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Development of a novel prognostic signature for predicting the overall survival of bladder cancer patients
Background: Bladder cancer is one of the most common malignancies. So far, no effective biomarker for bladder cancer prognosis has been identified. Aberrant DNA methylation is frequently observed in the bladder cancer and holds considerable promise as a biomarker for predicting the overall survival...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Portland Press Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7286875/ https://www.ncbi.nlm.nih.gov/pubmed/32441304 http://dx.doi.org/10.1042/BSR20194432 |
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author | Tang, Huamei Kan, Lijuan Ou, Tong Chen, Dayang Dou, Xiaowen Wu, Wei Ji, Xiang Wang, Mengmeng Zong, Zengyan Mo, Hongmei Zhang, Xiuming Xiong, Dan |
author_facet | Tang, Huamei Kan, Lijuan Ou, Tong Chen, Dayang Dou, Xiaowen Wu, Wei Ji, Xiang Wang, Mengmeng Zong, Zengyan Mo, Hongmei Zhang, Xiuming Xiong, Dan |
author_sort | Tang, Huamei |
collection | PubMed |
description | Background: Bladder cancer is one of the most common malignancies. So far, no effective biomarker for bladder cancer prognosis has been identified. Aberrant DNA methylation is frequently observed in the bladder cancer and holds considerable promise as a biomarker for predicting the overall survival (OS) of patients. Materials and methods: We downloaded the DNA methylation and transcriptome data for bladder cancer from The Cancer Genome Atlas (TCGA), a public database, screened hypo-methylated and up-regulated genes, similarly, hyper-methylation with low expression genes, then retrieved the relevant methylation sites. Cox regression analysis was used to identify a nine-methylation site signature of a training group. Predictive ability was validated in a test group by receiver operating characteristic (ROC) analysis. Results: We identified nine bladder cancer-specific methylation sites as potential prognostic biomarkers and established a risk score system based on the methylation site signature to evaluate the OS. The performance of the signature was accurate, with area under curve was 0.73 in the training group and 0.71 in the test group. Taking clinical features into consideration, we constructed a nomogram consisting of the nine-methylation site signature and patients’ clinical variables, and found that the signature was an independent risk factor. Conclusions: Overall, the significant nine methylation sites could be novel prediction biomarkers, which could aid in treatment and also predict the overall survival likelihoods of bladder cancer patients. |
format | Online Article Text |
id | pubmed-7286875 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Portland Press Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72868752020-06-17 Development of a novel prognostic signature for predicting the overall survival of bladder cancer patients Tang, Huamei Kan, Lijuan Ou, Tong Chen, Dayang Dou, Xiaowen Wu, Wei Ji, Xiang Wang, Mengmeng Zong, Zengyan Mo, Hongmei Zhang, Xiuming Xiong, Dan Biosci Rep Bioinformatics Background: Bladder cancer is one of the most common malignancies. So far, no effective biomarker for bladder cancer prognosis has been identified. Aberrant DNA methylation is frequently observed in the bladder cancer and holds considerable promise as a biomarker for predicting the overall survival (OS) of patients. Materials and methods: We downloaded the DNA methylation and transcriptome data for bladder cancer from The Cancer Genome Atlas (TCGA), a public database, screened hypo-methylated and up-regulated genes, similarly, hyper-methylation with low expression genes, then retrieved the relevant methylation sites. Cox regression analysis was used to identify a nine-methylation site signature of a training group. Predictive ability was validated in a test group by receiver operating characteristic (ROC) analysis. Results: We identified nine bladder cancer-specific methylation sites as potential prognostic biomarkers and established a risk score system based on the methylation site signature to evaluate the OS. The performance of the signature was accurate, with area under curve was 0.73 in the training group and 0.71 in the test group. Taking clinical features into consideration, we constructed a nomogram consisting of the nine-methylation site signature and patients’ clinical variables, and found that the signature was an independent risk factor. Conclusions: Overall, the significant nine methylation sites could be novel prediction biomarkers, which could aid in treatment and also predict the overall survival likelihoods of bladder cancer patients. Portland Press Ltd. 2020-06-10 /pmc/articles/PMC7286875/ /pubmed/32441304 http://dx.doi.org/10.1042/BSR20194432 Text en © 2020 The Author(s). https://creativecommons.org/licenses/by/4.0/ This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY). |
spellingShingle | Bioinformatics Tang, Huamei Kan, Lijuan Ou, Tong Chen, Dayang Dou, Xiaowen Wu, Wei Ji, Xiang Wang, Mengmeng Zong, Zengyan Mo, Hongmei Zhang, Xiuming Xiong, Dan Development of a novel prognostic signature for predicting the overall survival of bladder cancer patients |
title | Development of a novel prognostic signature for predicting the overall survival of bladder cancer patients |
title_full | Development of a novel prognostic signature for predicting the overall survival of bladder cancer patients |
title_fullStr | Development of a novel prognostic signature for predicting the overall survival of bladder cancer patients |
title_full_unstemmed | Development of a novel prognostic signature for predicting the overall survival of bladder cancer patients |
title_short | Development of a novel prognostic signature for predicting the overall survival of bladder cancer patients |
title_sort | development of a novel prognostic signature for predicting the overall survival of bladder cancer patients |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7286875/ https://www.ncbi.nlm.nih.gov/pubmed/32441304 http://dx.doi.org/10.1042/BSR20194432 |
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