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A DNA CpG Methylation Signature Based Diagnosis Model for Early Hepatocellular Carcinoma from HBV-Related Liver Disease

36 Pages Posted: 1 Jun 2021

See all articles by Kang Li

Kang Li

Capital Medical University - Biomedical Information Center

Yi Song

Capital Medical University - Experimental Center

Ling Qin

Capital Medical University - Beijing YouAn Hospital

Ang Li

Capital Medical University - Beijing YouAn Hospital

Sanjie Jiang

Capital Medical University - Biomedical Information Center

Lei Ren

Government of the People's Republic of China - Pharmacology Department

Guifang Qiao

Capital Medical University - Biomedical Information Center

Yanchao Dai

Capital Medical University - Beijing YouAn Hospital

Chaoran Zang

Capital Medical University - Biomedical Information Center

Jian ping Sun

Capital Medical University - Biomedical Information Center

Yan Zhao

Capital Medical University - Clinical Laboratory Center

Yonghong Zhang

Capital Medical University - Beijing YouAn Hospital

More...

Abstract

Background: Aberrant methylation of CpG sites severed as 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 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), an test set (n = 212). Using training set, we selected and built a six-CpG-scorer (including 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 (including six-CpG-scorer, age, sex, 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 model was internally validated with 10000 bootstrap validation dataset, and then applied to the test set for validation.

Findings: 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, decision curve analysis. Using enhanced bootstrap validation, adjusted C-statistics and adjusted brier score was 0.809 and 0.199, respectively. The model also showed AUROC value of 0.84 (95% CI 0.79-0.88) of diagnosis for early HCC in test set.

Interpretation: Our model based six-CpG-scorer was a reliable diagnosis tool for early HCC from HBVLD. The use of MBS method can realize large-scale detection of CpG loci in clinical diagnosis of early HCC, and benefit the majority of patients.

Funding: This project was supported by grants National Key R&D Program of China (2020YFE0202400), Beijing Natural Science Foundation (7202069, 7191004), Capital’s Funds of Health Improvement and Research (CFH2020-1-2182, CFH2020-2-1153) and Beijing Key Laboratory (BZ0373).

Declaration of Interest: The authors declare no conflict of interest.

Ethical Approval: The study was approved by the Institutional Ethics Committee of the Beijing You’An Hospital. All participants signed written informed consents on enrolment.

Keywords: Multiplex bisulfite sequencing; methylation of CpG sites; early HCC; diagnostic model; enhanced bootstrap validation

Suggested Citation

Li, Kang and Song, Yi and Qin, Ling and Li, Ang and Jiang, Sanjie and Ren, Lei and Qiao, Guifang and Dai, Yanchao and Zang, Chaoran and Sun, Jian ping and Zhao, Yan and Zhang, Yonghong, A DNA CpG Methylation Signature Based Diagnosis Model for Early Hepatocellular Carcinoma from HBV-Related Liver Disease. Available at SSRN: https://ssrn.com/abstract=3857604 or http://dx.doi.org/10.2139/ssrn.3857604

Kang Li

Capital Medical University - Biomedical Information Center

Beijing
China

Yi Song

Capital Medical University - Experimental Center

China

Ling Qin

Capital Medical University - Beijing YouAn Hospital

China

Ang Li

Capital Medical University - Beijing YouAn Hospital

Beijing, 100069
China

Sanjie Jiang

Capital Medical University - Biomedical Information Center ( email )

Beijing
China

Lei Ren

Government of the People's Republic of China - Pharmacology Department

Beijing
China

Guifang Qiao

Capital Medical University - Biomedical Information Center ( email )

Beijing
China

Yanchao Dai

Capital Medical University - Beijing YouAn Hospital

China

Chaoran Zang

Capital Medical University - Biomedical Information Center ( email )

Beijing
China

Jian ping Sun

Capital Medical University - Biomedical Information Center

Beijing
China

Yan Zhao

Capital Medical University - Clinical Laboratory Center

Beijing, 100069
China

Yonghong Zhang (Contact Author)

Capital Medical University - Beijing YouAn Hospital ( email )

China