Article

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: 3.91). 08/2011; 4:62. DOI: 10.1186/1755-8794-4-62
Source: PubMed

ABSTRACT 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.

2 Followers
 · 
103 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: The transforming growth factor (TGF)-β signalling pathway plays a dual role in hepatocarcinogenesis. It has been recognised for its role as a tumour suppressor as well as a tumour promoter depending on the cellular context. The aim of this study was to investigate the clinical significance of serum TGF-β1 level and TGF-β1 messenger RNA (mRNA) in the peripheral blood of liver cirrhosis and hepatocellular carcinoma (HCC) patients as noninvasive biomarkers in diagnosing HCC. Twenty patients were allocated to each of the liver cirrhosis and HCC groups, in addition to 20 healthy volunteers. TGF-β1 gene expression in peripheral blood was quantitated using real-time polymerase chain reaction (PCR), while serum TGF-β1 was analysed using enzyme-linked immunosorbent assay (ELISA). TGF-β1 gene expression was significantly lower in HCC patients (median 0.401 (0.241-0.699) fold change) than in liver cirrhosis patients (median 0.595 (0.464-0.816)) (p=0.042) and normal controls (median 1.00 (0.706-1.426) fold change) (p=0.001). TGF-β1 gene expression showed significant positive correlation with serum TGF-β1 (r=0.272, p=0.036) and significant negative correlation with alpha-fetoprotein (AFP) (r=-0.528, p=0.001). Receiver operating characteristic (ROC) analysis was conducted for TGF-β1 gene expression in comparison with AFP. The area under the curve for TGF-β1 gene expression was 0.688 (95% CI=0.517-0.858) (p=0.042) and AFP was 0.869 (95% CI=0.761-0.976) (p=0.001). The sensitivity and specificity of TGF-β1 gene expression were 65% and 75%, respectively, at a cutoff value of 0.462 fold change. TGF-β1 gene expression in the peripheral blood may be used as a molecular marker for the diagnosis of HCC. Additional studies on a large-scale population are necessary to gain greater insight into the impact of TGF-β1 gene expression in the pathogenesis of HCC. Copyright © 2014. Published by Elsevier Ltd.
    Arab Journal of Gastroenterology 12/2014; 15(3-4). DOI:10.1016/j.ajg.2014.10.007
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Hepatitis C virus infection is one of the most common and chronic in the world, and hepatitis associated with HCV infection is a major risk factor for the development of cirrhosis and hepatocellular carcinoma (HCC). The rapidly growing number of viral-host and host protein-protein interactions is enabling more and more reliable network-based analyses of viral infection supported by omics data. The study of molecular interaction networks helps to elucidate the mechanistic pathways linking HCV molecular activities and the host response that modulates the stepwise hepatocarcinogenic process from preneoplastic lesions (cirrhosis and dysplasia) to HCC. Simulating the impact of HCV-host molecular interactions throughout the host protein-protein interaction (PPI) network, we ranked the host proteins in relation to their network proximity to viral targets. We observed that the set of proteins in the neighborhood of HCV targets in the host interactome is enriched in key players of the host response to HCV infection. In opposition to HCV targets, subnetworks of proteins in network proximity to HCV targets are significantly enriched in proteins reported as differentially expressed in preneoplastic and neoplastic liver samples by two independent studies. Using multi-objective optimization, we extracted subnetworks that are simultaneously "guilt-by-association" with HCV proteins and enriched in proteins differentially expressed. These subnetworks contain established, recently proposed and novel candidate proteins for the regulation of the mechanisms of liver cells response to chronic HCV infection.
    PLoS ONE 12/2014; 9(12):e113660. DOI:10.1371/journal.pone.0113660 · 3.53 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Rapid development of genome-wide profiling technologies has made it possible to conduct integrative analysis on genomic data from multiple platforms. In this study, we develop a novel integrative Bayesian network approach to investigate the relationships between genetic and epigenetic alterations as well as how these mutations affect a patient’s clinical outcome. We take a Bayesian network approach that admits a convenient decomposition of the joint distribution into local distributions. Exploiting the prior biological knowledge about regulatory mechanisms, we model each local distribution as linear regressions. This allows us to analyze multi-platform genome-wide data in a computationally efficient manner. We illustrate the performance of our approach through simulation studies. Our methods are motivated by and applied to a multi-platform glioblastoma dataset, from which we reveal several biologically relevant relationships that have been validated in the literature as well as new genes that could potentially be novel biomarkers for cancer progression.
    Cancer informatics 09/2014; 13(S2):39-48. DOI:10.4137/CIn.s13786

Preview (4 Sources)

Download
0 Downloads
Available from