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Characterization of the Immune Cell Infiltration Landscape in Esophageal Squamous Cell Carcinoma
BACKGROUND: Immunotherapy has achieved remarkable efficacy in treating oesophageal squamous cell carcinoma (ESCC). However, this treatment has limited efficacy in some patients. An increasing number of evidence suggested that immune cells within the tumour microenvironment (TME) are strongly related...
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/PMC9300817/ https://www.ncbi.nlm.nih.gov/pubmed/35875070 http://dx.doi.org/10.3389/fonc.2022.879326 |
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author | Sui, Zhilin Wu, Xianxian Du, Longde Wang, Han Yuan, Lijuan Zhang, Jian V. Yu, Zhentao |
author_facet | Sui, Zhilin Wu, Xianxian Du, Longde Wang, Han Yuan, Lijuan Zhang, Jian V. Yu, Zhentao |
author_sort | Sui, Zhilin |
collection | PubMed |
description | BACKGROUND: Immunotherapy has achieved remarkable efficacy in treating oesophageal squamous cell carcinoma (ESCC). However, this treatment has limited efficacy in some patients. An increasing number of evidence suggested that immune cells within the tumour microenvironment (TME) are strongly related to immunotherapy response and patient prognosis. Thus, the landscape of immune cell infiltration (ICI) in ESCC needs to be mapped. METHODS: In the study, the ICI pattern in 206 cases of ESCC was characterised by two algorithms, namely, CIBERSORT and single-sample gene set enrichment analysis (ssGSEA). The ICI score of each specimen was calculated by principal component analysis (PCA) according to ICI signature genes A (ICISGA) and B (ICISGB). The prognostic difference was evaluated by using the Kaplan–Meier method. The related pathways of ICI score were investigated by applying gene set enrichment analysis (GSEA). The R packages of ‘regplot’, ‘timeROC’ and ‘rms’ were applied for the construction of nomogram model. RESULT: Three TME subtypes were identified with no prognostic implication. A total of 333 differentially expressed genes (DEGs) among immune subtypes were determined, among which ICISGA and ICISGB were identified. Finally, ICI scores were constructed, and the patients were grouped into high or low ICI score group. Compared with the low ICI score group, the high ICI score group had better prognosis. GSEA revealed that the high ICI score group referred to multiple signalling pathways, including B cell receptor, Fc gamma R-mediated phagocytosis, NOD-like receptor and TGF-β signalling pathways. In addition, the nomogram model was constructed to evaluate 1-, 3- and 5-year probability of death in an ESCC patient. The ROC and calibration curves indicated that the model has a good discrimination ability. CONCLUSION: We depicted a comprehensive ICI landscape in ESCC. ICI score may be used as a predictor of survival rate, which may be helpful for guiding immunotherapy in the future. |
format | Online Article Text |
id | pubmed-9300817 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93008172022-07-22 Characterization of the Immune Cell Infiltration Landscape in Esophageal Squamous Cell Carcinoma Sui, Zhilin Wu, Xianxian Du, Longde Wang, Han Yuan, Lijuan Zhang, Jian V. Yu, Zhentao Front Oncol Oncology BACKGROUND: Immunotherapy has achieved remarkable efficacy in treating oesophageal squamous cell carcinoma (ESCC). However, this treatment has limited efficacy in some patients. An increasing number of evidence suggested that immune cells within the tumour microenvironment (TME) are strongly related to immunotherapy response and patient prognosis. Thus, the landscape of immune cell infiltration (ICI) in ESCC needs to be mapped. METHODS: In the study, the ICI pattern in 206 cases of ESCC was characterised by two algorithms, namely, CIBERSORT and single-sample gene set enrichment analysis (ssGSEA). The ICI score of each specimen was calculated by principal component analysis (PCA) according to ICI signature genes A (ICISGA) and B (ICISGB). The prognostic difference was evaluated by using the Kaplan–Meier method. The related pathways of ICI score were investigated by applying gene set enrichment analysis (GSEA). The R packages of ‘regplot’, ‘timeROC’ and ‘rms’ were applied for the construction of nomogram model. RESULT: Three TME subtypes were identified with no prognostic implication. A total of 333 differentially expressed genes (DEGs) among immune subtypes were determined, among which ICISGA and ICISGB were identified. Finally, ICI scores were constructed, and the patients were grouped into high or low ICI score group. Compared with the low ICI score group, the high ICI score group had better prognosis. GSEA revealed that the high ICI score group referred to multiple signalling pathways, including B cell receptor, Fc gamma R-mediated phagocytosis, NOD-like receptor and TGF-β signalling pathways. In addition, the nomogram model was constructed to evaluate 1-, 3- and 5-year probability of death in an ESCC patient. The ROC and calibration curves indicated that the model has a good discrimination ability. CONCLUSION: We depicted a comprehensive ICI landscape in ESCC. ICI score may be used as a predictor of survival rate, which may be helpful for guiding immunotherapy in the future. Frontiers Media S.A. 2022-07-07 /pmc/articles/PMC9300817/ /pubmed/35875070 http://dx.doi.org/10.3389/fonc.2022.879326 Text en Copyright © 2022 Sui, Wu, Du, Wang, Yuan, Zhang and Yu 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 | Oncology Sui, Zhilin Wu, Xianxian Du, Longde Wang, Han Yuan, Lijuan Zhang, Jian V. Yu, Zhentao Characterization of the Immune Cell Infiltration Landscape in Esophageal Squamous Cell Carcinoma |
title | Characterization of the Immune Cell Infiltration Landscape in Esophageal Squamous Cell Carcinoma |
title_full | Characterization of the Immune Cell Infiltration Landscape in Esophageal Squamous Cell Carcinoma |
title_fullStr | Characterization of the Immune Cell Infiltration Landscape in Esophageal Squamous Cell Carcinoma |
title_full_unstemmed | Characterization of the Immune Cell Infiltration Landscape in Esophageal Squamous Cell Carcinoma |
title_short | Characterization of the Immune Cell Infiltration Landscape in Esophageal Squamous Cell Carcinoma |
title_sort | characterization of the immune cell infiltration landscape in esophageal squamous cell carcinoma |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9300817/ https://www.ncbi.nlm.nih.gov/pubmed/35875070 http://dx.doi.org/10.3389/fonc.2022.879326 |
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