Skip to main content

Gene Systems Network Inferred from Expression Profiles in Hepatocellular Carcinogenesis by Graphical Gaussian Model

Abstract

Hepatocellular carcinoma (HCC) in a liver with advanced-stage chronic hepatitis C (CHC) is induced by hepatitis C virus, which chronically infects about 170 million people worldwide. To elucidate the associations between gene groups in hepatocellular carcinogenesis, we analyzed the profiles of the genes characteristically expressed in the CHC and HCC cell stages by a statistical method for inferring the network between gene systems based on the graphical Gaussian model. A systematic evaluation of the inferred network in terms of the biological knowledge revealed that the inferred network was strongly involved in the known gene-gene interactions with high significance , and that the clusters characterized by different cancer-related responses were associated with those of the gene groups related to metabolic pathways and morphological events. Although some relationships in the network remain to be interpreted, the analyses revealed a snapshot of the orchestrated expression of cancer-related groups and some pathways related with metabolisms and morphological events in hepatocellular carcinogenesis, and thus provide possible clues on the disease mechanism and insights that address the gap between molecular and clinical assessments.

[1234567891011121314151617181920212223242526272829303132333435363738]

References

  1. 1.

    Alter MJ, Margolis HS, Krawczynski K, et al.: The natural history of community-acquired hepatitis C in the United States. The sentinel counties chronic non-A, non-B hepatitis study team. The New England Journal of Medicine 1992, 327(27):1899-1905. 10.1056/NEJM199212313272702

    Article  Google Scholar 

  2. 2.

    Di Bisceglie AM: Hepatitis C. The Lancet 1998, 351(9099):351-355. 10.1016/S0140-6736(97)07361-3

    Article  Google Scholar 

  3. 3.

    Zeuzem S, Feinman SV, Rasenack J, et al.: Peginterferon alfa-2a in patients with chronic hepatitis C. The New England Journal of Medicine 2000, 343(23):1666-1672. 10.1056/NEJM200012073432301

    Article  Google Scholar 

  4. 4.

    Thorgeirsson SS, Lee J-S, Grisham JW: Molecular prognostication of liver cancer: end of the beginning. Journal of Hepatology 2006, 44(4):798-805. 10.1016/j.jhep.2006.01.008

    Article  Google Scholar 

  5. 5.

    Iizuka N, Oka M, Yamada-Okabe H, et al.: Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection. The Lancet 2003, 361(9361):923-929. 10.1016/S0140-6736(03)12775-4

    Article  Google Scholar 

  6. 6.

    Okabe H, Satoh S, Kato T, et al.: Genome-wide analysis of gene expression in human hepatocellular carcinomas using cDNA microarray: identification of genes involved in viral carcinogenesis and tumor progression. Cancer Research 2001, 61(5):2129-2137.

    Google Scholar 

  7. 7.

    Zhang L-H, Ji J-F: Molecular profiling of hepatocellular carcinomas by cDNA microarray. World Journal of Gastroenterology 2005, 11(4):463-468.

    Article  MathSciNet  Google Scholar 

  8. 8.

    Jiang J, Nilsson-Ehle P, Xu N: Influence of liver cancer on lipid and lipoprotein metabolism. Lipids in Health and Disease 2006, 5: 4. 10.1186/1476-511X-5-4

    Article  Google Scholar 

  9. 9.

    Zerbini A, Pilli M, Ferrari C, Missale G: Is there a role for immunotherapy in hepatocellular carcinoma? Digestive and Liver Disease 2006, 38(4):221-225. 10.1016/j.dld.2005.12.004

    Article  Google Scholar 

  10. 10.

    Horimoto K, Toh H: Statistical estimation of cluster boundaries in gene expression profile data. Bioinformatics 2001, 17(12):1143-1151. 10.1093/bioinformatics/17.12.1143

    Article  Google Scholar 

  11. 11.

    Toh H, Horimoto K: Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling. Bioinformatics 2002, 18(2):287-297. 10.1093/bioinformatics/18.2.287

    Article  Google Scholar 

  12. 12.

    Lauritzen S: Graphical Models. Oxford University Press, Oxford, UK; 1996.

    Google Scholar 

  13. 13.

    Whittaker J: Graphical Models in Applied Multivariate Statistics. John Wiley & Sons, New York, NY, USA; 1990.

    Google Scholar 

  14. 14.

    Toh H, Horimoto K: System for automatically inferring a genetic network from expression profiles. Journal of Biological Physics 2002, 28(3):449-464. 10.1023/A:1020337311471

    Article  Google Scholar 

  15. 15.

    Slonim DK: From patterns to pathways: gene expression data analysis comes of age. Nature Genetics 2002, 32(5):502-508.

    Article  Google Scholar 

  16. 16.

    Aburatani S, Kuhara S, Toh H, Horimoto K: Deduction of a gene regulatory relationship framework from gene expression data by the application of graphical Gaussian modeling. Signal Processing 2003, 83(4):777-788. 10.1016/S0165-1684(02)00476-0

    Article  MATH  Google Scholar 

  17. 17.

    Ashburner M, Ball CA, Blake JA, et al.: Gene ontology: tool for the unification of biology. Nature Genetics 2000, 25(1):25-29. 10.1038/75556

    Article  Google Scholar 

  18. 18.

    Boyle EI, Weng S, Gollub J, et al.: GO::TermFinder—open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes. Bioinformatics 2004, 20(18):3710-3715. 10.1093/bioinformatics/bth456

    Article  Google Scholar 

  19. 19.

    Nikitin A, Egorov S, Daraselia N, Mazo I: Pathway studio—the analysis and navigation of molecular networks. Bioinformatics 2003, 19(16):2155-2157. 10.1093/bioinformatics/btg290

    Article  Google Scholar 

  20. 20.

    Kaufman L, Rousseeuw PJ: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, New York, NY, USA; 1990.

    Google Scholar 

  21. 21.

    Freund RJ, Wilson WJ: Regression Analysis: Statistical Modeling of a Response Variable. Academic Press, San Diego, Calif, USA; 1998.

    Google Scholar 

  22. 22.

    Dempster AP: Covariance selection. Biometrics 1972, 28(1):157-175. 10.2307/2528966

    Article  Google Scholar 

  23. 23.

    Wermuth N, Scheidt E: Algorithm AS 105: fitting a covariance selection model to a matrix. Applied Statistics 1977, 26(1):88-92. 10.2307/2346883

    Article  Google Scholar 

  24. 24.

    Wu LF, Hughes TR, Davierwala AP, Robinson MD, Stoughton R, Altschuler SJ: Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters. Nature Genetics 2002, 31(3):255-265. 10.1038/ng906

    Article  Google Scholar 

  25. 25.

    Anderson TW: An Introduction to Multivariate Statistical Analysis. 2nd edition. John Wiley & Sons, New York, NY, USA; 1984.

    Google Scholar 

  26. 26.

    Aburatani S, Goto K, Saito S, et al.: ASIAN: a website for network inference. Bioinformatics 2004, 20(16):2853-2856. 10.1093/bioinformatics/bth296

    Article  Google Scholar 

  27. 27.

    Aburatani S, Goto K, Saito S, Toh H, Horimoto K: ASIAN: a web server for inferring a regulatory network framework from gene expression profiles. Nucleic Acids Research 2005, 33: W659-W664. 10.1093/nar/gki446

    Article  Google Scholar 

  28. 28.

    Honda M, Kaneko S, Kawai H, Shirota Y, Kobayashi K: Differential gene expression between chronic hepatitis B and C hepatic lesion. Gastroenterology 2001, 120(4):955-966. 10.1053/gast.2001.22468

    Article  Google Scholar 

  29. 29.

    Wu T: Cyclooxygenase-2 in hepatocellular carcinoma. Cancer Treatment Reviews 2006, 32(1):28-44. 10.1016/j.ctrv.2005.10.004

    Article  Google Scholar 

  30. 30.

    Xiao H, Palhan V, Yang Y, Roeder RG: TIP30 has an intrinsic kinase activity required for up-regulation of a subset of apoptotic genes. The EMBO Journal 2000, 19(5):956-963. 10.1093/emboj/19.5.956

    Article  Google Scholar 

  31. 31.

    Coleman WB: Mechanisms of human hepatocarcinogenesis. Current Molecular Medicine 2003, 3(6):573-588. 10.2174/1566524033479546

    Article  Google Scholar 

  32. 32.

    Xu Y, Sengupta PK, Seto E, Smith BD: Regulatory factor for X-box family proteins differentially interact with histone deacetylases to repress collagen 2(I) gene (COL1A2) expression. Journal of Biological Chemistry 2006, 281(14):9260-9270.

    Article  Google Scholar 

  33. 33.

    Barker PA, Salehi A: The MAGE proteins: emerging roles in cell cycle progression, apoptosis, and neurogenetic disease. Journal of Neuroscience Research 2002, 67(6):705-712. 10.1002/jnr.10160

    Article  Google Scholar 

  34. 34.

    Xu Y, Wang L, Buttice G, Sengupta PK, Smith BD: Interferon repression of collagen (COL1A2) transcription is mediated by the RFX5 complex. The Journal of Biological Chemistry 2003, 278(49):49134-49144. 10.1074/jbc.M309003200

    Article  Google Scholar 

  35. 35.

    Macian F, Garcia-Rodriguez C, Rao A: Gene expression elicited by NFAT in the presence or absence of cooperative recruitment of Fos and Jun. The EMBO Journal 2000, 19(17):4783-4795. 10.1093/emboj/19.17.4783

    Article  Google Scholar 

  36. 36.

    Fu J, Tay SSW, Ling EA, Dheen ST: High glucose alters the expression of genes involved in proliferation and cell-fate specification of embryonic neural stem cells. Diabetologia 2006, 49(5):1027-1038. 10.1007/s00125-006-0153-3

    Article  Google Scholar 

  37. 37.

    Schäfer J, Strimmer K: An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 2005, 21(6):754-764. 10.1093/bioinformatics/bti062

    Article  Google Scholar 

  38. 38.

    de la Fuente A, Bing N, Hoeschele I, Mendes P: Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 2004, 20(18):3565-3574. 10.1093/bioinformatics/bth445

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Sachiyo Aburatani.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Aburatani, S., Sun, F., Saito, S. et al. Gene Systems Network Inferred from Expression Profiles in Hepatocellular Carcinogenesis by Graphical Gaussian Model. J Bioinform Sys Biology 2007, 47214 (2007). https://doi.org/10.1155/2007/47214

Download citation

Keywords

  • Gene System
  • System Biology
  • System Network
  • Gaussian Model
  • Graphical Gaussian Model