ArticleOncology letters2018
Identification and functional analysis of a core gene module associated with hepatitis C virus-induced human hepatocellular carcinoma progression.
Article in Oncology letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- HCV-related hepatocellular carcinoma: gene signatures associated with TERT promoter mutations and sex.Journal of translational medicine · 2025Article
- A Mixture Method for Robust Detection HCV Early Diagnosis Biomarker with ML Approach and Molecular Docking.International journal of molecular sciences · 2023Article
- Identification of Gene Signature as Diagnostic and Prognostic Blood Biomarker for Early Hepatocellular Carcinoma Using Integrated Cross-Species Transcriptomic and Network Analyses.Frontiers in genetics · 2021Article
- Application of next-generation sequencing technology to precision medicine in cancer: joint consensus of the Tumor Biomarker Committee of the Chinese Society of Clinical Oncology.Cancer biology & medicine · 2019Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatitis C virus (HCV)-induced human hepatocellular carcinoma (HCC) progression may be due to a complex multi-step processes. The developmental mechanism of these processes is worth investigating for the prevention, diagnosis and therapy of HCC. The aim of the present study was to investigate the molecular mechanism underlying the progression of HCV-induced hepatocarcinogenesis. First, the dynamic gene module, consisting of key genes associated with progression between the normal stage and HCC, was identified using the Weighted Gene Co-expression Network Analysis tool from R language. By defining those genes in the module as seeds, the change of co-expression in differentially expressed gene sets in two consecutive stages of pathological progression was examined. Finally, interaction pairs of HCV viral proteins and their directly targeted proteins in the identified module were extracted from the literature and a comprehensive interaction dataset from yeast two-hybrid experiments. By combining the interactions between HCV and their targets, and protein-protein interactions in the Search Tool for the Retrieval of Interacting Genes database (STRING), the HCV-key genes interaction network was constructed and visualized using Cytoscape software 3.2. As a result, a module containing 44 key genes was identified to be associated with HCC progression, due to the dynamic features and functions of those genes in the module. Several important differentially co-expressed gene pairs were identified between non-HCC and HCC stages. In the key genes, cyclin dependent kinase 1 (CDK1), NDC80, cyclin A2 (CCNA2) and rac GTPase activating protein 1 (RACGAP1) were shown to be targeted by the HCV nonstructural proteins NS5A, NS3 and NS5B, respectively. The four genes perform an intermediary role between the HCV viral proteins and the dysfunctional module in the HCV key genes interaction network. These findings provided valuable information for understanding the mechanism of HCV-induced HCC progression and for seeking drug targets for the therapy and prevention of HCC.
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