ArticlePloS one2011
A comprehensive analysis of the dynamic biological networks in HCV induced hepatocarcinogenesis.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 27 citations in OpenAlex.
- PathExt: a general framework for path-based mining of omics-integrated biological networks.Bioinformatics (Oxford, England) · 2021Article
- Association of HLA-DQA1 gene polymorphisms with the risk of children primary nephrotic syndrome in Chinese population.Journal of clinical laboratory analysis · 2019Article
- Extracting proteins involved in disease progression using temporally connected networks.BMC systems biology · 2018Article
- The CDKN2A polymorphisms and the susceptibility of HBV-related gestational diabetes mellitus.Journal of clinical laboratory analysis · 2018Article
- Identification and functional analysis of a core gene module associated with hepatitis C virus-induced human hepatocellular carcinoma progression.Oncology letters · 2018Article
- Prognostic value ofCancer management and research · 2018Article
- Low expression of VSIG4 is associated with poor prognosis in hepatocellular carcinoma patients with hepatitis B infection.Cancer management and research · 2018Article
- Identifying key regulating miRNAs in hepatocellular carcinomas by an omics' method.Oncotarget · 2017Article
- Driver genes in non-small cell lung cancer: Characteristics, detection methods, and targeted therapies.Oncotarget · 2017Review
- Precision medicine for hepatocellular carcinoma: driver mutations and targeted therapy.Oncotarget · 2017Review
- Transcriptome profiling identifies a recurrent CRYL1-IFT88 chimeric transcript in hepatocellular carcinoma.Oncotarget · 2017Article
- Tracking disease progression by searching paths in a temporal network of biological processes.PloS one · 2017Article
- Screening key genes associated with congenital heart defects in Down syndrome based on differential expression network.International journal of clinical and experimental pathology · 2015Article
- Identification of the key genes connected with plasma cells of multiple myeloma using expression profiles.OncoTargets and therapy · 2015Article
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- Systems biology analysis of the endocannabinoid system reveals a scale-free network with distinct roles for anandamide and 2-arachidonoylglycerol.Omics : a journal of integrative biology · 2013Article
- Spatio-temporal analysis of type 2 diabetes mellitus based on differential expression networks.Scientific reports · 2013Article
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Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) is a primary malignancy of the liver, which is closely related to hepatitis C and cirrhosis. The molecular mechanisms underlying the hepatocarcinogenesis induced by HCV infection remain clarified from a standpoint of systems biology. By integrating data from protein-protein interactions, transcriptional regulation, and disease related microarray analysis, we carried out a dynamic biological network analysis on the progression of HCV induced hepatocarcinogenesis, and systematically explored the potentially disease-related mechanisms through a network view. The dysfunctional interactions among proteins and deregulatory relationships between transcription factors and their target genes could be causes for the occurrence and progression of this disease. The six pathologically defined disease stages in the development and progression of HCC after HCV infection were included in this study. We constructed disease-related biological networks for each disease stage, and identified progression-related sub-networks that potentially play roles in the developmental stage of the corresponding disease and participate in the later stage of cancer progression. In addition, we identified novel risk factors related to HCC based on the analysis of the progression-related sub-networks. The dynamic characteristics of the network reflect important features of the disease development and progression, which provide important information for us to further explore underlying mechanisms of the disease.
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