Integrative analysis of DNA methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkers
Abstract 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 pati...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-05-01
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| Series: | Genome Medicine |
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| Online Access: | http://link.springer.com/article/10.1186/s13073-018-0548-z |
| _version_ | 1818242704082993152 |
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| author | Jinming Cheng Dongkai Wei Yuan Ji Lingli Chen Liguang Yang Guang Li Leilei Wu Ting Hou Lu Xie Guohui Ding Hong Li Yixue Li |
| author_facet | Jinming Cheng Dongkai Wei Yuan Ji Lingli Chen Liguang Yang Guang Li Leilei Wu Ting Hou Lu Xie Guohui Ding Hong Li Yixue Li |
| author_sort | Jinming Cheng |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-12T13:49:27Z |
| format | Article |
| id | doaj.art-d5b2e3c7a4944d15adbad2e449b00955 |
| institution | Directory Open Access Journal |
| issn | 1756-994X |
| language | English |
| last_indexed | 2024-12-12T13:49:27Z |
| publishDate | 2018-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Medicine |
| spelling | doaj.art-d5b2e3c7a4944d15adbad2e449b009552022-12-22T00:22:36ZengBMCGenome Medicine1756-994X2018-05-0110111110.1186/s13073-018-0548-zIntegrative analysis of DNA methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkersJinming Cheng0Dongkai Wei1Yuan Ji2Lingli Chen3Liguang Yang4Guang Li5Leilei Wu6Ting Hou7Lu Xie8Guohui Ding9Hong Li10Yixue Li11Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityBasepair biotechnology Co. LTDDepartment of Pathology, Zhongshan Hospital, Fudan UniversityDepartment of Pathology, Zhongshan Hospital, Fudan UniversityKey Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of SciencesBasepair biotechnology Co. LTDDepartment of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityKey Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of SciencesShanghai Center for Bioinformation Technology, Shanghai Academy of Science and TechnologyKey Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of SciencesKey Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of SciencesDepartment of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong UniversityAbstract 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.http://link.springer.com/article/10.1186/s13073-018-0548-zHepatocellular carcinomaMethylationCpG island methylator phenotypeGene regulationSpecific diagnostic biomarker |
| spellingShingle | Jinming Cheng Dongkai Wei Yuan Ji Lingli Chen Liguang Yang Guang Li Leilei Wu Ting Hou Lu Xie Guohui Ding Hong Li Yixue Li Integrative analysis of DNA methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkers Genome Medicine Hepatocellular carcinoma Methylation CpG island methylator phenotype Gene regulation Specific diagnostic biomarker |
| 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 | Hepatocellular carcinoma Methylation CpG island methylator phenotype Gene regulation Specific diagnostic biomarker |
| url | http://link.springer.com/article/10.1186/s13073-018-0548-z |
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