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DNA methylation-based classification and identification of bladder cancer prognosis-associated subgroups
BACKGROUND: Bladder cancer (BCA) is the most common urinary tumor, but its pathogenesis is unclear, and the associated treatment strategy has rarely been updated. In recent years, a deeper understanding of tumor epigenetics has been gained, providing new opportunities for cancer detection and treatm...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302382/ https://www.ncbi.nlm.nih.gov/pubmed/32565739 http://dx.doi.org/10.1186/s12935-020-01345-1 |
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author | Tian, Zijian Meng, Lingfeng Long, Xingbo Diao, Tongxiang Hu, Maolin Wang, Miao Liu, Ming Wang, Jianye |
author_facet | Tian, Zijian Meng, Lingfeng Long, Xingbo Diao, Tongxiang Hu, Maolin Wang, Miao Liu, Ming Wang, Jianye |
author_sort | Tian, Zijian |
collection | PubMed |
description | BACKGROUND: Bladder cancer (BCA) is the most common urinary tumor, but its pathogenesis is unclear, and the associated treatment strategy has rarely been updated. In recent years, a deeper understanding of tumor epigenetics has been gained, providing new opportunities for cancer detection and treatment. METHODS: We identified prognostic methylation sites based on DNA methylation profiles of BCA in the TCGA database and constructed a specific prognostic subgroup. RESULTS: Based on the consistent clustering of 402 CpGs, we identified seven subgroups that had a significant association with survival. The difference in DNA methylation levels was related to T stage, N stage, M stage, grade, sex, age, stage and prognosis. Finally, the prediction model was constructed using a Cox regression model and verified using the test dataset; the prognosis was consistent with that of the training set. CONCLUSIONS: The classification based on DNA methylation is closely related to the clinicopathological characteristics of BCA and determines the prognostic value of each epigenetic subtype. Therefore, our findings provide a basis for the development of DNA methylation subtype-specific therapeutic strategies for human bladder cancer. |
format | Online Article Text |
id | pubmed-7302382 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-73023822020-06-19 DNA methylation-based classification and identification of bladder cancer prognosis-associated subgroups Tian, Zijian Meng, Lingfeng Long, Xingbo Diao, Tongxiang Hu, Maolin Wang, Miao Liu, Ming Wang, Jianye Cancer Cell Int Primary Research BACKGROUND: Bladder cancer (BCA) is the most common urinary tumor, but its pathogenesis is unclear, and the associated treatment strategy has rarely been updated. In recent years, a deeper understanding of tumor epigenetics has been gained, providing new opportunities for cancer detection and treatment. METHODS: We identified prognostic methylation sites based on DNA methylation profiles of BCA in the TCGA database and constructed a specific prognostic subgroup. RESULTS: Based on the consistent clustering of 402 CpGs, we identified seven subgroups that had a significant association with survival. The difference in DNA methylation levels was related to T stage, N stage, M stage, grade, sex, age, stage and prognosis. Finally, the prediction model was constructed using a Cox regression model and verified using the test dataset; the prognosis was consistent with that of the training set. CONCLUSIONS: The classification based on DNA methylation is closely related to the clinicopathological characteristics of BCA and determines the prognostic value of each epigenetic subtype. Therefore, our findings provide a basis for the development of DNA methylation subtype-specific therapeutic strategies for human bladder cancer. BioMed Central 2020-06-17 /pmc/articles/PMC7302382/ /pubmed/32565739 http://dx.doi.org/10.1186/s12935-020-01345-1 Text en © The Author(s) 2020 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/. 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 in a credit line to the data. |
spellingShingle | Primary Research Tian, Zijian Meng, Lingfeng Long, Xingbo Diao, Tongxiang Hu, Maolin Wang, Miao Liu, Ming Wang, Jianye DNA methylation-based classification and identification of bladder cancer prognosis-associated subgroups |
title | DNA methylation-based classification and identification of bladder cancer prognosis-associated subgroups |
title_full | DNA methylation-based classification and identification of bladder cancer prognosis-associated subgroups |
title_fullStr | DNA methylation-based classification and identification of bladder cancer prognosis-associated subgroups |
title_full_unstemmed | DNA methylation-based classification and identification of bladder cancer prognosis-associated subgroups |
title_short | DNA methylation-based classification and identification of bladder cancer prognosis-associated subgroups |
title_sort | dna methylation-based classification and identification of bladder cancer prognosis-associated subgroups |
topic | Primary Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302382/ https://www.ncbi.nlm.nih.gov/pubmed/32565739 http://dx.doi.org/10.1186/s12935-020-01345-1 |
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