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Development of a prognostic index and screening of prognosis related genes based on an immunogenomic landscape analysis of bladder cancer

Background: Bladder cancer (BLCA) is one of the most common urinary tract malignant tumors. It is associated with poor outcomes, and its etiology and pathogenesis are not fully understood. There is great hope for immunotherapy in treating many malignant tumors; therefore, it is worthwhile to explore...

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Autores principales: Qu, GenYi, Liu, Zhengsheng, Yang, Guang, Xu, Yong, Xiang, Maolin, Tang, Cheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8109062/
https://www.ncbi.nlm.nih.gov/pubmed/33888644
http://dx.doi.org/10.18632/aging.202917
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author Qu, GenYi
Liu, Zhengsheng
Yang, Guang
Xu, Yong
Xiang, Maolin
Tang, Cheng
author_facet Qu, GenYi
Liu, Zhengsheng
Yang, Guang
Xu, Yong
Xiang, Maolin
Tang, Cheng
author_sort Qu, GenYi
collection PubMed
description Background: Bladder cancer (BLCA) is one of the most common urinary tract malignant tumors. It is associated with poor outcomes, and its etiology and pathogenesis are not fully understood. There is great hope for immunotherapy in treating many malignant tumors; therefore, it is worthwhile to explore the use of immunotherapy for BLCA.Methods: Gene expression profiles and clinical information were obtained from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were downloaded from the Immunology Database and Analysis Portal. Differentially-expressed and survival-associated IRGs in patients with BLCA were identified using computational algorithms and Cox regression analysis. We also performed functional enrichment analysis. Based on IRGs, we employed multivariate Cox analysis to develop a new prognostic index.Results: We identified 261 IRGs that were differentially expressed between BLCA tissue and adjacent tissue, 30 of which were significantly associated with the overall survival (all P<0.01). According to multivariate Cox analysis, nine survival-related IRGs (MMP9, PDGFRA, AHNAK, OAS1, OLR1, RAC3, IGF1, PGF, and SH3BP2) were high-risk genes. We developed a prognostic index based on these IRGs and found it accurately predicted BLCA outcomes associated with the TNM stage. Intriguingly, the IRG-based prognostic index reflected infiltration of macrophages.Conclusions: An independent IRG-based prognostic index provides a practical approach for assessing patients' immune status and prognosis with BLCA. This index independently predicted outcomes of BLCA.
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spelling pubmed-81090622021-05-12 Development of a prognostic index and screening of prognosis related genes based on an immunogenomic landscape analysis of bladder cancer Qu, GenYi Liu, Zhengsheng Yang, Guang Xu, Yong Xiang, Maolin Tang, Cheng Aging (Albany NY) Research Paper Background: Bladder cancer (BLCA) is one of the most common urinary tract malignant tumors. It is associated with poor outcomes, and its etiology and pathogenesis are not fully understood. There is great hope for immunotherapy in treating many malignant tumors; therefore, it is worthwhile to explore the use of immunotherapy for BLCA.Methods: Gene expression profiles and clinical information were obtained from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were downloaded from the Immunology Database and Analysis Portal. Differentially-expressed and survival-associated IRGs in patients with BLCA were identified using computational algorithms and Cox regression analysis. We also performed functional enrichment analysis. Based on IRGs, we employed multivariate Cox analysis to develop a new prognostic index.Results: We identified 261 IRGs that were differentially expressed between BLCA tissue and adjacent tissue, 30 of which were significantly associated with the overall survival (all P<0.01). According to multivariate Cox analysis, nine survival-related IRGs (MMP9, PDGFRA, AHNAK, OAS1, OLR1, RAC3, IGF1, PGF, and SH3BP2) were high-risk genes. We developed a prognostic index based on these IRGs and found it accurately predicted BLCA outcomes associated with the TNM stage. Intriguingly, the IRG-based prognostic index reflected infiltration of macrophages.Conclusions: An independent IRG-based prognostic index provides a practical approach for assessing patients' immune status and prognosis with BLCA. This index independently predicted outcomes of BLCA. Impact Journals 2021-04-22 /pmc/articles/PMC8109062/ /pubmed/33888644 http://dx.doi.org/10.18632/aging.202917 Text en Copyright: © 2021 Qu et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/) (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Paper
Qu, GenYi
Liu, Zhengsheng
Yang, Guang
Xu, Yong
Xiang, Maolin
Tang, Cheng
Development of a prognostic index and screening of prognosis related genes based on an immunogenomic landscape analysis of bladder cancer
title Development of a prognostic index and screening of prognosis related genes based on an immunogenomic landscape analysis of bladder cancer
title_full Development of a prognostic index and screening of prognosis related genes based on an immunogenomic landscape analysis of bladder cancer
title_fullStr Development of a prognostic index and screening of prognosis related genes based on an immunogenomic landscape analysis of bladder cancer
title_full_unstemmed Development of a prognostic index and screening of prognosis related genes based on an immunogenomic landscape analysis of bladder cancer
title_short Development of a prognostic index and screening of prognosis related genes based on an immunogenomic landscape analysis of bladder cancer
title_sort development of a prognostic index and screening of prognosis related genes based on an immunogenomic landscape analysis of bladder cancer
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8109062/
https://www.ncbi.nlm.nih.gov/pubmed/33888644
http://dx.doi.org/10.18632/aging.202917
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