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In silico analysis of the immune microenvironment in bladder cancer
BACKGROUND: Infiltrating immune and stromal cells are vital components of the bladder cancer (BC) microenvironment, which can significantly affect BC progression and outcome. However, the contribution of each subset of tumour-infiltrating immune cells is unclear. The objective of this study was to p...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7106767/ https://www.ncbi.nlm.nih.gov/pubmed/32228629 http://dx.doi.org/10.1186/s12885-020-06740-5 |
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author | Zhang, Ye Ou, De-hua Zhuang, Dong-wu Zheng, Ze-feng Lin, Ming-en |
author_facet | Zhang, Ye Ou, De-hua Zhuang, Dong-wu Zheng, Ze-feng Lin, Ming-en |
author_sort | Zhang, Ye |
collection | PubMed |
description | BACKGROUND: Infiltrating immune and stromal cells are vital components of the bladder cancer (BC) microenvironment, which can significantly affect BC progression and outcome. However, the contribution of each subset of tumour-infiltrating immune cells is unclear. The objective of this study was to perform cell phenotyping and transcriptional profiling of the tumour immune microenvironment and analyse the association of distinct cell subsets and genes with BC prognosis. METHODS: Clinical data of 412 patients with BC and 433 transcription files for normal and cancer tissues were downloaded from The Cancer Genome Atlas. The CIBERSORT algorithm was used to determine the relative abundance of 22 immune cell types in each sample and the ESTIMATE algorithm was used to identify differentially expressed genes within the tumour microenvironment of BC, which were subjected to functional enrichment and protein-protein interaction (PPI) analyses. The association of cell subsets and differentially expressed genes with patient survival and clinical parameters was examined by Cox regression analysis and the Kaplan-Meier method. RESULTS: Resting natural killer cells and activated memory CD4(+) and CD8(+) T cells were associated with favourable patient outcome, whereas resting memory CD4(+) T cells were associated with poor outcome. Differential expression analysis revealed 1334 genes influencing both immune and stromal cell scores; of them, 97 were predictive of overall survival in patients with BC. Among the top 10 statistically significant hub genes in the PPI network, CXCL12, FN1, LCK, and CXCR4 were found to be associated with BC prognosis. CONCLUSION: Tumour-infiltrating immune cells and cancer microenvironment-related genes can affect the outcomes of patients and are likely to be important determinants of both prognosis and response to immunotherapy in BC. |
format | Online Article Text |
id | pubmed-7106767 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-71067672020-04-01 In silico analysis of the immune microenvironment in bladder cancer Zhang, Ye Ou, De-hua Zhuang, Dong-wu Zheng, Ze-feng Lin, Ming-en BMC Cancer Research Article BACKGROUND: Infiltrating immune and stromal cells are vital components of the bladder cancer (BC) microenvironment, which can significantly affect BC progression and outcome. However, the contribution of each subset of tumour-infiltrating immune cells is unclear. The objective of this study was to perform cell phenotyping and transcriptional profiling of the tumour immune microenvironment and analyse the association of distinct cell subsets and genes with BC prognosis. METHODS: Clinical data of 412 patients with BC and 433 transcription files for normal and cancer tissues were downloaded from The Cancer Genome Atlas. The CIBERSORT algorithm was used to determine the relative abundance of 22 immune cell types in each sample and the ESTIMATE algorithm was used to identify differentially expressed genes within the tumour microenvironment of BC, which were subjected to functional enrichment and protein-protein interaction (PPI) analyses. The association of cell subsets and differentially expressed genes with patient survival and clinical parameters was examined by Cox regression analysis and the Kaplan-Meier method. RESULTS: Resting natural killer cells and activated memory CD4(+) and CD8(+) T cells were associated with favourable patient outcome, whereas resting memory CD4(+) T cells were associated with poor outcome. Differential expression analysis revealed 1334 genes influencing both immune and stromal cell scores; of them, 97 were predictive of overall survival in patients with BC. Among the top 10 statistically significant hub genes in the PPI network, CXCL12, FN1, LCK, and CXCR4 were found to be associated with BC prognosis. CONCLUSION: Tumour-infiltrating immune cells and cancer microenvironment-related genes can affect the outcomes of patients and are likely to be important determinants of both prognosis and response to immunotherapy in BC. BioMed Central 2020-03-30 /pmc/articles/PMC7106767/ /pubmed/32228629 http://dx.doi.org/10.1186/s12885-020-06740-5 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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 Article Zhang, Ye Ou, De-hua Zhuang, Dong-wu Zheng, Ze-feng Lin, Ming-en In silico analysis of the immune microenvironment in bladder cancer |
title | In silico analysis of the immune microenvironment in bladder cancer |
title_full | In silico analysis of the immune microenvironment in bladder cancer |
title_fullStr | In silico analysis of the immune microenvironment in bladder cancer |
title_full_unstemmed | In silico analysis of the immune microenvironment in bladder cancer |
title_short | In silico analysis of the immune microenvironment in bladder cancer |
title_sort | in silico analysis of the immune microenvironment in bladder cancer |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7106767/ https://www.ncbi.nlm.nih.gov/pubmed/32228629 http://dx.doi.org/10.1186/s12885-020-06740-5 |
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