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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

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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

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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.

Project description:Objective: The study aimed to elucidate the significance of CLEC4G, CAMK2β, SLC22A1, CBFA2T3, and STAB2 in the prognosis of hepatocellular carcinoma (HCC) patients and their associated molecular biological characteristics. Additionally, the research sought to identify new potential biomarkers with therapeutic and diagnostic relevance for clinical applications. Methods and Materials: We utilized a publicly available high throughput phosphoproteomics and proteomics data set of HCC to focus on the analysis of 12 downregulated phosphoproteins in HCC. Our approach integrates bioinformatic analysis with pathway analysis, encompassing gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and the construction of a protein-protein interaction (PPI) network. Results: In total, we quantified 11547 phosphorylation sites associated with 4043 phosphoproteins from a cohort of 159 HCC patients. Within this extensive data set, our specific focus was on 19 phosphorylation sites displaying significant downregulation (log2 FC ≤ -2 with p-values < 0.0001). Remarkably, our investigation revealed distinct pathways exhibiting differential regulation across multiple dimensions, including the genomic, transcriptomic, proteomic, and phosphoproteomic levels. These pathways encompass a wide range of critical cellular processes, including cellular component organization, cell cycle control, signaling pathways, transcriptional and translational control, and metabolism. Furthermore, our bioinformatics analysis unveiled noteworthy insights into the subcellular localizations, biological processes, and molecular functions associated with these proteins and phosphoproteins. Within the context of the PPI network, we identified 12 key genes CLEC4G, STAB2, ADH1A, ADH1B, CAMK2B, ADH4, CHGB, PYGL, ADH1C, AKAP12, CBFA2T3, and SLC22A1 as the top highly interconnected hub genes. Conclusions: The findings related to CLEC4G, ADH1B, SLC22A1, CAMK2β, CBFA2T3, and STAB2 indicate their reduced expression in HCC, which is associated with an unfavorable prognosis. Furthermore, the results of KEGG and GO pathway analyses suggest that these genes may impact liver cancer by engaging various targets and pathways, ultimately promoting the progression of hepatocellular carcinoma. These results underscore the significant potential of CLEC4G, ADH1B, SLC22A1, CAMK2β, CBFA2T3, and STAB2 as key contributors to HCC development and advancement. This insight holds promise for identifying therapeutic targets and charting research avenues to enhance our understanding of the intricate molecular mechanisms underlying hepatocellular carcinoma.

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.

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