Volume 12 Supplement 7

UT-ORNL-KBRIN Bioinformatics Summit 2011

Open Access

Identifying the key genes and pathways in the progression of hepatitis C virus induced hepatocellular carcinoma using a systems biology approach

BMC Bioinformatics201112(Suppl 7):A4

DOI: 10.1186/1471-2105-12-S7-A4

Published: 5 August 2011

Background

Incidence of hepatitis C virus (HCV) induced hepatocellular carcinoma (HCC) has been increasing in many developed countries including the United States and Europe during the recent years. Although many efforts have been made to understand the pathogenesis, the picture of its progression still remains elusive.

Materials and methods

We developed a systematic approach to identify deregulated biological networks in HCC by integrating gene expression profiles [1] with high-throughput protein-protein interaction data [2]. Samples were grouped into five disease stages including normal, cirrhotic, dysplastic, early and advanced HCC. For each pair of consecutive stages, we compared gene expressions and then mapped these measures to the protein interaction network. Responsive subnetworks were then identified from these node weighted networks. The searching algorithm is adapted from a previous study [3], which expands the seed graphs under constrains of several parameters.

Results

Four networks were identified including precancerous networks (normal-cirrhosis and cirrhosis-dysplasia) and cancerous networks (dysplasia-early HCC, early-advanced HCC). A summary of these networks is shown in Table 1. An independent dataset was used for network validation. Statistical significance of these networks was assessed within three hypotheses. Little overlap was observed between precancerous and cancerous networks, in contrast to a substantial overlap within precancerous or cancerous networks. Network functions were annotated with Gene Ontology biological process using hypergeometric distribution based enrichment analysis. Significant functions were then assembled into a module map in temporal order. The apoptosis gene ZBTB16 was highlighted by examining the module map, which shows a negative expression pattern with c-myc. Network analysis led to the identifications of key genes and pathways by developmental stage, such as LCK signaling pathways in cirrhosis, MMP genes and TIMP genes in dysplastic liver, and CDC2-mediated cell cycle signaling in early and advanced HCC. CDC2, a cell cycle regulatory gene, is particularly interesting because it is a hub protein of the module that shows correlative pattern with cancer progression.
Table 1

Overview of the responsive networks.

Network

#Genes

#Interactions

#DEGs*

#Hub interactions

Normal- cirrhosis

55

67

53 (96.3%)

42 (62.7%)

Cirrhosis- dysplasia

38

50

37 (97.4%)

35 (70.0%)

Dysplasia -early HCC

60

65

53 (88.3%)

37 (56.9%)

Early- advanced HCC

68

98

59 (86.8%)

79 (80.6%)

*Differentially expressed genes (DEGs) were identified as genes with up or down regulation fold change ≥ 2 and student t test P value ≤ 0.01.

Hub interaction number refers to the total number of interactions involving hub genes.

Hub genes were defined to have at least 5 interactions in each network.

Conclusions

Our study uncovers a temporal spectrum of functional deregulation and prioritizes key genes and pathways in the progression of HCV induced HCC. Despite the confirmation of much knowledge in the pathogenesis of this disease, these findings also provide additional insights for further investigations.

Declarations

Acknowledgements

We thank Drs. Scott Hiebert, William Tansey, Jingchun Sun and Peilin Jia and Mr. Jeffery Ewers for helpful discussions.

Authors’ Affiliations

(1)
Department of Biomedical Informatics, Vanderbilt University Medical Center
(2)
Department of Cancer Biology, Vanderbilt University Medical Center

References

  1. Wurmbach E, Chen YB, Khitrov G, Zhang W, Roayaie S, Schwartz M, Fiel I, Thung S, Mazzaferro V, Bruix J, et al.: Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma. Hepatology (Baltimore, Md) 2007, 45(4):938–947.View ArticleGoogle Scholar
  2. Wu J, Vallenius T, Ovaska K, Westermarck J, Makela TP, Hautaniemi S: Integrated network analysis platform for protein-protein interactions. Nature methods 2009, 6(1):75–77. 10.1038/nmeth.1282View ArticlePubMedGoogle Scholar
  3. Chuang HY, Lee E, Liu YT, Lee D, Ideker T: Network-based classification of breast cancer metastasis. Molecular systems biology 2007, 3: 140. 10.1038/msb4100180PubMed CentralView ArticlePubMedGoogle Scholar

Copyright

© Zheng and Zhao; licensee BioMed Central Ltd. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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