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A novel immune-related gene pair prognostic signature for predicting overall survival in bladder cancer
BACKGROUND: Bladder cancer (BC) is the ninth most common malignant tumor. We constructed a risk signature using immune-related gene pairs (IRGPs) to predict the prognosis of BC patients. METHODS: The mRNA transcriptome, simple nucleotide variation and clinical data of BC patients were downloaded fro...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8281685/ https://www.ncbi.nlm.nih.gov/pubmed/34266411 http://dx.doi.org/10.1186/s12885-021-08486-0 |
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author | Fu, Yang Sun, Shanshan Bi, Jianbin Kong, Chuize Yin, Lei |
author_facet | Fu, Yang Sun, Shanshan Bi, Jianbin Kong, Chuize Yin, Lei |
author_sort | Fu, Yang |
collection | PubMed |
description | BACKGROUND: Bladder cancer (BC) is the ninth most common malignant tumor. We constructed a risk signature using immune-related gene pairs (IRGPs) to predict the prognosis of BC patients. METHODS: The mRNA transcriptome, simple nucleotide variation and clinical data of BC patients were downloaded from The Cancer Genome Atlas (TCGA) database (TCGA-BLCA). The mRNA transcriptome and clinical data were also extracted from Gene Expression Omnibus (GEO) datasets (GSE31684). A risk signature was built based on the IRGPs. The ability of the signature to predict prognosis was analyzed with survival curves and Cox regression. The relationships between immunological parameters [immune cell infiltration, immune checkpoints, tumor microenvironment (TME) and tumor mutation burden (TMB)] and the risk score were investigated. Finally, gene set enrichment analysis (GSEA) was used to explore molecular mechanisms underlying the risk score. RESULTS: The risk signature utilized 30 selected IRGPs. The prognosis of the high-risk group was significantly worse than that of the low-risk group. We used the GSE31684 dataset to validate the signature. Close relationships were found between the risk score and immunological parameters. Finally, GSEA showed that gene sets related to the extracellular matrix (ECM), stromal cells and epithelial-mesenchymal transition (EMT) were enriched in the high-risk group. In the low-risk group, we found a number of immune-related pathways in the enriched pathways and biofunctions. CONCLUSIONS: We used a new tool, IRGPs, to build a risk signature to predict the prognosis of BC. By evaluating immune parameters and molecular mechanisms, we gained a better understanding of the mechanisms underlying the risk signature. This signature can also be used as a tool to predict the effect of immunotherapy in patients with BC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08486-0. |
format | Online Article Text |
id | pubmed-8281685 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82816852021-07-16 A novel immune-related gene pair prognostic signature for predicting overall survival in bladder cancer Fu, Yang Sun, Shanshan Bi, Jianbin Kong, Chuize Yin, Lei BMC Cancer Article BACKGROUND: Bladder cancer (BC) is the ninth most common malignant tumor. We constructed a risk signature using immune-related gene pairs (IRGPs) to predict the prognosis of BC patients. METHODS: The mRNA transcriptome, simple nucleotide variation and clinical data of BC patients were downloaded from The Cancer Genome Atlas (TCGA) database (TCGA-BLCA). The mRNA transcriptome and clinical data were also extracted from Gene Expression Omnibus (GEO) datasets (GSE31684). A risk signature was built based on the IRGPs. The ability of the signature to predict prognosis was analyzed with survival curves and Cox regression. The relationships between immunological parameters [immune cell infiltration, immune checkpoints, tumor microenvironment (TME) and tumor mutation burden (TMB)] and the risk score were investigated. Finally, gene set enrichment analysis (GSEA) was used to explore molecular mechanisms underlying the risk score. RESULTS: The risk signature utilized 30 selected IRGPs. The prognosis of the high-risk group was significantly worse than that of the low-risk group. We used the GSE31684 dataset to validate the signature. Close relationships were found between the risk score and immunological parameters. Finally, GSEA showed that gene sets related to the extracellular matrix (ECM), stromal cells and epithelial-mesenchymal transition (EMT) were enriched in the high-risk group. In the low-risk group, we found a number of immune-related pathways in the enriched pathways and biofunctions. CONCLUSIONS: We used a new tool, IRGPs, to build a risk signature to predict the prognosis of BC. By evaluating immune parameters and molecular mechanisms, we gained a better understanding of the mechanisms underlying the risk signature. This signature can also be used as a tool to predict the effect of immunotherapy in patients with BC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08486-0. BioMed Central 2021-07-15 /pmc/articles/PMC8281685/ /pubmed/34266411 http://dx.doi.org/10.1186/s12885-021-08486-0 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 | Article Fu, Yang Sun, Shanshan Bi, Jianbin Kong, Chuize Yin, Lei A novel immune-related gene pair prognostic signature for predicting overall survival in bladder cancer |
title | A novel immune-related gene pair prognostic signature for predicting overall survival in bladder cancer |
title_full | A novel immune-related gene pair prognostic signature for predicting overall survival in bladder cancer |
title_fullStr | A novel immune-related gene pair prognostic signature for predicting overall survival in bladder cancer |
title_full_unstemmed | A novel immune-related gene pair prognostic signature for predicting overall survival in bladder cancer |
title_short | A novel immune-related gene pair prognostic signature for predicting overall survival in bladder cancer |
title_sort | novel immune-related gene pair prognostic signature for predicting overall survival in bladder cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8281685/ https://www.ncbi.nlm.nih.gov/pubmed/34266411 http://dx.doi.org/10.1186/s12885-021-08486-0 |
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