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Integrative analysis of DNA methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkers
BACKGROUND: Hepatocellular carcinoma (HCC) is the one of the most common cancers and lethal diseases in the world. DNA methylation alteration is frequently observed in HCC and may play important roles in carcinogenesis and diagnosis. METHODS: Using the TCGA HCC dataset, we classified HCC patients in...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977535/ https://www.ncbi.nlm.nih.gov/pubmed/29848370 http://dx.doi.org/10.1186/s13073-018-0548-z |
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author | Cheng, Jinming Wei, Dongkai Ji, Yuan Chen, Lingli Yang, Liguang Li, Guang Wu, Leilei Hou, Ting Xie, Lu Ding, Guohui Li, Hong Li, Yixue |
author_facet | Cheng, Jinming Wei, Dongkai Ji, Yuan Chen, Lingli Yang, Liguang Li, Guang Wu, Leilei Hou, Ting Xie, Lu Ding, Guohui Li, Hong Li, Yixue |
author_sort | Cheng, Jinming |
collection | PubMed |
description | BACKGROUND: Hepatocellular carcinoma (HCC) is the one of the most common cancers and lethal diseases in the world. DNA methylation alteration is frequently observed in HCC and may play important roles in carcinogenesis and diagnosis. METHODS: Using the TCGA HCC dataset, we classified HCC patients into different methylation subtypes, identified differentially methylated and expressed genes, and analyzed cis- and trans-regulation of DNA methylation and gene expression. To find potential diagnostic biomarkers for HCC, we screened HCC-specific CpGs by comparing the methylation profiles of 375 samples from HCC patients, 50 normal liver samples, 184 normal blood samples, and 3780 samples from patients with other cancers. A logistic regression model was constructed to distinguish HCC patients from normal controls. Model performance was evaluated using three independent datasets (including 327 HCC samples and 122 normal samples) and ten newly collected biopsies. RESULTS: We identified a group of patients with a CpG island methylator phenotype (CIMP) and found that the overall survival of CIMP patients was poorer than that of non-CIMP patients. Our analyses showed that the cis-regulation of DNA methylation and gene expression was dominated by the negative correlation, while the trans-regulation was more complex. More importantly, we identified six HCC-specific hypermethylated sites as potential diagnostic biomarkers. The combination of six sites achieved ~ 92% sensitivity in predicting HCC, ~ 98% specificity in excluding normal livers, and ~ 98% specificity in excluding other cancers. Compared with previously published methylation markers, our markers are the only ones that can distinguish HCC from other cancers. CONCLUSIONS: Overall, our study systematically describes the DNA methylation characteristics of HCC and provides promising biomarkers for the diagnosis of HCC. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13073-018-0548-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5977535 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-59775352018-06-06 Integrative analysis of DNA methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkers Cheng, Jinming Wei, Dongkai Ji, Yuan Chen, Lingli Yang, Liguang Li, Guang Wu, Leilei Hou, Ting Xie, Lu Ding, Guohui Li, Hong Li, Yixue Genome Med Research BACKGROUND: Hepatocellular carcinoma (HCC) is the one of the most common cancers and lethal diseases in the world. DNA methylation alteration is frequently observed in HCC and may play important roles in carcinogenesis and diagnosis. METHODS: Using the TCGA HCC dataset, we classified HCC patients into different methylation subtypes, identified differentially methylated and expressed genes, and analyzed cis- and trans-regulation of DNA methylation and gene expression. To find potential diagnostic biomarkers for HCC, we screened HCC-specific CpGs by comparing the methylation profiles of 375 samples from HCC patients, 50 normal liver samples, 184 normal blood samples, and 3780 samples from patients with other cancers. A logistic regression model was constructed to distinguish HCC patients from normal controls. Model performance was evaluated using three independent datasets (including 327 HCC samples and 122 normal samples) and ten newly collected biopsies. RESULTS: We identified a group of patients with a CpG island methylator phenotype (CIMP) and found that the overall survival of CIMP patients was poorer than that of non-CIMP patients. Our analyses showed that the cis-regulation of DNA methylation and gene expression was dominated by the negative correlation, while the trans-regulation was more complex. More importantly, we identified six HCC-specific hypermethylated sites as potential diagnostic biomarkers. The combination of six sites achieved ~ 92% sensitivity in predicting HCC, ~ 98% specificity in excluding normal livers, and ~ 98% specificity in excluding other cancers. Compared with previously published methylation markers, our markers are the only ones that can distinguish HCC from other cancers. CONCLUSIONS: Overall, our study systematically describes the DNA methylation characteristics of HCC and provides promising biomarkers for the diagnosis of HCC. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13073-018-0548-z) contains supplementary material, which is available to authorized users. BioMed Central 2018-05-30 /pmc/articles/PMC5977535/ /pubmed/29848370 http://dx.doi.org/10.1186/s13073-018-0548-z Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. |
spellingShingle | Research Cheng, Jinming Wei, Dongkai Ji, Yuan Chen, Lingli Yang, Liguang Li, Guang Wu, Leilei Hou, Ting Xie, Lu Ding, Guohui Li, Hong Li, Yixue Integrative analysis of DNA methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkers |
title | Integrative analysis of DNA methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkers |
title_full | Integrative analysis of DNA methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkers |
title_fullStr | Integrative analysis of DNA methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkers |
title_full_unstemmed | Integrative analysis of DNA methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkers |
title_short | Integrative analysis of DNA methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkers |
title_sort | integrative analysis of dna methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkers |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977535/ https://www.ncbi.nlm.nih.gov/pubmed/29848370 http://dx.doi.org/10.1186/s13073-018-0548-z |
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