Dataset Information

Knowledge-based analyses reveal new candidate genes associated with risk of hepatitis B virus related hepatocellular carcinoma.

ABSTRACT:

Background

Recent genome-wide association studies (GWASs) have suggested several susceptibility loci of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) by statistical analysis at individual single-nucleotide polymorphisms (SNPs). However, these loci only explain a small fraction of HBV-related HCC heritability. In the present study, we aimed to identify additional susceptibility loci of HBV-related HCC using advanced knowledge-based analysis.

Methods

We performed knowledge-based analysis (including gene- and gene-set-based association tests) on variant-level association p-values from two existing GWASs of HBV-related HCC. Five different types of gene-sets were collected for the association analysis. A number of SNPs within the gene prioritized by the knowledge-based association tests were selected to replicate genetic associations in an independent sample of 965 cases and 923 controls.

Results

The gene-based association analysis detected four genes significantly or suggestively associated with HBV-related HCC risk: SLC39A8, GOLGA8M, SMIM31, and WHAMMP2. The gene-set-based association analysis prioritized two promising gene sets for HCC, cell cycle G1/S transition and NOTCH1 intracellular domain regulates transcription. Within the gene sets, three promising candidate genes (CDC45, NCOR1 and KAT2A) were further prioritized for HCC. Among genes of liver-specific expression, multiple genes previously implicated in HCC were also highlighted. However, probably due to small sample size, none of the genes prioritized by the knowledge-based association analyses were successfully replicated by variant-level association test in the independent sample.

Conclusions

This comprehensive knowledge-based association mining study suggested several promising genes and gene-sets associated with HBV-related HCC risks, which would facilitate follow-up functional studies on the pathogenic mechanism of HCC.

SUBMITTER: Jiang D

PROVIDER: S-EPMC7216662 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Publications

Knowledge-based analyses reveal new candidate genes associated with risk of hepatitis B virus related hepatocellular carcinoma.

Jiang Deke D Deng Jiaen J Dong Changzheng C Ma Xiaopin X Xiao Qianyi Q Zhou Bin B Yang Chou C Wei Lin L Conran Carly C Zheng S Lilly SL Ng Irene Oi-Lin IO Yu Long L Xu Jianfeng J Sham Pak C PC Qi Xiaolong X Hou Jinlin J Ji Yuan Y Cao Guangwen G Li Miaoxin M

BMC cancer 20200511 1

<h4>Background</h4>Recent genome-wide association studies (GWASs) have suggested several susceptibility loci of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) by statistical analysis at individual single-nucleotide polymorphisms (SNPs). However, these loci only explain a small fraction of HBV-related HCC heritability. In the present study, we aimed to identify additional susceptibility loci of HBV-related HCC using advanced knowledge-based analysis.<h4>Methods</h4>We performed kn ...[more]

PMID: 32393195

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Project description:BackgroundThe molecular mechanism by which hepatitis B virus (HBV) induces hepatocellular carcinoma (HCC) is still unknown. The genomic expression profile and bioinformatics methods were used to investigate the potential pathogenesis and therapeutic targets for HBV-associated HCC (HBV-HCC).MethodsThe microarray dataset GSE55092 was downloaded from the Gene Expression Omnibus (GEO) database. The data was analyzed by the bioinformatics software to find differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, ingenuity pathway analysis (IPA), and protein-protein interaction (PPI) network analysis were then performed on DEGs. The hub genes were identified using Centiscape2.2 and Molecular Complex Detection (MCODE) in the Cytoscape software (Cytoscape_v3.7.2). The survival data of these hub genes was downloaded from the Gene Expression Profiling Interactive Analysis (GEPIA).ResultsA total of 2264 mRNA transcripts were differentially expressed, including 764 upregulated and 1500 downregulated in tumor tissues. GO analysis revealed that these DEGs were related to the small-molecule metabolic process, xenobiotic metabolic process, and cellular nitrogen compound metabolic process. KEGG pathway analysis revealed that metabolic pathways, complement and coagulation cascades, and chemical carcinogenesis were involved. Diseases and biofunctions showed that DEGs were mainly associated with the following diseases or biological function abnormalities: cancer, organismal injury and abnormalities, gastrointestinal disease, and hepatic system disease. The top 10 upstream regulators were predicted to be activated or inhibited by Z-score and identified 25 networks. The 10 genes with the highest degree of connectivity were defined as the hub genes. Cox regression revealed that all the 10 genes (CDC20, BUB1B, KIF11, TTK, EZH2, ZWINT, NDC80, TPX2, MELK, and KIF20A) were related to the overall survival.ConclusionOur study provided a registry of genes that play important roles in regulating the development of HBV-HCC, assisting us in understanding the molecular mechanisms that underlie the carcinogenesis and progression of HCC.

