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Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction
BACKGROUND AND AIM: Preoperative evaluation of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is important for surgical strategy determination. We aimed to develop and establish a preoperative predictive model for MVI status based on DNA methylation markers. METHODS: A...
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/PMC9720918/ https://www.ncbi.nlm.nih.gov/pubmed/36464701 http://dx.doi.org/10.1186/s40001-022-00910-w |
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author | Hao, Yijie Yang, Qingxia He, Qiye Hu, Huanjing Weng, Zongpeng Su, Zhixi Chen, Shuling Peng, Sui Kuang, Ming Chen, Zhihang Xu, Lixia |
author_facet | Hao, Yijie Yang, Qingxia He, Qiye Hu, Huanjing Weng, Zongpeng Su, Zhixi Chen, Shuling Peng, Sui Kuang, Ming Chen, Zhihang Xu, Lixia |
author_sort | Hao, Yijie |
collection | PubMed |
description | BACKGROUND AND AIM: Preoperative evaluation of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is important for surgical strategy determination. We aimed to develop and establish a preoperative predictive model for MVI status based on DNA methylation markers. METHODS: A total of 35 HCC tissues and the matched peritumoral normal liver tissues as well as 35 corresponding HCC patients’ plasma samples and 24 healthy plasma samples were used for genome-wide methylation sequencing and subsequent methylation haplotype block (MHB) analysis. Predictive models were constructed based on selected MHB markers and 3-cross validation was used. RESULTS: We grouped 35 HCC patients into 2 categories, including the MVI− group with 17 tissue and plasma samples, and MVI + group with 18 tissue and plasma samples. We identified a tissue DNA methylation signature with an AUC of 98.0% and a circulating free DNA (cfDNA) methylation signature with an AUC of 96.0% for HCC detection. Furthermore, we established a tissue DNA methylation signature for MVI status prediction, and achieved an AUC of 85.9%. Based on the MVI status predicted by the DNA methylation signature, the recurrence-free survival (RFS) and overall survival (OS) were significantly better in the predicted MVI− group than that in the predicted MVI + group. CONCLUSIONS: In this study, we identified a cfDNA methylation signature for HCC detection and a tissue DNA methylation signature for MVI status prediction with high accuracy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40001-022-00910-w. |
format | Online Article Text |
id | pubmed-9720918 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-97209182022-12-06 Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction Hao, Yijie Yang, Qingxia He, Qiye Hu, Huanjing Weng, Zongpeng Su, Zhixi Chen, Shuling Peng, Sui Kuang, Ming Chen, Zhihang Xu, Lixia Eur J Med Res Research BACKGROUND AND AIM: Preoperative evaluation of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) is important for surgical strategy determination. We aimed to develop and establish a preoperative predictive model for MVI status based on DNA methylation markers. METHODS: A total of 35 HCC tissues and the matched peritumoral normal liver tissues as well as 35 corresponding HCC patients’ plasma samples and 24 healthy plasma samples were used for genome-wide methylation sequencing and subsequent methylation haplotype block (MHB) analysis. Predictive models were constructed based on selected MHB markers and 3-cross validation was used. RESULTS: We grouped 35 HCC patients into 2 categories, including the MVI− group with 17 tissue and plasma samples, and MVI + group with 18 tissue and plasma samples. We identified a tissue DNA methylation signature with an AUC of 98.0% and a circulating free DNA (cfDNA) methylation signature with an AUC of 96.0% for HCC detection. Furthermore, we established a tissue DNA methylation signature for MVI status prediction, and achieved an AUC of 85.9%. Based on the MVI status predicted by the DNA methylation signature, the recurrence-free survival (RFS) and overall survival (OS) were significantly better in the predicted MVI− group than that in the predicted MVI + group. CONCLUSIONS: In this study, we identified a cfDNA methylation signature for HCC detection and a tissue DNA methylation signature for MVI status prediction with high accuracy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40001-022-00910-w. BioMed Central 2022-12-05 /pmc/articles/PMC9720918/ /pubmed/36464701 http://dx.doi.org/10.1186/s40001-022-00910-w Text en © The Author(s) 2022 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 Hao, Yijie Yang, Qingxia He, Qiye Hu, Huanjing Weng, Zongpeng Su, Zhixi Chen, Shuling Peng, Sui Kuang, Ming Chen, Zhihang Xu, Lixia Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction |
title | Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction |
title_full | Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction |
title_fullStr | Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction |
title_full_unstemmed | Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction |
title_short | Identification of DNA methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction |
title_sort | identification of dna methylation signatures for hepatocellular carcinoma detection and microvascular invasion prediction |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9720918/ https://www.ncbi.nlm.nih.gov/pubmed/36464701 http://dx.doi.org/10.1186/s40001-022-00910-w |
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