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A CpG Methylation Signature as a Potential Marker for Early Diagnosis of Hepatocellular Carcinoma From HBV-Related Liver Disease Using Multiplex Bisulfite Sequencing.

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cam.issuedOnline2021-10-20
dc.contributor.authorLi, Kang
dc.contributor.authorSong, Yi
dc.contributor.authorQin, Ling
dc.contributor.authorLi, Ang
dc.contributor.authorJiang, Sanjie
dc.contributor.authorRen, Lei
dc.contributor.authorZang, Chaoran
dc.contributor.authorSun, Jianping
dc.contributor.authorZhao, Yan
dc.contributor.authorZhang, Yonghong
dc.date.accessioned2021-11-03T07:11:30Z
dc.date.available2021-11-03T07:11:30Z
dc.date.issued2021
dc.date.submitted2021-08-11
dc.date.updated2021-11-03T07:11:29Z
dc.description.abstractBACKGROUND: Aberrant methylation of CpG sites served as an epigenetic marker for building diagnostic, prognostic, and recurrence models for hepatocellular carcinoma (HCC). METHODS: Using Illumina 450K and EPIC Beadchip, we identified 34 CpG sites in peripheral blood mononuclear cell (PBMC) DNA that were differentially methylated in early HCC versus HBV-related liver diseases (HBVLD). We employed multiplex bisulfite sequencing (MBS) based on next-generation sequencing (NGS) to measure methylation of 34 CpG sites in PBMC DNA from 654 patients that were divided into a training set (n = 442) and a test set (n = 212). Using the training set, we selected and built a six-CpG-scorer (namely, cg14171514, cg07721852, cg05166871, cg18087306, cg05213896, and cg18772205), applying least absolute shrinkage and selection operator (LASSO) regression. We performed multivariable analyses of four candidate risk predictors (namely, six-CpG-scorer, age, sex, and AFP level), using 20 times imputation of missing data, non-linearly transformed, and backwards feature selection with logistic regression. The final model's regression coefficients were calculated according to "Rubin's Rules". The diagnostic accuracy of the model was internally validated with a 10,000 bootstrap validation dataset and then applied to the test set for validation. RESULTS: The area under the receiver operating characteristic curve (AUROC) of the model was 0.81 (95% CI, 0.77-0.85) and it showed good calibration and decision curve analysis. Using enhanced bootstrap validation, adjusted C-statistics and adjusted Brier score were 0.809 and 0.199, respectively. The model also showed an AUROC value of 0.84 (95% CI 0.79-0.88) of diagnosis for early HCC in the test set. CONCLUSIONS: Our model based on the six-CpG-scorer was a reliable diagnosis tool for early HCC from HBVLD. The usage of the MBS method can realize large-scale detection of CpG sites in clinical diagnosis of early HCC and benefit the majority of patients.
dc.identifier.doi10.17863/CAM.77663
dc.identifier.eissn2234-943X
dc.identifier.issn2234-943X
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330221
dc.languageen
dc.language.isoeng
dc.publisherFrontiers Media SA
dc.publisher.urlhttp://dx.doi.org/10.3389/fonc.2021.756326
dc.subjectCpG methylation
dc.subjectdiagnostic model
dc.subjectearly HCC
dc.subjectenhanced bootstrap validation
dc.subjectmultiplex bisulfite sequencing
dc.titleA CpG Methylation Signature as a Potential Marker for Early Diagnosis of Hepatocellular Carcinoma From HBV-Related Liver Disease Using Multiplex Bisulfite Sequencing.
dc.typeArticle
dcterms.dateAccepted2021-09-27
prism.publicationNameFront Oncol
prism.volume11
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
rioxxterms.versionVoR
rioxxterms.versionofrecord10.3389/fonc.2021.756326

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