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Cell-free DNA methylation markers for differential diagnosis of hepatocellular carcinoma
BACKGROUND: Aberrant DNA methylation may offer opportunities in revolutionizing cancer screening and diagnosis. We sought to identify a non-invasive DNA methylation-based screening approach using cell-free DNA (cfDNA) for early detection of hepatocellular carcinoma (HCC). METHODS: Differentially, DN...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8759185/ https://www.ncbi.nlm.nih.gov/pubmed/35027051 http://dx.doi.org/10.1186/s12916-021-02201-3 |
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author | Luo, Biyuan Ma, Fang Liu, Hao Hu, Jixiong Rao, Le Liu, Chun Jiang, Yongfang Kuangzeng, Shuyu Lin, Xuan Wang, Chenyang Lei, Yiyu Si, Zhongzhou Chen, Guangshun Zhou, Ning Liang, Chengbai Jiang, Fangqing Liu, Fenge Dai, Weidong Liu, Wei Gao, Yawen Li, Zhihong Li, Xi Zhou, Guangyu Li, Bingsi Zhang, Zhihong Nian, Weiqi Luo, Lihua Liu, Xianling |
author_facet | Luo, Biyuan Ma, Fang Liu, Hao Hu, Jixiong Rao, Le Liu, Chun Jiang, Yongfang Kuangzeng, Shuyu Lin, Xuan Wang, Chenyang Lei, Yiyu Si, Zhongzhou Chen, Guangshun Zhou, Ning Liang, Chengbai Jiang, Fangqing Liu, Fenge Dai, Weidong Liu, Wei Gao, Yawen Li, Zhihong Li, Xi Zhou, Guangyu Li, Bingsi Zhang, Zhihong Nian, Weiqi Luo, Lihua Liu, Xianling |
author_sort | Luo, Biyuan |
collection | PubMed |
description | BACKGROUND: Aberrant DNA methylation may offer opportunities in revolutionizing cancer screening and diagnosis. We sought to identify a non-invasive DNA methylation-based screening approach using cell-free DNA (cfDNA) for early detection of hepatocellular carcinoma (HCC). METHODS: Differentially, DNA methylation blocks were determined by comparing methylation profiles of biopsy-proven HCC, liver cirrhosis, and normal tissue samples with high throughput DNA bisulfite sequencing. A multi-layer HCC screening model was subsequently constructed based on tissue-derived differentially methylated blocks (DMBs). This model was tested in a cohort consisting of 120 HCC, 92 liver cirrhotic, and 290 healthy plasma samples including 65 hepatitis B surface antigen-seropositive (HBsAg+) samples, independently validated in a cohort consisting of 67 HCC, 111 liver cirrhotic, and 242 healthy plasma samples including 56 HBsAg+ samples. RESULTS: Based on methylation profiling of tissue samples, 2321 DMBs were identified, which were subsequently used to construct a cfDNA-based HCC screening model, achieved a sensitivity of 86% and specificity of 98% in the training cohort and a sensitivity of 84% and specificity of 96% in the independent validation cohort. This model obtained a sensitivity of 76% in 37 early-stage HCC (Barcelona clinical liver cancer [BCLC] stage 0-A) patients. The screening model can effectively discriminate HCC patients from non-HCC controls, including liver cirrhotic patients, asymptomatic HBsAg+ and healthy individuals, achieving an AUC of 0.957(95% CI 0.939–0.975), whereas serum α-fetoprotein (AFP) only achieved an AUC of 0.803 (95% CI 0.758–0.847). Besides detecting patients with early-stage HCC from non-HCC controls, this model showed high capacity for distinguishing early-stage HCC from a high risk population (AUC=0.934; 95% CI 0.905–0.963), also significantly outperforming AFP. Furthermore, our model also showed superior performance in distinguishing HCC with normal AFP (< 20ng ml(−1)) from high risk population (AUC=0.93; 95% CI 0.892–0.969). CONCLUSIONS: We have developed a sensitive blood-based non-invasive HCC screening model which can effectively distinguish early-stage HCC patients from high risk population and demonstrated its performance through an independent validation cohort. TRIAL REGISTRATION: The study was approved by the ethic committee of The Second Xiangya Hospital of Central South University (KYLL2018072) and Chongqing University Cancer Hospital (2019167). The study is registered at ClinicalTrials.gov(#NCT04383353). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-021-02201-3. |
format | Online Article Text |
