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An Individualized Prognostic Signature for Clinically Predicting the Survival of Patients With Bladder Cancer
Background: The tumor immune microenvironment (TIME) plays an important role in the development and prognosis of bladder cancer. It is essential to conduct a risk model to explore the prognostic value of the immunologic genes and establish an individualized prognostic signature for predicting the su...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002098/ https://www.ncbi.nlm.nih.gov/pubmed/35422849 http://dx.doi.org/10.3389/fgene.2022.837301 |
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author | Liu, Qing Wang, Yunchao Gao, Huayu Sun, Fahai Wang, Xuan Zhang, Huawei Wang, Jianning |
author_facet | Liu, Qing Wang, Yunchao Gao, Huayu Sun, Fahai Wang, Xuan Zhang, Huawei Wang, Jianning |
author_sort | Liu, Qing |
collection | PubMed |
description | Background: The tumor immune microenvironment (TIME) plays an important role in the development and prognosis of bladder cancer. It is essential to conduct a risk model to explore the prognostic value of the immunologic genes and establish an individualized prognostic signature for predicting the survival of patients with bladder cancer. Method: The differentially expressed immunologic genes (DEGs) are identified in The Cancer Genome Atlas (TCGA). The nonnegative matrix factorization (NMF) was used to stratify the DEGs in TCGA. We used the least absolute shrinkage and selection operator (LASSO) Cox regression and univariate Cox analysis to establish a prognostic risk model. A nomogram was used to establish an individualized prognostic signature for predicting survival. The potential pathways underlying the model were explored. Results: A total of 1,018 DEGs were screened. All samples were divided into two clusters (C1 and C2) by NMF with different immune cell infiltration, and the C2 subtype had poor prognosis. We constructed a 15-gene prognostic risk model from TCGA cohort. The patients from the high-risk group had a poor overall survival rate compared with the low-risk group. Time-dependent ROC curves demonstrated good predictive ability of the signature (0.827, 0.802, and 0.812 for 1-, 3-, and 5-year survival, respectively). Univariate and multivariate Cox regression analyses showed that the immunologic prognostic risk model was an independent factor. The decision curve demonstrated a relatively good performance of the risk model and individualized prognostic signature, showing the best net benefit for 1-, 3-, and 5-year OS. Gene aggregation analysis showed that the high-risk group was mainly concentrated in tumorigenesis and migration and immune signaling pathways. Conclusion: We established a risk model and an individualized prognostic signature, and these may be useful biomarkers for prognostic prediction of patients with bladder cancer. |
format | Online Article Text |
id | pubmed-9002098 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90020982022-04-13 An Individualized Prognostic Signature for Clinically Predicting the Survival of Patients With Bladder Cancer Liu, Qing Wang, Yunchao Gao, Huayu Sun, Fahai Wang, Xuan Zhang, Huawei Wang, Jianning Front Genet Genetics Background: The tumor immune microenvironment (TIME) plays an important role in the development and prognosis of bladder cancer. It is essential to conduct a risk model to explore the prognostic value of the immunologic genes and establish an individualized prognostic signature for predicting the survival of patients with bladder cancer. Method: The differentially expressed immunologic genes (DEGs) are identified in The Cancer Genome Atlas (TCGA). The nonnegative matrix factorization (NMF) was used to stratify the DEGs in TCGA. We used the least absolute shrinkage and selection operator (LASSO) Cox regression and univariate Cox analysis to establish a prognostic risk model. A nomogram was used to establish an individualized prognostic signature for predicting survival. The potential pathways underlying the model were explored. Results: A total of 1,018 DEGs were screened. All samples were divided into two clusters (C1 and C2) by NMF with different immune cell infiltration, and the C2 subtype had poor prognosis. We constructed a 15-gene prognostic risk model from TCGA cohort. The patients from the high-risk group had a poor overall survival rate compared with the low-risk group. Time-dependent ROC curves demonstrated good predictive ability of the signature (0.827, 0.802, and 0.812 for 1-, 3-, and 5-year survival, respectively). Univariate and multivariate Cox regression analyses showed that the immunologic prognostic risk model was an independent factor. The decision curve demonstrated a relatively good performance of the risk model and individualized prognostic signature, showing the best net benefit for 1-, 3-, and 5-year OS. Gene aggregation analysis showed that the high-risk group was mainly concentrated in tumorigenesis and migration and immune signaling pathways. Conclusion: We established a risk model and an individualized prognostic signature, and these may be useful biomarkers for prognostic prediction of patients with bladder cancer. Frontiers Media S.A. 2022-03-29 /pmc/articles/PMC9002098/ /pubmed/35422849 http://dx.doi.org/10.3389/fgene.2022.837301 Text en Copyright © 2022 Liu, Wang, Gao, Sun, Wang, Zhang and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Liu, Qing Wang, Yunchao Gao, Huayu Sun, Fahai Wang, Xuan Zhang, Huawei Wang, Jianning An Individualized Prognostic Signature for Clinically Predicting the Survival of Patients With Bladder Cancer |
title | An Individualized Prognostic Signature for Clinically Predicting the Survival of Patients With Bladder Cancer |
title_full | An Individualized Prognostic Signature for Clinically Predicting the Survival of Patients With Bladder Cancer |
title_fullStr | An Individualized Prognostic Signature for Clinically Predicting the Survival of Patients With Bladder Cancer |
title_full_unstemmed | An Individualized Prognostic Signature for Clinically Predicting the Survival of Patients With Bladder Cancer |
title_short | An Individualized Prognostic Signature for Clinically Predicting the Survival of Patients With Bladder Cancer |
title_sort | individualized prognostic signature for clinically predicting the survival of patients with bladder cancer |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002098/ https://www.ncbi.nlm.nih.gov/pubmed/35422849 http://dx.doi.org/10.3389/fgene.2022.837301 |
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