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Identification of a novel metabolism-related gene signature associated with the survival of bladder cancer

BACKGROUND: Bladder cancer (BC) is one of the most common malignancies and has a relatively poor outcome worldwide. In this study, we attempted to construct a novel metabolism-related gene (MRG) signature for predicting the survival probability of BC patients. METHODS: First, differentially expresse...

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Autores principales: Li, Xiaotao, Fu, Shi, Huang, Yinglong, Luan, Ting, Wang, Haifeng, Wang, Jiansong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8611960/
https://www.ncbi.nlm.nih.gov/pubmed/34819038
http://dx.doi.org/10.1186/s12885-021-09006-w
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author Li, Xiaotao
Fu, Shi
Huang, Yinglong
Luan, Ting
Wang, Haifeng
Wang, Jiansong
author_facet Li, Xiaotao
Fu, Shi
Huang, Yinglong
Luan, Ting
Wang, Haifeng
Wang, Jiansong
author_sort Li, Xiaotao
collection PubMed
description BACKGROUND: Bladder cancer (BC) is one of the most common malignancies and has a relatively poor outcome worldwide. In this study, we attempted to construct a novel metabolism-related gene (MRG) signature for predicting the survival probability of BC patients. METHODS: First, differentially expressed MRGs between BC and normal samples were identified and used to construct a protein-protein interaction (PPI) network and perform mutation analysis. Next, univariate Cox regression analysis was utilized to select prognostic genes, and multivariate Cox regression analysis was applied to establish an MRG signature for predicting the survival probability of BC patients. Moreover, Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) analysis were performed to evaluate the predictive capability of the MRG signature. Finally, a nomogram based on the MRG signature was established to better predict the survival of BC. RESULTS: In the present study, 27 differentially expressed MRGs were identified, most of which presented mutations in BC patients, and LRP1 showed the highest mutation rate. Next, an MRG signature, including MAOB, FASN and LRP1, was established by using univariate and multivariate Cox regression analysis. Furthermore, survival analysis indicated that BC patients in the high-risk group had a dramatically lower survival probability than those in the low-risk group. Finally, Cox regression analysis showed that the risk score was an independent prognostic factor, and a nomogram integrating age, pathological tumor stage and risk score was established and presented good predictive ability. CONCLUSION: We successfully constructed a novel MRG signature to predict the prognosis of BC patients, which might contribute to the clinical treatment of BC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-09006-w.
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spelling pubmed-86119602021-11-29 Identification of a novel metabolism-related gene signature associated with the survival of bladder cancer Li, Xiaotao Fu, Shi Huang, Yinglong Luan, Ting Wang, Haifeng Wang, Jiansong BMC Cancer Research BACKGROUND: Bladder cancer (BC) is one of the most common malignancies and has a relatively poor outcome worldwide. In this study, we attempted to construct a novel metabolism-related gene (MRG) signature for predicting the survival probability of BC patients. METHODS: First, differentially expressed MRGs between BC and normal samples were identified and used to construct a protein-protein interaction (PPI) network and perform mutation analysis. Next, univariate Cox regression analysis was utilized to select prognostic genes, and multivariate Cox regression analysis was applied to establish an MRG signature for predicting the survival probability of BC patients. Moreover, Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) analysis were performed to evaluate the predictive capability of the MRG signature. Finally, a nomogram based on the MRG signature was established to better predict the survival of BC. RESULTS: In the present study, 27 differentially expressed MRGs were identified, most of which presented mutations in BC patients, and LRP1 showed the highest mutation rate. Next, an MRG signature, including MAOB, FASN and LRP1, was established by using univariate and multivariate Cox regression analysis. Furthermore, survival analysis indicated that BC patients in the high-risk group had a dramatically lower survival probability than those in the low-risk group. Finally, Cox regression analysis showed that the risk score was an independent prognostic factor, and a nomogram integrating age, pathological tumor stage and risk score was established and presented good predictive ability. CONCLUSION: We successfully constructed a novel MRG signature to predict the prognosis of BC patients, which might contribute to the clinical treatment of BC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-09006-w. BioMed Central 2021-11-24 /pmc/articles/PMC8611960/ /pubmed/34819038 http://dx.doi.org/10.1186/s12885-021-09006-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Research
Li, Xiaotao
Fu, Shi
Huang, Yinglong
Luan, Ting
Wang, Haifeng
Wang, Jiansong
Identification of a novel metabolism-related gene signature associated with the survival of bladder cancer
title Identification of a novel metabolism-related gene signature associated with the survival of bladder cancer
title_full Identification of a novel metabolism-related gene signature associated with the survival of bladder cancer
title_fullStr Identification of a novel metabolism-related gene signature associated with the survival of bladder cancer
title_full_unstemmed Identification of a novel metabolism-related gene signature associated with the survival of bladder cancer
title_short Identification of a novel metabolism-related gene signature associated with the survival of bladder cancer
title_sort identification of a novel metabolism-related gene signature associated with the survival of bladder cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8611960/
https://www.ncbi.nlm.nih.gov/pubmed/34819038
http://dx.doi.org/10.1186/s12885-021-09006-w
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