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Global Characterization of Immune Infiltration in Clear Cell Renal Cell Carcinoma

BACKGROUND: Immunotherapy has revolutionized the treatment of clear cell renal cell carcinoma (ccRCC). However, the therapy is constrained by drug resistance. Therefore, further characterization of immune infiltration in ccRCC is needed to improve its efficacy. METHODS: Here, we adopted the CIBERSOR...

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Autores principales: Zheng, Yan, Wen, Yibo, Cao, Huixia, Gu, Yue, Yan, Lei, Wang, Yanliang, Wang, Limeng, Zhang, Lina, Shao, Fengmin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7997590/
https://www.ncbi.nlm.nih.gov/pubmed/33790572
http://dx.doi.org/10.2147/OTT.S282763
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author Zheng, Yan
Wen, Yibo
Cao, Huixia
Gu, Yue
Yan, Lei
Wang, Yanliang
Wang, Limeng
Zhang, Lina
Shao, Fengmin
author_facet Zheng, Yan
Wen, Yibo
Cao, Huixia
Gu, Yue
Yan, Lei
Wang, Yanliang
Wang, Limeng
Zhang, Lina
Shao, Fengmin
author_sort Zheng, Yan
collection PubMed
description BACKGROUND: Immunotherapy has revolutionized the treatment of clear cell renal cell carcinoma (ccRCC). However, the therapy is constrained by drug resistance. Therefore, further characterization of immune infiltration in ccRCC is needed to improve its efficacy. METHODS: Here, we adopted the CIBERSORT method to analyze the level of 22 immune cells, and analyzed the correlation of immune cells and clinical parameters in ccRCC in The Cancer Genome Atlas. We used consensus clustering to cluster ccRCC and identified differently expressed genes (DEGs) between hot and cold tumors using the “Limma” package, and then performed enrichment analysis of DEGs. Finally, we constructed and validated a Cox regression model using the “survival”, “glmnet”, and “survivalROC” packages, implemented in R. RESULTS: Regulatory T cells upregulated in tumor tissue increased during tumor progression, and correlated with poor overall survival in ccRCC. Consensus clustering identified four clusters of ccRCC. To elucidate the underlying mechanisms of immune cell infiltration, we subdivided these four clusters into two major types, immune hot and cold, and identified DEGs between them. The results revealed different transcription profiles in the two tumor types, with hot tumors being enriched in immune-related signaling, whereas cold tumors were enriched in extracellular matrix remodeling and the phosphatidylinositol 3-kinase–AKT (PI3K/AKT) pathway. We further identified hub genes and prognostic-related genes from the DEGs, and constructed a Cox regression model for predicting the overall survival of patients with ccRCC. The areas under the receiver operating characteristics curve for the risk model for the training, testing, and external Zhengzhou validation cohorts were 0.834, 0.733, and 0.812, respectively. Notably, gene sets in the prediction model could also predict the overall survival of patients receiving immunotherapy. CONCLUSION: These findings provide a comprehensive characterization of immune infiltration in ccRCC, while the constructed model can be used effectively to predict the overall survival of ccRCC patients.
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spelling pubmed-79975902021-03-30 Global Characterization of Immune Infiltration in Clear Cell Renal Cell Carcinoma Zheng, Yan Wen, Yibo Cao, Huixia Gu, Yue Yan, Lei Wang, Yanliang Wang, Limeng Zhang, Lina Shao, Fengmin Onco Targets Ther Original Research BACKGROUND: Immunotherapy has revolutionized the treatment of clear cell renal cell carcinoma (ccRCC). However, the therapy is constrained by drug resistance. Therefore, further characterization of immune infiltration in ccRCC is needed to improve its efficacy. METHODS: Here, we adopted the CIBERSORT method to analyze the level of 22 immune cells, and analyzed the correlation of immune cells and clinical parameters in ccRCC in The Cancer Genome Atlas. We used consensus clustering to cluster ccRCC and identified differently expressed genes (DEGs) between hot and cold tumors using the “Limma” package, and then performed enrichment analysis of DEGs. Finally, we constructed and validated a Cox regression model using the “survival”, “glmnet”, and “survivalROC” packages, implemented in R. RESULTS: Regulatory T cells upregulated in tumor tissue increased during tumor progression, and correlated with poor overall survival in ccRCC. Consensus clustering identified four clusters of ccRCC. To elucidate the underlying mechanisms of immune cell infiltration, we subdivided these four clusters into two major types, immune hot and cold, and identified DEGs between them. The results revealed different transcription profiles in the two tumor types, with hot tumors being enriched in immune-related signaling, whereas cold tumors were enriched in extracellular matrix remodeling and the phosphatidylinositol 3-kinase–AKT (PI3K/AKT) pathway. We further identified hub genes and prognostic-related genes from the DEGs, and constructed a Cox regression model for predicting the overall survival of patients with ccRCC. The areas under the receiver operating characteristics curve for the risk model for the training, testing, and external Zhengzhou validation cohorts were 0.834, 0.733, and 0.812, respectively. Notably, gene sets in the prediction model could also predict the overall survival of patients receiving immunotherapy. CONCLUSION: These findings provide a comprehensive characterization of immune infiltration in ccRCC, while the constructed model can be used effectively to predict the overall survival of ccRCC patients. Dove 2021-03-22 /pmc/articles/PMC7997590/ /pubmed/33790572 http://dx.doi.org/10.2147/OTT.S282763 Text en © 2021 Zheng et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Zheng, Yan
Wen, Yibo
Cao, Huixia
Gu, Yue
Yan, Lei
Wang, Yanliang
Wang, Limeng
Zhang, Lina
Shao, Fengmin
Global Characterization of Immune Infiltration in Clear Cell Renal Cell Carcinoma
title Global Characterization of Immune Infiltration in Clear Cell Renal Cell Carcinoma
title_full Global Characterization of Immune Infiltration in Clear Cell Renal Cell Carcinoma
title_fullStr Global Characterization of Immune Infiltration in Clear Cell Renal Cell Carcinoma
title_full_unstemmed Global Characterization of Immune Infiltration in Clear Cell Renal Cell Carcinoma
title_short Global Characterization of Immune Infiltration in Clear Cell Renal Cell Carcinoma
title_sort global characterization of immune infiltration in clear cell renal cell carcinoma
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7997590/
https://www.ncbi.nlm.nih.gov/pubmed/33790572
http://dx.doi.org/10.2147/OTT.S282763
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