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Identification and validation of an 18-gene signature highly-predictive of bladder cancer metastasis
We found two deviant groups that were unpredictable with clinical models predicting bladder cancer metastasis. The group G consists of patients at high risk of pN+ , but they have pN0. The group P consists of patients at low risk of pN+ , but they have pN+ . We aimed to determine the genetic differe...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5762631/ https://www.ncbi.nlm.nih.gov/pubmed/29321541 http://dx.doi.org/10.1038/s41598-017-18773-1 |
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author | Wang, Beihe Wan, Fangning Sheng, Haoyue Zhu, Yiping Shi, Guohai Zhang, Hailiang Dai, Bo Shen, Yijun Zhu, Yao Ye, Dingwei |
author_facet | Wang, Beihe Wan, Fangning Sheng, Haoyue Zhu, Yiping Shi, Guohai Zhang, Hailiang Dai, Bo Shen, Yijun Zhu, Yao Ye, Dingwei |
author_sort | Wang, Beihe |
collection | PubMed |
description | We found two deviant groups that were unpredictable with clinical models predicting bladder cancer metastasis. The group G consists of patients at high risk of pN+ , but they have pN0. The group P consists of patients at low risk of pN+ , but they have pN+ . We aimed to determine the genetic differences between these two groups. 1603 patients from SEER database were enrolled to build a multivariate model. This model was applied to patients from the TCGA database to distinguish groups G and P. Differentially expressed genes between the two groups were identified. RT-qPCR was used to validate the results in a cohort from FUSCC. Two deviant groups were identified both in the SEER population and the TCGA population. Expression of 183 genes was significantly different between the two groups. 18 genes achieved significant statistical power in predicting lymph node metastasis excluding these two deviant groups. The 18-gene signature outperformed 3 other bladder cancer lymph node prediction tools in 2 external GEO datasets. RT-qPCR results of our own cohort identified NECTIN2 (P = 0.036) as the only gene that could predict metastasis. Our study showed a novel gene screening method and proposed an 18-gene signature highly predictive of bladder cancer metastasis. |
format | Online Article Text |
id | pubmed-5762631 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-57626312018-01-17 Identification and validation of an 18-gene signature highly-predictive of bladder cancer metastasis Wang, Beihe Wan, Fangning Sheng, Haoyue Zhu, Yiping Shi, Guohai Zhang, Hailiang Dai, Bo Shen, Yijun Zhu, Yao Ye, Dingwei Sci Rep Article We found two deviant groups that were unpredictable with clinical models predicting bladder cancer metastasis. The group G consists of patients at high risk of pN+ , but they have pN0. The group P consists of patients at low risk of pN+ , but they have pN+ . We aimed to determine the genetic differences between these two groups. 1603 patients from SEER database were enrolled to build a multivariate model. This model was applied to patients from the TCGA database to distinguish groups G and P. Differentially expressed genes between the two groups were identified. RT-qPCR was used to validate the results in a cohort from FUSCC. Two deviant groups were identified both in the SEER population and the TCGA population. Expression of 183 genes was significantly different between the two groups. 18 genes achieved significant statistical power in predicting lymph node metastasis excluding these two deviant groups. The 18-gene signature outperformed 3 other bladder cancer lymph node prediction tools in 2 external GEO datasets. RT-qPCR results of our own cohort identified NECTIN2 (P = 0.036) as the only gene that could predict metastasis. Our study showed a novel gene screening method and proposed an 18-gene signature highly predictive of bladder cancer metastasis. Nature Publishing Group UK 2018-01-10 /pmc/articles/PMC5762631/ /pubmed/29321541 http://dx.doi.org/10.1038/s41598-017-18773-1 Text en © The Author(s) 2017 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Wang, Beihe Wan, Fangning Sheng, Haoyue Zhu, Yiping Shi, Guohai Zhang, Hailiang Dai, Bo Shen, Yijun Zhu, Yao Ye, Dingwei Identification and validation of an 18-gene signature highly-predictive of bladder cancer metastasis |
title | Identification and validation of an 18-gene signature highly-predictive of bladder cancer metastasis |
title_full | Identification and validation of an 18-gene signature highly-predictive of bladder cancer metastasis |
title_fullStr | Identification and validation of an 18-gene signature highly-predictive of bladder cancer metastasis |
title_full_unstemmed | Identification and validation of an 18-gene signature highly-predictive of bladder cancer metastasis |
title_short | Identification and validation of an 18-gene signature highly-predictive of bladder cancer metastasis |
title_sort | identification and validation of an 18-gene signature highly-predictive of bladder cancer metastasis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5762631/ https://www.ncbi.nlm.nih.gov/pubmed/29321541 http://dx.doi.org/10.1038/s41598-017-18773-1 |
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