id | pubmed-8759185 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87591852022-01-18 Cell-free DNA methylation markers for differential diagnosis of hepatocellular carcinoma Luo, Biyuan Ma, Fang Liu, Hao Hu, Jixiong Rao, Le Liu, Chun Jiang, Yongfang Kuangzeng, Shuyu Lin, Xuan Wang, Chenyang Lei, Yiyu Si, Zhongzhou Chen, Guangshun Zhou, Ning Liang, Chengbai Jiang, Fangqing Liu, Fenge Dai, Weidong Liu, Wei Gao, Yawen Li, Zhihong Li, Xi Zhou, Guangyu Li, Bingsi Zhang, Zhihong Nian, Weiqi Luo, Lihua Liu, Xianling BMC Med Research Article BACKGROUND: Aberrant DNA methylation may offer opportunities in revolutionizing cancer screening and diagnosis. We sought to identify a non-invasive DNA methylation-based screening approach using cell-free DNA (cfDNA) for early detection of hepatocellular carcinoma (HCC). METHODS: Differentially, DNA methylation blocks were determined by comparing methylation profiles of biopsy-proven HCC, liver cirrhosis, and normal tissue samples with high throughput DNA bisulfite sequencing. A multi-layer HCC screening model was subsequently constructed based on tissue-derived differentially methylated blocks (DMBs). This model was tested in a cohort consisting of 120 HCC, 92 liver cirrhotic, and 290 healthy plasma samples including 65 hepatitis B surface antigen-seropositive (HBsAg+) samples, independently validated in a cohort consisting of 67 HCC, 111 liver cirrhotic, and 242 healthy plasma samples including 56 HBsAg+ samples. RESULTS: Based on methylation profiling of tissue samples, 2321 DMBs were identified, which were subsequently used to construct a cfDNA-based HCC screening model, achieved a sensitivity of 86% and specificity of 98% in the training cohort and a sensitivity of 84% and specificity of 96% in the independent validation cohort. This model obtained a sensitivity of 76% in 37 early-stage HCC (Barcelona clinical liver cancer [BCLC] stage 0-A) patients. The screening model can effectively discriminate HCC patients from non-HCC controls, including liver cirrhotic patients, asymptomatic HBsAg+ and healthy individuals, achieving an AUC of 0.957(95% CI 0.939–0.975), whereas serum α-fetoprotein (AFP) only achieved an AUC of 0.803 (95% CI 0.758–0.847). Besides detecting patients with early-stage HCC from non-HCC controls, this model showed high capacity for distinguishing early-stage HCC from a high risk population (AUC=0.934; 95% CI 0.905–0.963), also significantly outperforming AFP. Furthermore, our model also showed superior performance in distinguishing HCC with normal AFP (< 20ng ml(−1)) from high risk population (AUC=0.93; 95% CI 0.892–0.969). CONCLUSIONS: We have developed a sensitive blood-based non-invasive HCC screening model which can effectively distinguish early-stage HCC patients from high risk population and demonstrated its performance through an independent validation cohort. TRIAL REGISTRATION: The study was approved by the ethic committee of The Second Xiangya Hospital of Central South University (KYLL2018072) and Chongqing University Cancer Hospital (2019167). The study is registered at ClinicalTrials.gov(#NCT04383353). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-021-02201-3. BioMed Central 2022-01-14 /pmc/articles/PMC8759185/ /pubmed/35027051 http://dx.doi.org/10.1186/s12916-021-02201-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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. |
spellingShingle | Research Article Luo, Biyuan Ma, Fang Liu, Hao Hu, Jixiong Rao, Le Liu, Chun Jiang, Yongfang Kuangzeng, Shuyu Lin, Xuan Wang, Chenyang Lei, Yiyu Si, Zhongzhou Chen, Guangshun Zhou, Ning Liang, Chengbai Jiang, Fangqing Liu, Fenge Dai, Weidong Liu, Wei Gao, Yawen Li, Zhihong Li, Xi Zhou, Guangyu Li, Bingsi Zhang, Zhihong Nian, Weiqi Luo, Lihua Liu, Xianling Cell-free DNA methylation markers for differential diagnosis of hepatocellular carcinoma |
title | Cell-free DNA methylation markers for differential diagnosis of hepatocellular carcinoma |
title_full | Cell-free DNA methylation markers for differential diagnosis of hepatocellular carcinoma |
title_fullStr | Cell-free DNA methylation markers for differential diagnosis of hepatocellular carcinoma |
title_full_unstemmed | Cell-free DNA methylation markers for differential diagnosis of hepatocellular carcinoma |
title_short | Cell-free DNA methylation markers for differential diagnosis of hepatocellular carcinoma |
title_sort | cell-free dna methylation markers for differential diagnosis of hepatocellular carcinoma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8759185/ https://www.ncbi.nlm.nih.gov/pubmed/35027051 http://dx.doi.org/10.1186/s12916-021-02201-3 |
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