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A signature of immune-related genes correlating with clinical prognosis and immune microenvironment in sepsis

BACKGROUND: Immune-related genes (IRGs) remain poorly understood in their function in the onset and progression of sepsis. METHODS: GSE65682 was obtained from the Gene Expression Omnibus database. The IRGs associated with survival were screened for subsequent modeling using univariate Cox regression...

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Autores principales: Chen, Zhong-Hua, Zhang, Wen-Yuan, Ye, Hui, Guo, Yu-Qian, Zhang, Kai, Fang, Xiang-Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9843880/
https://www.ncbi.nlm.nih.gov/pubmed/36650470
http://dx.doi.org/10.1186/s12859-023-05134-1
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author Chen, Zhong-Hua
Zhang, Wen-Yuan
Ye, Hui
Guo, Yu-Qian
Zhang, Kai
Fang, Xiang-Ming
author_facet Chen, Zhong-Hua
Zhang, Wen-Yuan
Ye, Hui
Guo, Yu-Qian
Zhang, Kai
Fang, Xiang-Ming
author_sort Chen, Zhong-Hua
collection PubMed
description BACKGROUND: Immune-related genes (IRGs) remain poorly understood in their function in the onset and progression of sepsis. METHODS: GSE65682 was obtained from the Gene Expression Omnibus database. The IRGs associated with survival were screened for subsequent modeling using univariate Cox regression analysis and least absolute shrinkage and selection operator in the training cohort. Then, we assessed the reliability of the 7 IRGs signature's independent predictive value in the training and validation cohorts following the creation of a signature applying multivariable Cox regression analysis. After that, we utilized the E-MTAB-4451 external dataset in order to do an independent validation of the prognostic signature. Finally, the CIBERSORT algorithm and single-sample gene set enrichment analysis was utilized to investigate and characterize the properties of the immune microenvironment. RESULTS: Based on 7 IRGs signature, patients could be separated into low-risk and high-risk groups. Patients in the low-risk group had a remarkably increased 28-day survival compared to those in the high-risk group (P < 0.001). In multivariable Cox regression analyses, the risk score calculated by this signature was an independent predictor of 28-day survival (P < 0.001). The signature's predictive ability was confirmed by receiver operating characteristic curve analysis with the area under the curve reaching 0.876 (95% confidence interval 0.793–0.946). Moreover, both the validation set and the external dataset demonstrated that the signature had strong clinical prediction performance. In addition, patients in the high-risk group were characterized by a decreased neutrophil count and by reduced inflammation-promoting function. CONCLUSION: We developed a 7 IRGs signature as a novel prognostic marker for predicting sepsis patients’ 28-day survival, indicating possibilities for individualized reasonable resource distribution of intensive care unit. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05134-1.
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spelling pubmed-98438802023-01-18 A signature of immune-related genes correlating with clinical prognosis and immune microenvironment in sepsis Chen, Zhong-Hua Zhang, Wen-Yuan Ye, Hui Guo, Yu-Qian Zhang, Kai Fang, Xiang-Ming BMC Bioinformatics Research BACKGROUND: Immune-related genes (IRGs) remain poorly understood in their function in the onset and progression of sepsis. METHODS: GSE65682 was obtained from the Gene Expression Omnibus database. The IRGs associated with survival were screened for subsequent modeling using univariate Cox regression analysis and least absolute shrinkage and selection operator in the training cohort. Then, we assessed the reliability of the 7 IRGs signature's independent predictive value in the training and validation cohorts following the creation of a signature applying multivariable Cox regression analysis. After that, we utilized the E-MTAB-4451 external dataset in order to do an independent validation of the prognostic signature. Finally, the CIBERSORT algorithm and single-sample gene set enrichment analysis was utilized to investigate and characterize the properties of the immune microenvironment. RESULTS: Based on 7 IRGs signature, patients could be separated into low-risk and high-risk groups. Patients in the low-risk group had a remarkably increased 28-day survival compared to those in the high-risk group (P < 0.001). In multivariable Cox regression analyses, the risk score calculated by this signature was an independent predictor of 28-day survival (P < 0.001). The signature's predictive ability was confirmed by receiver operating characteristic curve analysis with the area under the curve reaching 0.876 (95% confidence interval 0.793–0.946). Moreover, both the validation set and the external dataset demonstrated that the signature had strong clinical prediction performance. In addition, patients in the high-risk group were characterized by a decreased neutrophil count and by reduced inflammation-promoting function. CONCLUSION: We developed a 7 IRGs signature as a novel prognostic marker for predicting sepsis patients’ 28-day survival, indicating possibilities for individualized reasonable resource distribution of intensive care unit. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05134-1. BioMed Central 2023-01-17 /pmc/articles/PMC9843880/ /pubmed/36650470 http://dx.doi.org/10.1186/s12859-023-05134-1 Text en © The Author(s) 2023 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 Research
Chen, Zhong-Hua
Zhang, Wen-Yuan
Ye, Hui
Guo, Yu-Qian
Zhang, Kai
Fang, Xiang-Ming
A signature of immune-related genes correlating with clinical prognosis and immune microenvironment in sepsis
title A signature of immune-related genes correlating with clinical prognosis and immune microenvironment in sepsis
title_full A signature of immune-related genes correlating with clinical prognosis and immune microenvironment in sepsis
title_fullStr A signature of immune-related genes correlating with clinical prognosis and immune microenvironment in sepsis
title_full_unstemmed A signature of immune-related genes correlating with clinical prognosis and immune microenvironment in sepsis
title_short A signature of immune-related genes correlating with clinical prognosis and immune microenvironment in sepsis
title_sort signature of immune-related genes correlating with clinical prognosis and immune microenvironment in sepsis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9843880/
https://www.ncbi.nlm.nih.gov/pubmed/36650470
http://dx.doi.org/10.1186/s12859-023-05134-1
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