Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0023890 (cirrhosis)
 42,195 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Hepatocellular carcinoma (HCC) is a leading cause of global cancer mortality. However, little is known about the precise molecular mechanisms involved in tumor formation and pathogenesis. The primary goal of this study was to elucidate genome-wide molecular networks involved in development of HCC with multiple etiologies by exploring high quality microarray data. We undertook a comparative network analysis across 264 human microarray profiles monitoring transcript changes in healthy liver, liver cirrhosis, and HCC with viral and alcoholic etiologies. Gene co-expression profiling was used to derive a consensus gene relevance network of HCC progression that consisted of 798 genes and 2,012 links. The HCC interactome was further confirmed to be phenotype-specific and non-random. Additionally, we confirmed that co-expressed genes are more likely to share biological function, but not sub-cellular localization. Analysis of individual HCC genes revealed that they are topologically central in a human protein-protein interaction network. We used quantitative RT-PCR in a cohort of normal liver tissue (n = 8), hepatitis C virus (HCV)-induced chronic liver disease (n = 9), and HCC (n = 7) to validate co-expressions of several well-connected genes, namely ASPM, CDKN3, NEK2, RACGAP1, and TOP2A. We show that HCC is a heterogeneous disorder, underpinned by complex cross talk between immune response, cell cycle, and mRNA translation pathways. Our work provides a systems-wide resource for deeper understanding of molecular mechanisms in HCC progression and may be used further to define novel targets for efficient treatment or diagnosis of this disease.
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PMID:Functional and topological properties in hepatocellular carcinoma transcriptome. 2253 75

Hepatitis C virus (HCV) cirrhosis is at a high risk of hepatocellular carcinoma (HCC), and its progression is influenced by a complex network of gene interactions. A weighted gene co-expression network was constructed to identify gene modules associated with the seven-stage disease progression from HCV cirrhosis to HCV-related HCC (n=65). In the significant module (R2=0.86), a total of 25 network hub genes were identified, half of which were also hub nodes in the protein-protein interaction network of the module genes. In validation, most hub genes showed a moderate correlation with the disease progression, and only ASPM was highly correlated (R2=0.801). In the test set (n=63), ASPM was also more highly expressed in HCV cirrhosis with concomitant HCC than in those without HCC (P=0.0054). Gene set enrichment analysis (GSEA) demonstrated that the gene set of "regulation of protein amino acid phosphorylation" (n=20) was enriched in HCV cirrhosis samples with ASPM highly expressed (false discovery rate (FDR)=0.049). In gene ontology (GO) analysis, genes in the enriched set were associated with liver neoplasms and other neoplastic diseases. In conclusion, through co-expression analysis, ASPM was identified and validated in association with the progression of HCV cirrhosis probably by regulating tumor-related phosphorylation.
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PMID:Strong correlation between ASPM gene expression and HCV cirrhosis progression identified by co-expression analysis. 2787