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A novel survival model based on a Ferroptosis-related gene signature for predicting overall survival in bladder cancer

BACKGROUND: The effective treatment and prognosis prediction of bladder cancer (BLCA) remains a medical problem. Ferroptosis is an iron-dependent form of programmed cell death. Ferroptosis is closely related to tumour occurrence and progression, but the prognostic value of ferroptosis-related genes...

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Autores principales: Liang, Yingchun, Ye, Fangdie, Xu, Chenyang, Zou, Lujia, Hu, Yun, Hu, Jimeng, Jiang, Haowen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8380338/
https://www.ncbi.nlm.nih.gov/pubmed/34418989
http://dx.doi.org/10.1186/s12885-021-08687-7
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author Liang, Yingchun
Ye, Fangdie
Xu, Chenyang
Zou, Lujia
Hu, Yun
Hu, Jimeng
Jiang, Haowen
author_facet Liang, Yingchun
Ye, Fangdie
Xu, Chenyang
Zou, Lujia
Hu, Yun
Hu, Jimeng
Jiang, Haowen
author_sort Liang, Yingchun
collection PubMed
description BACKGROUND: The effective treatment and prognosis prediction of bladder cancer (BLCA) remains a medical problem. Ferroptosis is an iron-dependent form of programmed cell death. Ferroptosis is closely related to tumour occurrence and progression, but the prognostic value of ferroptosis-related genes (FRGs) in BLCA remains to be further clarified. In this study, we identified an FRG signature with potential prognostic value for patients with BLCA. METHODS: The corresponding clinical data and mRNA expression profiles of BLCA patients were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to extract FRGs related to survival time, and a Cox regression model was used to construct a multigene signature. Both principal component analysis (PCA) and single-sample gene set enrichment analysis (ssGSEA) were performed for functional annotation. RESULTS: Clinical traits were combined with FRGs, and 15 prognosis-related FRGs were identified by Cox regression. High expression of CISD1, GCLM, CRYAB, SLC7A11, TFRC, ACACA, ZEB1, SQLE, FADS2, ABCC1, G6PD and PGD was related to poor survival in BLCA patients. Multivariate Cox regression was used to construct a prognostic model with 7 FRGs that divided patients into two risk groups. Compared with that in the low-risk group, the overall survival (OS) of patients in the high-risk group was significantly lower (P < 0.001). In multivariate regression analysis, the risk score was shown to be an independent predictor of OS (HR = 1.772, P < 0.01). Receiver operating characteristic (ROC) curve analysis verified the predictive ability of the model. In addition, the two risk groups displayed different immune statuses in ssGSEA and different distributed patterns in PCA. CONCLUSION: Our research suggests that a new gene model related to ferroptosis can be applied for the prognosis prediction of BLCA. Targeting FRGs may be a treatment option for BLCA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08687-7.
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spelling pubmed-83803382021-08-23 A novel survival model based on a Ferroptosis-related gene signature for predicting overall survival in bladder cancer Liang, Yingchun Ye, Fangdie Xu, Chenyang Zou, Lujia Hu, Yun Hu, Jimeng Jiang, Haowen BMC Cancer Research BACKGROUND: The effective treatment and prognosis prediction of bladder cancer (BLCA) remains a medical problem. Ferroptosis is an iron-dependent form of programmed cell death. Ferroptosis is closely related to tumour occurrence and progression, but the prognostic value of ferroptosis-related genes (FRGs) in BLCA remains to be further clarified. In this study, we identified an FRG signature with potential prognostic value for patients with BLCA. METHODS: The corresponding clinical data and mRNA expression profiles of BLCA patients were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to extract FRGs related to survival time, and a Cox regression model was used to construct a multigene signature. Both principal component analysis (PCA) and single-sample gene set enrichment analysis (ssGSEA) were performed for functional annotation. RESULTS: Clinical traits were combined with FRGs, and 15 prognosis-related FRGs were identified by Cox regression. High expression of CISD1, GCLM, CRYAB, SLC7A11, TFRC, ACACA, ZEB1, SQLE, FADS2, ABCC1, G6PD and PGD was related to poor survival in BLCA patients. Multivariate Cox regression was used to construct a prognostic model with 7 FRGs that divided patients into two risk groups. Compared with that in the low-risk group, the overall survival (OS) of patients in the high-risk group was significantly lower (P < 0.001). In multivariate regression analysis, the risk score was shown to be an independent predictor of OS (HR = 1.772, P < 0.01). Receiver operating characteristic (ROC) curve analysis verified the predictive ability of the model. In addition, the two risk groups displayed different immune statuses in ssGSEA and different distributed patterns in PCA. CONCLUSION: Our research suggests that a new gene model related to ferroptosis can be applied for the prognosis prediction of BLCA. Targeting FRGs may be a treatment option for BLCA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08687-7. BioMed Central 2021-08-21 /pmc/articles/PMC8380338/ /pubmed/34418989 http://dx.doi.org/10.1186/s12885-021-08687-7 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
Liang, Yingchun
Ye, Fangdie
Xu, Chenyang
Zou, Lujia
Hu, Yun
Hu, Jimeng
Jiang, Haowen
A novel survival model based on a Ferroptosis-related gene signature for predicting overall survival in bladder cancer
title A novel survival model based on a Ferroptosis-related gene signature for predicting overall survival in bladder cancer
title_full A novel survival model based on a Ferroptosis-related gene signature for predicting overall survival in bladder cancer
title_fullStr A novel survival model based on a Ferroptosis-related gene signature for predicting overall survival in bladder cancer
title_full_unstemmed A novel survival model based on a Ferroptosis-related gene signature for predicting overall survival in bladder cancer
title_short A novel survival model based on a Ferroptosis-related gene signature for predicting overall survival in bladder cancer
title_sort novel survival model based on a ferroptosis-related gene signature for predicting overall survival in bladder cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8380338/
https://www.ncbi.nlm.nih.gov/pubmed/34418989
http://dx.doi.org/10.1186/s12885-021-08687-7
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