Dataset Information

Bioinformatics analysis of the gene expression profile of hepatocellular carcinoma: preliminary results.

ABSTRACT: AIM OF THE STUDY:To analyse the expression profile of hepatocellular carcinoma compared with normal liver by using bioinformatics methods. MATERIAL AND METHODS:In this study, we analysed the microarray expression data of HCC and adjacent normal liver samples from the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes. Then, functional analyses were performed using GenCLiP analysis, Gene Ontology categories, and aberrant pathway identification. In addition, we used the CMap database to identify small molecules that can induce HCC. RESULTS:Overall, 2721 differentially expressed genes (DEGs) were identified. We found 180 metastasis-related genes and constructed co-occurrence networks. Several significant pathways, including the transforming growth factor ? (TGF-?) signalling pathway, were identified as closely related to these DEGs. Some candidate small molecules (such as betahistine) were identified that might provide a basis for developing HCC treatments in the future. CONCLUSIONS:Although we functionally analysed the differences in the gene expression profiles of HCC and normal liver tissues, our study is essentially preliminary, and it may be premature to apply our results to clinical trials. Further research and experimental testing are required in future studies.

SUBMITTER: Li J

PROVIDER: S-EPMC4829745 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Bioinformatics analysis of the gene expression profile of hepatocellular carcinoma: preliminary results.

Li Jia J Huang Zhongxi Z Wei Lixin L

Contemporary oncology (Poznan, Poland) 20160316 1

<h4>Aim of the study</h4>To analyse the expression profile of hepatocellular carcinoma compared with normal liver by using bioinformatics methods.<h4>Material and methods</h4>In this study, we analysed the microarray expression data of HCC and adjacent normal liver samples from the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes. Then, functional analyses were performed using GenCLiP analysis, Gene Ontology categories, and aberrant pathway identification. In a ...[more]

PMID: 27095935

Similar Datasets

Project description:Several studies have demonstrated that chronic hepatitis delta virus (HDV) infection is associated with a worsening of hepatitis B virus (HBV) infection and increased risk of hepatocellular carcinoma (HCC). However, there is limited data on the role of HDV in the oncogenesis of HCC. This study is aimed at assessing the potential mechanisms of HDV-associated hepatocarcinogenesis, especially to screen and identify key genes and pathways possibly involved in the pathogenesis of HCC. We selected three microarray datasets: GSE55092 contains 39 cancer specimens and 81 paracancer specimens from 11 HBV-associated HCC patients, GSE98383 contains 11 cancer specimens and 24 paracancer specimens from 5 HDV-associated HCC patients, and 371 HCC patients with the RNA-sequencing data combined with their clinical data from the Cancer Genome Atlas (TCGA). Afterwards, 948 differentially expressed genes (DEGs) closely related to HDV-associated HCC were obtained using the R package and filtering with a Venn diagram. We then performed gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to determine the biological processes (BP), cellular component (CC), molecular function (MF), and KEGG signaling pathways most enriched for DEGs. Additionally, we performed Weighted Gene Coexpression Network Analysis (WGCNA) and protein-to-protein interaction (PPI) network construction with 948 DEGs, from which one module was identified by WGCNA and three modules were identified by the PPI network. Subsequently, we validated the expression of 52 hub genes from the PPI network with an independent set of HCC dataset stored in the Gene Expression Profiling Interactive Analysis (GEPIA) database. Finally, seven potential key genes were identified by intersecting with key modules from WGCNA, including 3 reported genes, namely, CDCA5, CENPH, and MCM7, and 4 novel genes, namely, CDC6, CDC45, CDCA8, and MCM4, which are associated with nucleoplasm, cell cycle, DNA replication, and mitotic cell cycle. The CDCA8 and stage of HCC were the independent factors associated with overall survival of HDV-associated HCC. All the related findings of these genes can help gain a better understanding of the role of HDV in the underlying mechanism of HCC carcinogenesis.

Dataset Information

Bioinformatics analysis of the gene expression profile of hepatocellular carcinoma: preliminary results.

Publications

Bioinformatics analysis of the gene expression profile of hepatocellular carcinoma: preliminary results.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets