Integrative network analysis identifies key genes and pathways in the progression of hepatitis C virus induced hepatocellular carcinoma

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
BMC Medical Genomics (Impact Factor: 2.87). 08/2011; 4(1):62. DOI: 10.1186/1755-8794-4-62
Source: PubMed


Incidence of hepatitis C virus (HCV) induced hepatocellular carcinoma (HCC) has been increasing in the United States and Europe during recent years. Although HCV-associated HCC shares many pathological characteristics with other types of HCC, its molecular mechanisms of progression remain elusive.
To investigate the underlying pathology, we developed a systematic approach to identify deregulated biological networks in HCC by integrating gene expression profiles with high-throughput protein-protein interaction data. We examined five stages including normal (control) liver, cirrhotic liver, dysplasia, early HCC and advanced HCC.
Among the five consecutive pathological stages, we identified four networks including precancerous networks (Normal-Cirrhosis and Cirrhosis-Dysplasia) and cancerous networks (Dysplasia-Early HCC, Early-Advanced HCC). We found little overlap between precancerous and cancerous networks, opposite to a substantial overlap within precancerous or cancerous networks. We further found that the hub proteins interacted with HCV proteins, suggesting direct interventions of these networks by the virus. The functional annotation of each network demonstrates a high degree of consistency with current knowledge in HCC. By assembling these functions into a module map, we could depict the stepwise biological functions that are deregulated in HCV-induced hepatocarcinogenesis. Additionally, these networks enable us to identify important genes and pathways by developmental stage, such as LCK signalling pathways in cirrhosis, MMP genes and TIMP genes in dysplastic liver, and CDC2-mediated cell cycle signalling in early and advanced HCC. CDC2 (alternative symbol CDK1), a cell cycle regulatory gene, is particularly interesting due to its topological position in temporally deregulated networks.
Our study uncovers a temporal spectrum of functional deregulation and prioritizes key genes and pathways in the progression of HCV induced HCC. These findings present a wealth of information for further investigation.

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Available from: Scott W Hiebert, Jun 15, 2015
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    • "Most approaches to extracting protein interaction subnetworks, including our previous one [24], were seeded from a set of DEGs. In this work, we intended to use DCPs as alternative seeds; therefore, we initially set out to investigate if DCPs were a better option for seeds. "
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    • "It implies that the hub nodes (miRNAs or target genes) are much stronger determinants of the realized gene expression profiles, whereas the periphery nodes that should be regulated are not regulating. Furthermore , it also implicates the potential modules are subsistent in the networks, which in biological networks often represent molecular complexes and pathways [22]. "
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    • "In the process of network building, miRNA nodes were weighted by their expression fold changes (absolute value of log2), while target genes were weighted based on degree distributions between consecutive groups and thus obtained a node-weighted miRNA-target interaction network for each stage. In order to validate the veracity of above network, rank all nodes (including miRNAs and target genes) of network according to their weights and tested the similarity of them [23], thereafter, obtained deregulated nodes for mapping the network of consecutive TCM syndromes progression. In the weighted miRNA-target network, the nodes represent miR- NAs or genes, the edges represent the connection strength (adjacency), and the neighbors of each node >2 were selected in the network. "
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