Project description:BackgroundHepatitis B Virus (HBV) infection is a global public health problem. After infection, patients experience a natural course from chronic hepatitis to cirrhosis and even Hepatitis B associated Hepatocellular Carcinoma (HBV-HCC). With the multi-omics research, many differentially expressed genes from chronic hepatitis to HCC stages have been discovered. All these provide important clues for new biomarkers and therapeutic targets. The purpose of this study is to explore the differential gene expression of HBV and HBV-related liver cancer, and analyze their enrichments and significance of related pathways.MethodsIn this study, we downloaded four microarray datasets GSE121248, GSE67764, GSE55092, GSE55092 and GSE83148 from the Gene Expression Omnibus (GEO) database. Using these four datasets, patients with chronic hepatitis B (CHB) differentially expressed genes (CHB DEGs) and patients with HBV-related HCC differentially expressed genes (HBV-HCC DEGs) were identified. Then Protein-protein Interaction (PPI) network analysis, Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to excavate the functional interaction of these two groups of DEGs and the common DEGs. Finally, the Kaplan website was used to analyze the role of these genes in HCC prognostic.ResultsA total of 241 CHB DEGs, 276 HBV-HCC DEGs, and 4 common DEGs (cytochrome P450 family 26 subfamily A member 1 (CYP26A1), family with sequence similarity 110 member C(FAM110C), SET and MYND domain containing 3(SMYD3) and zymogen granule protein 16(ZG16)) were identified. CYP26A1, FAM110C, SMYD3 and ZG16 exist in 4 models and interact with 33 genes in the PPI network of CHB and HBV-HCC DEGs,. GO function analysis showed that: CYP26A1, FAM110C, SMYD3, ZG16, and the 33 genes in their models mainly affect the regulation of synaptic vesicle transport, tangential migration from the subventricular zone to the olfactory bulb, cellular response to manganese ion, protein localization to mitochondrion, cellular response to dopamine, negative regulation of neuron death in the biological process of CHB. In the biological process of HBV-HCC, they mainly affect tryptophan catabolic process, ethanol oxidation, drug metabolic process, tryptophan catabolic process to kynurenine, xenobiotic metabolic process, retinoic acid metabolic process, steroid metabolic process, retinoid metabolic process, steroid catabolic process, retinal metabolic process, and rogen metabolic process. The analysis of the 4 common DEGs related to the prognosis of liver cancer showed that: CYP26A1, FAM110C, SMYD3 and ZG16 are closely related to the development of liver cancer and patient survival. Besides, further investigation of the research status of the four genes showed that CYP26A1 and SMYD3 could also affect HBV replication and the prognosis of liver cancer.ConclusionCYP26A1, FAM110C, SMYD3 and ZG16 are unique genes to differentiate HBV infection and HBV-related HCC, and expected to be novel targets for HBV-related HCC occurrence and prognostic judgement.

Dataset Information

Knowledge-based analyses reveal new candidate genes associated with risk of hepatitis B virus related hepatocellular carcinoma.

Background

Methods

Results

Conclusions

Publications

Knowledge-based analyses reveal new candidate genes associated with risk of hepatitis B virus related hepatocellular carcinoma.

Similar Datasets

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