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. 2020 Aug 3;18:200. doi: 10.1186/s12916-020-01667-x

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© The Author(s) 2020

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 4 — Hypomethylation around HBV integration regions aid HCC prediction. a Boxplot displays Methyl_HBV5k and the methylation level of top 5 selected regions in all the 54 samples. b Receiver operating characteristics (ROC) curve based on fivefold cross-validation for HCC detection by different indicators in discriminating HCC patients from individuals without HCC (healthy individuals, patients with hepatitis and cirrhosis). c The comparisons between healthy individuals and patients with early-stage HCC using Methyl_HBV5k and the methylation level of top 5 selected regions in the validation dataset. d ROC curves for 16 healthy individuals and 24 HCC patients in the validation cohort using genome-wide hypomethylation analysis and region 5. e ROC curves for HCC patient detection using all the healthy individuals and HCC patients in the validation cohort by the identified features