DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups
Abstract Background Renal cell carcinoma (RCC) is the most common kidney cancer and includes several molecular and histological subtypes with different clinical characteristics. The combination of DNA methylation and gene expression data can improve the classification of tumor heterogeneity, by inco...
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| Format: | Article |
| Language: | English |
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BMC
2019-07-01
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| Series: | Cancer Cell International |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12935-019-0900-4 |
| _version_ | 1798045832368357376 |
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| author | Wenbiao Chen Jia Zhuang Peizhong Peter Wang Jingjing Jiang Chenhong Lin Ping Zeng Yan Liang Xujun Zhang Yong Dai Hongyan Diao |
| author_facet | Wenbiao Chen Jia Zhuang Peizhong Peter Wang Jingjing Jiang Chenhong Lin Ping Zeng Yan Liang Xujun Zhang Yong Dai Hongyan Diao |
| author_sort | Wenbiao Chen |
| collection | DOAJ |
| description | Abstract Background Renal cell carcinoma (RCC) is the most common kidney cancer and includes several molecular and histological subtypes with different clinical characteristics. The combination of DNA methylation and gene expression data can improve the classification of tumor heterogeneity, by incorporating differences at the epigenetic level and clinical features. Methods In this study, we identified the prognostic methylation and constructed specific prognosis-subgroups based on the DNA methylation spectrum of RCC from the TCGA database. Results Significant differences in DNA methylation profiles among the seven subgroups were revealed by consistent clustering using 3389 CpGs that indicated that were significant differences in prognosis. The specific DNA methylation patterns reflected differentially in the clinical index, including TNM classification, pathological grade, clinical stage, and age. In addition, 437 CpGs corresponding to 477 genes of 151 samples were identified as specific hyper/hypomethylation sites for each specific subgroup. A total of 277 and 212 genes corresponding to DNA methylation at promoter sites were enriched in transcription factor of GKLF and RREB-1, respectively. Finally, Bayesian network classifier with specific methylation sites was constructed and was used to verify the test set of prognoses into DNA methylation subgroups, which was found to be consistent with the classification results of the train set. DNA methylation-based classification can be used to identify the distinct subtypes of renal cell carcinoma. Conclusions This study shows that DNA methylation-based classification is highly relevant for future diagnosis and treatment of renal cell carcinoma as it identifies the prognostic value of each epigenetic subtype. |
| first_indexed | 2024-04-11T23:27:28Z |
| format | Article |
| id | doaj.art-4d5826fb63cb49bfbe37a99126347543 |
| institution | Directory Open Access Journal |
| issn | 1475-2867 |
| language | English |
| last_indexed | 2024-04-11T23:27:28Z |
| publishDate | 2019-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Cell International |
| spelling | doaj.art-4d5826fb63cb49bfbe37a991263475432022-12-22T03:57:16ZengBMCCancer Cell International1475-28672019-07-0119111410.1186/s12935-019-0900-4DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroupsWenbiao Chen0Jia Zhuang1Peizhong Peter Wang2Jingjing Jiang3Chenhong Lin4Ping Zeng5Yan Liang6Xujun Zhang7Yong Dai8Hongyan Diao9State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityDepartment of Urinary Surgery, Puning People’s Hospital, Puning People’s Hospital Affiliated To Southern Medical UniversityDivision of Community Health and Humanities, Faculty of Medicine, Memorial University of NewfoundlandState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityClinical Medical Research Center, Shenzhen People’s Hospital, The Second Clinical Medical College of Jinan UniversityState Key Laboratory for Diagnosis and Treatment of Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang UniversityAbstract Background Renal cell carcinoma (RCC) is the most common kidney cancer and includes several molecular and histological subtypes with different clinical characteristics. The combination of DNA methylation and gene expression data can improve the classification of tumor heterogeneity, by incorporating differences at the epigenetic level and clinical features. Methods In this study, we identified the prognostic methylation and constructed specific prognosis-subgroups based on the DNA methylation spectrum of RCC from the TCGA database. Results Significant differences in DNA methylation profiles among the seven subgroups were revealed by consistent clustering using 3389 CpGs that indicated that were significant differences in prognosis. The specific DNA methylation patterns reflected differentially in the clinical index, including TNM classification, pathological grade, clinical stage, and age. In addition, 437 CpGs corresponding to 477 genes of 151 samples were identified as specific hyper/hypomethylation sites for each specific subgroup. A total of 277 and 212 genes corresponding to DNA methylation at promoter sites were enriched in transcription factor of GKLF and RREB-1, respectively. Finally, Bayesian network classifier with specific methylation sites was constructed and was used to verify the test set of prognoses into DNA methylation subgroups, which was found to be consistent with the classification results of the train set. DNA methylation-based classification can be used to identify the distinct subtypes of renal cell carcinoma. Conclusions This study shows that DNA methylation-based classification is highly relevant for future diagnosis and treatment of renal cell carcinoma as it identifies the prognostic value of each epigenetic subtype.http://link.springer.com/article/10.1186/s12935-019-0900-4Renal cell carcinomaDNA methylationPrognosis subgroupsMolecular subtypes |
| spellingShingle | Wenbiao Chen Jia Zhuang Peizhong Peter Wang Jingjing Jiang Chenhong Lin Ping Zeng Yan Liang Xujun Zhang Yong Dai Hongyan Diao DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups Cancer Cell International Renal cell carcinoma DNA methylation Prognosis subgroups Molecular subtypes |
| title | DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups |
| title_full | DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups |
| title_fullStr | DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups |
| title_full_unstemmed | DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups |
| title_short | DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups |
| title_sort | dna methylation based classification and identification of renal cell carcinoma prognosis subgroups |
| topic | Renal cell carcinoma DNA methylation Prognosis subgroups Molecular subtypes |
| url | http://link.springer.com/article/10.1186/s12935-019-0900-4 |
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