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Identifying changes in immune cells and constructing prognostic models using immune-related genes in post-burn immunosuppression
BACKGROUND: Burn patients are prone to infection as well as immunosuppression, which is a significant cause of death. Currently, there is a lack of prognostic biomarkers for immunosuppression in burn patients. This study was conducted to identify immune-related genes that are prognosis biomarkers in...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8761370/ https://www.ncbi.nlm.nih.gov/pubmed/35070500 http://dx.doi.org/10.7717/peerj.12680 |
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author | Wang, Peng Zhang, Zexin Yin, Bin Li, Jiayuan Xialin, Cheng Lian, Wenqin Su, Yingjun Jia, Chiyu |
author_facet | Wang, Peng Zhang, Zexin Yin, Bin Li, Jiayuan Xialin, Cheng Lian, Wenqin Su, Yingjun Jia, Chiyu |
author_sort | Wang, Peng |
collection | PubMed |
description | BACKGROUND: Burn patients are prone to infection as well as immunosuppression, which is a significant cause of death. Currently, there is a lack of prognostic biomarkers for immunosuppression in burn patients. This study was conducted to identify immune-related genes that are prognosis biomarkers in post-burn immunosuppression and potential targets for immunotherapy. METHODS: We downloaded the gene expression profiles and clinical data of 213 burn patients and 79 healthy samples from the Gene Expression Omnibus (GEO) database. Immune infiltration analysis was used to identify the proportion of circulating immune cells. Functional enrichment analyses were carried out to identify immune-related genes that were used to build miRNA-mRNA networks to screen key genes. Next, we carried out correlation analysis between immune cells and key genes that were then used to construct logistic regression models in GSE77791 and were validated in GSE19743. Finally, we determined the expression of key genes in burn patients using quantitative reverse transcription polymerase chain reaction (qRT-PCR). RESULTS: A total of 745 differently expressed genes were screened out: 299 were up-regulated and 446 were down-regulated. The number of Th-cells (CD4+) decreased while neutrophils increased in burn patients. The enrichment analysis showed that down-regulated genes were enriched in the T-cell activation pathway, while up-regulated genes were enriched in neutrophil activation response in burn patients. We screened out key genes (NFATC2, RORA, and CAMK4) that could be regulated by miRNA. The expression of key genes was related to the proportion of Th-cells (CD4+) and survival, and was an excellent predictor of prognosis in burns with an area under the curve (AUC) value of 0.945. Finally, we determined that NFATC2, RORA, and CAMK4 were down-regulated in burn patients. CONCLUSION: We found that NFATC2, RORA, and CAMK4 were likely prognostic biomarkers in post-burn immunosuppression and potential immunotherapeutic targets to convert Th-cell dysfunction. |
format | Online Article Text |
id | pubmed-8761370 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87613702022-01-21 Identifying changes in immune cells and constructing prognostic models using immune-related genes in post-burn immunosuppression Wang, Peng Zhang, Zexin Yin, Bin Li, Jiayuan Xialin, Cheng Lian, Wenqin Su, Yingjun Jia, Chiyu PeerJ Bioinformatics BACKGROUND: Burn patients are prone to infection as well as immunosuppression, which is a significant cause of death. Currently, there is a lack of prognostic biomarkers for immunosuppression in burn patients. This study was conducted to identify immune-related genes that are prognosis biomarkers in post-burn immunosuppression and potential targets for immunotherapy. METHODS: We downloaded the gene expression profiles and clinical data of 213 burn patients and 79 healthy samples from the Gene Expression Omnibus (GEO) database. Immune infiltration analysis was used to identify the proportion of circulating immune cells. Functional enrichment analyses were carried out to identify immune-related genes that were used to build miRNA-mRNA networks to screen key genes. Next, we carried out correlation analysis between immune cells and key genes that were then used to construct logistic regression models in GSE77791 and were validated in GSE19743. Finally, we determined the expression of key genes in burn patients using quantitative reverse transcription polymerase chain reaction (qRT-PCR). RESULTS: A total of 745 differently expressed genes were screened out: 299 were up-regulated and 446 were down-regulated. The number of Th-cells (CD4+) decreased while neutrophils increased in burn patients. The enrichment analysis showed that down-regulated genes were enriched in the T-cell activation pathway, while up-regulated genes were enriched in neutrophil activation response in burn patients. We screened out key genes (NFATC2, RORA, and CAMK4) that could be regulated by miRNA. The expression of key genes was related to the proportion of Th-cells (CD4+) and survival, and was an excellent predictor of prognosis in burns with an area under the curve (AUC) value of 0.945. Finally, we determined that NFATC2, RORA, and CAMK4 were down-regulated in burn patients. CONCLUSION: We found that NFATC2, RORA, and CAMK4 were likely prognostic biomarkers in post-burn immunosuppression and potential immunotherapeutic targets to convert Th-cell dysfunction. PeerJ Inc. 2022-01-13 /pmc/articles/PMC8761370/ /pubmed/35070500 http://dx.doi.org/10.7717/peerj.12680 Text en © 2022 Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Wang, Peng Zhang, Zexin Yin, Bin Li, Jiayuan Xialin, Cheng Lian, Wenqin Su, Yingjun Jia, Chiyu Identifying changes in immune cells and constructing prognostic models using immune-related genes in post-burn immunosuppression |
title | Identifying changes in immune cells and constructing prognostic models using immune-related genes in post-burn immunosuppression |
title_full | Identifying changes in immune cells and constructing prognostic models using immune-related genes in post-burn immunosuppression |
title_fullStr | Identifying changes in immune cells and constructing prognostic models using immune-related genes in post-burn immunosuppression |
title_full_unstemmed | Identifying changes in immune cells and constructing prognostic models using immune-related genes in post-burn immunosuppression |
title_short | Identifying changes in immune cells and constructing prognostic models using immune-related genes in post-burn immunosuppression |
title_sort | identifying changes in immune cells and constructing prognostic models using immune-related genes in post-burn immunosuppression |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8761370/ https://www.ncbi.nlm.nih.gov/pubmed/35070500 http://dx.doi.org/10.7717/peerj.12680 |
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