Cargando…

Identification of an immunogenic cell death-related gene signature predicts survival and sensitivity to immunotherapy in clear cell renal carcinoma

Immunogenic cell death (ICD) is the trigger of adaptive immune responses. However, the role of ICD-related genes in clear cell renal carcinoma (ccRCC) remains unclear. We aimed to identify biomarkers associated with ICD and develop an ICD-related predictive model that predicts the immune microenviro...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhou, Shuoming, Lu, Yanwen, Chen, Yuxin, Gan, Weidong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10023707/
https://www.ncbi.nlm.nih.gov/pubmed/36932108
http://dx.doi.org/10.1038/s41598-023-31493-z
_version_ 1784908942429650944
author Zhou, Shuoming
Lu, Yanwen
Chen, Yuxin
Gan, Weidong
author_facet Zhou, Shuoming
Lu, Yanwen
Chen, Yuxin
Gan, Weidong
author_sort Zhou, Shuoming
collection PubMed
description Immunogenic cell death (ICD) is the trigger of adaptive immune responses. However, the role of ICD-related genes in clear cell renal carcinoma (ccRCC) remains unclear. We aimed to identify biomarkers associated with ICD and develop an ICD-related predictive model that predicts the immune microenvironment, prognosis, and response to immunotherapy in ccRCC. Our study included 739 patients (603 in the training set and 136 in the validation set) with clinicopathologic information and transcriptome sequencing data. Consensus clustering, principal component analysis (PCA), weighted gene co-expression network analysis (WGCNA), univariate COX analysis, multivariate COX analysis, and the Lasso-Cox algorithm were applied to shrink predictors and construct a predictive signature of overall survival (OS). We used CIBERSORT, ESTIMATE, and TIMER in the R package IOBR to evaluate the tumor microenvironment and immune infiltration pattern of each sample. Finally, the single cell sequencing results of immune cells in ccRCC were used to verify the results of immune infiltration analysis, and the performance of the prognostic model was evaluated by calibration curves and c-index. This study revealed that inability of the initial immune response and primary immunodeficiency were significantly enriched in the ICD subgroup with poor prognosis. We found that the ten candidate ICD genes (CALR, ENTPD1, FOXP3, HSP90AA1, IFNB1, IFNG, IL6, LY96, PIK3CA, and TLR4) could affect the prognosis of ccRCC (p < 0.05). The prediction model (PRE) we constructed can not only predict the long-term survival probability but also evaluate the landscape of immune infiltration in ccRCC. Our study demonstrated that low infiltration of dendritic cells in ccRCC implies a poor prognosis, whereas the degree of CTL infiltration is less important. An individualized prediction model was created to predict the 1-, 2-, 3-, and 5-year survival and responsiveness of ccRCC patients to immunotherapy, which may serve as a potent tool for clinicians to make better treatment decisions and thus improve the overall survival (OS) of ccRCC patients in the future.
format Online
Article
Text
id pubmed-10023707
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-100237072023-03-19 Identification of an immunogenic cell death-related gene signature predicts survival and sensitivity to immunotherapy in clear cell renal carcinoma Zhou, Shuoming Lu, Yanwen Chen, Yuxin Gan, Weidong Sci Rep Article Immunogenic cell death (ICD) is the trigger of adaptive immune responses. However, the role of ICD-related genes in clear cell renal carcinoma (ccRCC) remains unclear. We aimed to identify biomarkers associated with ICD and develop an ICD-related predictive model that predicts the immune microenvironment, prognosis, and response to immunotherapy in ccRCC. Our study included 739 patients (603 in the training set and 136 in the validation set) with clinicopathologic information and transcriptome sequencing data. Consensus clustering, principal component analysis (PCA), weighted gene co-expression network analysis (WGCNA), univariate COX analysis, multivariate COX analysis, and the Lasso-Cox algorithm were applied to shrink predictors and construct a predictive signature of overall survival (OS). We used CIBERSORT, ESTIMATE, and TIMER in the R package IOBR to evaluate the tumor microenvironment and immune infiltration pattern of each sample. Finally, the single cell sequencing results of immune cells in ccRCC were used to verify the results of immune infiltration analysis, and the performance of the prognostic model was evaluated by calibration curves and c-index. This study revealed that inability of the initial immune response and primary immunodeficiency were significantly enriched in the ICD subgroup with poor prognosis. We found that the ten candidate ICD genes (CALR, ENTPD1, FOXP3, HSP90AA1, IFNB1, IFNG, IL6, LY96, PIK3CA, and TLR4) could affect the prognosis of ccRCC (p < 0.05). The prediction model (PRE) we constructed can not only predict the long-term survival probability but also evaluate the landscape of immune infiltration in ccRCC. Our study demonstrated that low infiltration of dendritic cells in ccRCC implies a poor prognosis, whereas the degree of CTL infiltration is less important. An individualized prediction model was created to predict the 1-, 2-, 3-, and 5-year survival and responsiveness of ccRCC patients to immunotherapy, which may serve as a potent tool for clinicians to make better treatment decisions and thus improve the overall survival (OS) of ccRCC patients in the future. Nature Publishing Group UK 2023-03-17 /pmc/articles/PMC10023707/ /pubmed/36932108 http://dx.doi.org/10.1038/s41598-023-31493-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) .
spellingShingle Article
Zhou, Shuoming
Lu, Yanwen
Chen, Yuxin
Gan, Weidong
Identification of an immunogenic cell death-related gene signature predicts survival and sensitivity to immunotherapy in clear cell renal carcinoma
title Identification of an immunogenic cell death-related gene signature predicts survival and sensitivity to immunotherapy in clear cell renal carcinoma
title_full Identification of an immunogenic cell death-related gene signature predicts survival and sensitivity to immunotherapy in clear cell renal carcinoma
title_fullStr Identification of an immunogenic cell death-related gene signature predicts survival and sensitivity to immunotherapy in clear cell renal carcinoma
title_full_unstemmed Identification of an immunogenic cell death-related gene signature predicts survival and sensitivity to immunotherapy in clear cell renal carcinoma
title_short Identification of an immunogenic cell death-related gene signature predicts survival and sensitivity to immunotherapy in clear cell renal carcinoma
title_sort identification of an immunogenic cell death-related gene signature predicts survival and sensitivity to immunotherapy in clear cell renal carcinoma
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10023707/
https://www.ncbi.nlm.nih.gov/pubmed/36932108
http://dx.doi.org/10.1038/s41598-023-31493-z
work_keys_str_mv AT zhoushuoming identificationofanimmunogeniccelldeathrelatedgenesignaturepredictssurvivalandsensitivitytoimmunotherapyinclearcellrenalcarcinoma
AT luyanwen identificationofanimmunogeniccelldeathrelatedgenesignaturepredictssurvivalandsensitivitytoimmunotherapyinclearcellrenalcarcinoma
AT chenyuxin identificationofanimmunogeniccelldeathrelatedgenesignaturepredictssurvivalandsensitivitytoimmunotherapyinclearcellrenalcarcinoma
AT ganweidong identificationofanimmunogeniccelldeathrelatedgenesignaturepredictssurvivalandsensitivitytoimmunotherapyinclearcellrenalcarcinoma