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Mol Med Rep ; 15(1): 380-386, 2017 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-27959423

RESUMO

Gene expression data of hepatocellular carcinoma (HCC) was compared with that of cirrhosis (C) to identify critical genes in HCC. A total of five gene expression data sets were downloaded from Gene Expression Omnibus. HCC and healthy samples were combined as dataset HCC, whereas cirrhosis samples were included in dataset C. A network was constructed for dataset HCC with the package R for performing Weighted Gene Co­expression Network Analysis. Modules were identified by cluster analysis with the packages flashClust and dynamicTreeCut. Hub genes were screened out by calculating connectivity. Functional annotations were assigned to the hub genes using the Database for Annotation, Visualization and Integration Discovery, and functional annotation networks were visualized with Cytoscape. Following the exclusion of outlier samples, 394 HCC samples and 47 healthy samples were included in dataset HCC and 233 cirrhosis samples were included in dataset C. A total of 6 modules were identified in the weighted gene co­expression network of dataset HCC (blue, brown, turquoise, green, red and yellow). Modules blue, brown and turquoise had high preservation whereas module yellow exhibited the lowest preservation. These modules were associated with transcription, mitosis, cation transportation, cation homeostasis, secretion and regulation of cyclase activity. Various hub genes of module yellow were cytokines, including chemokine (C­C motif) ligand 22 and interleukin­19, which may be important in the development of HCC. Gene expression profiles of HCC were compared with those of cirrhosis and numerous critical genes were identified, which may contribute to the progression of HCC. Further studies on these genes may improve the understanding of HCC pathogenesis.


Assuntos
Carcinoma Hepatocelular/genética , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica , Neoplasias Hepáticas/genética , Carcinoma Hepatocelular/patologia , Análise por Conglomerados , Redes Reguladoras de Genes , Humanos , Fígado/patologia , Neoplasias Hepáticas/patologia , Transcriptoma
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