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

TitleKnowledge-based analyses reveal new candidate genes associated with risk of hepatitis B virus related hepatocellular carcinoma
Authors
KeywordsKnowledge-based genetic association
Susceptibility
Hepatitis B virus
Hepatocellular carcinoma
Issue Date2020
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmccancer/
Citation
BMC Cancer, 2020, v. 20, p. article no. 403 How to Cite?
AbstractBackground: 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.
Persistent Identifierhttp://hdl.handle.net/10722/304752
ISSN
2023 Impact Factor: 3.4
2023 SCImago Journal Rankings: 1.087
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJiang, DK-
dc.contributor.authorDeng, J-
dc.contributor.authorDong, C-
dc.contributor.authorMa, X-
dc.contributor.authorXiao, Q-
dc.contributor.authorZhou, B-
dc.contributor.authorYang, C-
dc.contributor.authorWei, L-
dc.contributor.authorConran, C-
dc.contributor.authorZheng, SL-
dc.contributor.authorNg, IOL-
dc.contributor.authorYu, L-
dc.contributor.authorXu, J-
dc.contributor.authorSham, PC-
dc.contributor.authorQi, X-
dc.contributor.authorHou, J-
dc.contributor.authorJi, Y-
dc.contributor.authorCao, G-
dc.contributor.authorLi, M-
dc.date.accessioned2021-10-05T02:34:40Z-
dc.date.available2021-10-05T02:34:40Z-
dc.date.issued2020-
dc.identifier.citationBMC Cancer, 2020, v. 20, p. article no. 403-
dc.identifier.issn1471-2407-
dc.identifier.urihttp://hdl.handle.net/10722/304752-
dc.description.abstractBackground: 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.-
dc.languageeng-
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmccancer/-
dc.relation.ispartofBMC Cancer-
dc.rightsBMC Cancer. Copyright © BioMed Central Ltd.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectKnowledge-based genetic association-
dc.subjectSusceptibility-
dc.subjectHepatitis B virus-
dc.subjectHepatocellular carcinoma-
dc.titleKnowledge-based analyses reveal new candidate genes associated with risk of hepatitis B virus related hepatocellular carcinoma-
dc.typeArticle-
dc.identifier.emailNg, IOL: iolng@hku.hk-
dc.identifier.emailSham, PC: pcsham@hku.hk-
dc.identifier.authorityNg, IOL=rp00335-
dc.identifier.authoritySham, PC=rp00459-
dc.identifier.authorityLi, M=rp01722-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/s12885-020-06842-0-
dc.identifier.pmid32393195-
dc.identifier.pmcidPMC7216662-
dc.identifier.scopuseid_2-s2.0-85084579198-
dc.identifier.hkuros325815-
dc.identifier.volume20-
dc.identifier.spagearticle no. 403-
dc.identifier.epagearticle no. 403-
dc.identifier.isiWOS:000534338800001-
dc.publisher.placeUnited Kingdom-

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