, G., Berk, M.& Ebbels, T. Modelling Short Time Series in Metabolomics: A Functional Data Analysis Approach. Adv. Exp. Med. Biol 696, 307-315 (2011).
26. Peters, S., Janssen, H.-G. & Vivo-Truyols, G. Untargeted metabolite discovery in kinetic data from multi-dose intervention studies. J. Chromatogr. A 1218, 3337-3344 (2011).
27. Peters, S., Janssen, H. G. & Vivo-Truyols, G. Trend analysis of time-series data: A novel method for untargeted metabolite discovery. Anal. Chim. Acta. 663, 98-104 (2010).
28. Venook, A. P., Papandreou, C., Furuse, J. & de Guevara, L. L. The incidence and epidemiology of hepatocellular carcinoma: a global and regional perspective. Oncologist 15 Suppl 4, 5-13 (2010).
29. Farazi, P. A. & DePinho, R. A. Hepatocellular carcinoma pathogenesis: from genes to environment. Nat. Rev. Cancer 6, 674-687 (2006).
30. Altekruse, S. F., McGlynn, K. A. & Reichman, M. E. Hepatocellular carcinoma incidence, mortality, and survival trends in the United States from 1975 to 2005. J. Clin. Oncol. 27, 1485-1491 (2009).
31. Lencioni, R., Chen, X. P., Dagher, L. & Venook, A. P. Treatment of intermediate/advanced hepatocellular carcinoma in the clinic: how can outcomes be improved? Oncologist 15 Suppl 4, 42-52 (2010).
32. Qu, L. S. et al. Pre-S Deletion and Complex Mutations of Hepatitis B Virus Related to Young Age Hepatocellular Carcinoma in Qidong, China. Plos One 8, e59583 (2013).
33. Munoz, A. et al. Predictive power of hepatitis B 1762(T)/1764(A) mutations in plasma for hepatocellular carcinoma risk in Qidong, China. Carcinogenesis 32, 860-865 (2011).
34. Jackson, P. E. et al. Prospective detection of codon 249 mutations in plasma of hepatocellular carcinoma patients. Carcinogenesis 24, 1657-1663 (2003).
35. Tan, Y. et al. Metabolomics study of stepwise hepatocarcinogenesis from the model rats to patients: potential biomarkers effective for small hepatocellular carcinoma diagnosis. Mol. Cell. Proteomics. 11, M111. 010694 (2012).
36. Cortes, C. & Vapnik, V. Support vector machine. Machine learning 20, 273-297 (1995).
37. Hofmann, A. F. Bile acids, cholesterol, gallstone calcification, and the enterohepatic circulation of bilirubin. Gastroenterology 116, 1276-1277 (1999).
38. Russell, D. W. The enzymes, regulation, and genetics of bile acid synthesis. Annu. Rev. Biochem. 72, 137-174 (2003).
39. Hylemon, P. B. et al. Bile acids as regulatory molecules. J. Lipid Res. 50, 1509-1520 (2009).
40. Li, T. & Chiang, J. Y. Bile Acid signaling in liver metabolism and diseases. J. Lipids. 2012, 754067 (2012).
41. Amaral, J. D., Viana, R. J., Ramalho, R. M., Steer, C. J. & Rodrigues, C. M. Bile acids: regulation of apoptosis by ursodeoxycholic acid. J. Lipid Res. 50, 1721-1734 (2009).
42. Kim, I. et al. Differential regulation of bile acid homeostasis by the farnesoid X receptor in liver and intestine. J. Lipid Res. 48, 2664-2672 (2007).
43. Goodwin, B. et al. A regulatory cascade of the nuclear receptors FXR, SHP-1, and LRH-1 represses bile acid biosynthesis. Mol. cell 6, 517-526 (2000).
44. Zhou, L. N. et al. Serum metabolomics reveals the deregulation of fatty acids metabolism in hepatocellular carcinoma and chronic liver diseases. Anal. Bioanal. Chem. 403, 203-213 (2012).
45. Chen, T. L. et al. Serum and Urine Metabolite Profiling Reveals Potential Biomarkers of Human Hepatocellular Carcinoma. Mol. Cell. Proteomics 10, 1280-1289 (2011).
46. Yin, P. Y. et al. 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, 868-876 (2009).
47. Ponz De Leon, M., Murphy, G. M. & Dowling, R. H. Physiological factors influencing serum bile acid levels. Gut 19, 32-39 (1978).
48. Angelin, B., Bjokhem, I. & Einarsson, K. Individual serum bile acid concentrations in normo- and hyperlipoproteinemia as determined by mass fragmentography: relation to bile acid pool size. J. Lipid Res. 19, 527-537 (1978).
49. Kitazawa, S. et al. Enhanced Preneoplastic Liver Lesion Development under Selection Pressure Conditions after Administration of Deoxycholic or Lithocholic Acid in the Initiation Phase in Rats. Carcinogenesis 11, 1323-1328 (1990).
50. Tsuda, H. et al. Positive Influence of Dietary Deoxycholic-Acid on Development of Pre-Neoplastic Lesions Initiated by N-Methyl-N-Nitrosourea in Rat-Liver. Carcinogenesis 9, 1103-1105 (1988).
51. Anakk, S. et al. Bile Acids Activate YAP to Promote Liver Carcinogenesis. Cell Rep. 5, 1060-1069 (2013).
52. Tanaka, N., Matsubara, T., Krausz, K. W., Patterson, A. D. & Gonzalez, F. J. Disruption of phospholipid and bile acid homeostasis in mice with nonalcoholic steatohepatitis. Hepatology 56, 118-129 (2012).
53. Takano, S. et al. A Multicenter Randomized Controlled Dose Study of Ursodeoxycholic Acid for Chronic Hepatitis-C. Hepatology 20, 558-564 (1994).
54. Oyama, K., Shiota, G., Ito, H., Murawaki, Y. & Kawasaki, H. Reduction of hepatocarcinogenesis by ursodeoxycholic acid in rats. Carcinogenesis 23, 885-892 (2002).
55. Huang, L. R., Coughtrie, M. W. H. & Hsu, H. C. Down-regulation of dehydroepiandrosterone sulfotransferase gene in human hepatocellular carcinoma. Mol. Cell Endocrinol. 231, 87-94 (2005).
56. Alnouti, Y. Bile Acid Sulfation: A Pathway of Bile Acid Elimination and Detoxification. Toxicol. Sci. 108, 225-246 (2009).
57. Yalcin, E. B. et al. Downregulation of Sulfotransferase Expression and Activity in Diseased Human Livers. Drug Metab. Dispos. 41, 1642-1650 (2013).
58. Ming, L. et al. Dominant role of hepatitis B virus and cofactor role of aflatoxin in hepatocarcinogenesis in Qidong, China. Hepatology 36, 1214-1220 (2002).
59. Tusher, V. G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. U S A. 98, 5116-5121 (2001).
60. Smilde, A. K., van der Werf, M. J., Bijlsma, S., van der Werff-van der Vat, B. J. & Jellema, R. H. Fusion of mass spectrometry-based metabolomics data. Anal. chem. 77, 6729-6736 (2005).
61. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289-300 (1995).