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Identification of Important Modules and Biomarkers That Are Related to Immune Infiltration Cells in Severe Burns Based on Weighted Gene Co-Expression Network Analysis

Background: Immunosuppression is an important trigger for infection and a significant cause of death in patients with severe burns. Nevertheless, the prognostic value of immune-related genes remains unclear. This study aimed to identify the biomarkers related to immunosuppression in severe burns. Me...

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Autores principales: Zhang, Zexin, He, Yan, Lin, Rongjie, Lan, Junhong, Fan, Yueying, Wang, Peng, Jia, Chiyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9218676/
https://www.ncbi.nlm.nih.gov/pubmed/35754830
http://dx.doi.org/10.3389/fgene.2022.908510
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author Zhang, Zexin
He, Yan
Lin, Rongjie
Lan, Junhong
Fan, Yueying
Wang, Peng
Jia, Chiyu
author_facet Zhang, Zexin
He, Yan
Lin, Rongjie
Lan, Junhong
Fan, Yueying
Wang, Peng
Jia, Chiyu
author_sort Zhang, Zexin
collection PubMed
description Background: Immunosuppression is an important trigger for infection and a significant cause of death in patients with severe burns. Nevertheless, the prognostic value of immune-related genes remains unclear. This study aimed to identify the biomarkers related to immunosuppression in severe burns. Methods: The gene expression profile and clinical data of 185 burn and 75 healthy samples were obtained from the GEO database. Immune infiltration analysis and gene set variation analysis were utilized to identify the disorder of circulating immune cells. A weighted gene co-expression network analysis (WGCNA) was carried out to select immune-related gene modules. Enrichment analysis and protein–protein interaction (PPI) network were performed to select hub genes. Next, LASSO and logistic regression were utilized to construct the hazard regression model with a survival state. Finally, we investigated the correlation between high- and low-risk patients in total burn surface area (TBSA), age, and inhalation injury. Results: Gene set variation analysis (GSVA) and immune infiltration analysis showed that neutrophils increased and T cells decreased in severe burns. In WGCNA, four modular differently expressed in burns and controls were related to immune cells. Based on PPI and enrichment analysis, 210 immune-related genes were identified, mainly involved in T-cell inhibition and neutrophil activation. In LASSO and logistic regression, we screened out key genes, including LCK, SKAP1 and GZMB, and LY9. In the ROC analysis, the area under the curve (AUC) of key genes was 0.945, indicating that the key genes had excellent diagnostic value. Finally, we discovered that the key genes were related to T cells, and the regression model performed well when accompanied by TBSA and age. Conclusion: We identified LCK, SKAP1, GZMB, and LY9 as good prognostic biomarkers that may play a role in post-burn immunosuppression against T-cell dysfunction and as potential immunotherapeutic targets for transformed T-cell dysfunction.
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spelling pubmed-92186762022-06-24 Identification of Important Modules and Biomarkers That Are Related to Immune Infiltration Cells in Severe Burns Based on Weighted Gene Co-Expression Network Analysis Zhang, Zexin He, Yan Lin, Rongjie Lan, Junhong Fan, Yueying Wang, Peng Jia, Chiyu Front Genet Genetics Background: Immunosuppression is an important trigger for infection and a significant cause of death in patients with severe burns. Nevertheless, the prognostic value of immune-related genes remains unclear. This study aimed to identify the biomarkers related to immunosuppression in severe burns. Methods: The gene expression profile and clinical data of 185 burn and 75 healthy samples were obtained from the GEO database. Immune infiltration analysis and gene set variation analysis were utilized to identify the disorder of circulating immune cells. A weighted gene co-expression network analysis (WGCNA) was carried out to select immune-related gene modules. Enrichment analysis and protein–protein interaction (PPI) network were performed to select hub genes. Next, LASSO and logistic regression were utilized to construct the hazard regression model with a survival state. Finally, we investigated the correlation between high- and low-risk patients in total burn surface area (TBSA), age, and inhalation injury. Results: Gene set variation analysis (GSVA) and immune infiltration analysis showed that neutrophils increased and T cells decreased in severe burns. In WGCNA, four modular differently expressed in burns and controls were related to immune cells. Based on PPI and enrichment analysis, 210 immune-related genes were identified, mainly involved in T-cell inhibition and neutrophil activation. In LASSO and logistic regression, we screened out key genes, including LCK, SKAP1 and GZMB, and LY9. In the ROC analysis, the area under the curve (AUC) of key genes was 0.945, indicating that the key genes had excellent diagnostic value. Finally, we discovered that the key genes were related to T cells, and the regression model performed well when accompanied by TBSA and age. Conclusion: We identified LCK, SKAP1, GZMB, and LY9 as good prognostic biomarkers that may play a role in post-burn immunosuppression against T-cell dysfunction and as potential immunotherapeutic targets for transformed T-cell dysfunction. Frontiers Media S.A. 2022-06-09 /pmc/articles/PMC9218676/ /pubmed/35754830 http://dx.doi.org/10.3389/fgene.2022.908510 Text en Copyright © 2022 Zhang, He, Lin, Lan, Fan, Wang and Jia. 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 Genetics
Zhang, Zexin
He, Yan
Lin, Rongjie
Lan, Junhong
Fan, Yueying
Wang, Peng
Jia, Chiyu
Identification of Important Modules and Biomarkers That Are Related to Immune Infiltration Cells in Severe Burns Based on Weighted Gene Co-Expression Network Analysis
title Identification of Important Modules and Biomarkers That Are Related to Immune Infiltration Cells in Severe Burns Based on Weighted Gene Co-Expression Network Analysis
title_full Identification of Important Modules and Biomarkers That Are Related to Immune Infiltration Cells in Severe Burns Based on Weighted Gene Co-Expression Network Analysis
title_fullStr Identification of Important Modules and Biomarkers That Are Related to Immune Infiltration Cells in Severe Burns Based on Weighted Gene Co-Expression Network Analysis
title_full_unstemmed Identification of Important Modules and Biomarkers That Are Related to Immune Infiltration Cells in Severe Burns Based on Weighted Gene Co-Expression Network Analysis
title_short Identification of Important Modules and Biomarkers That Are Related to Immune Infiltration Cells in Severe Burns Based on Weighted Gene Co-Expression Network Analysis
title_sort identification of important modules and biomarkers that are related to immune infiltration cells in severe burns based on weighted gene co-expression network analysis
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9218676/
https://www.ncbi.nlm.nih.gov/pubmed/35754830
http://dx.doi.org/10.3389/fgene.2022.908510
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