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Identifying Potential Effective Diagnostic and Prognostic Biomarkers in Sepsis by Bioinformatics Analysis and Validation
PURPOSE: Sepsis is a serious life-threatening condition characterised by multi-organ failure due to a disturbed immune response caused by severe infection. The pathogenesis of sepsis is unclear. The aim of this article is to identify potential diagnostic and prognostic biomarkers of sepsis to improv...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271908/ https://www.ncbi.nlm.nih.gov/pubmed/35832399 http://dx.doi.org/10.2147/IJGM.S368782 |
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author | Huang, Xu Tan, Jixiang Chen, Xiaoying Zhao, Lin |
author_facet | Huang, Xu Tan, Jixiang Chen, Xiaoying Zhao, Lin |
author_sort | Huang, Xu |
collection | PubMed |
description | PURPOSE: Sepsis is a serious life-threatening condition characterised by multi-organ failure due to a disturbed immune response caused by severe infection. The pathogenesis of sepsis is unclear. The aim of this article is to identify potential diagnostic and prognostic biomarkers of sepsis to improve the survival of patients with sepsis. METHODS: We downloaded 7 datasets from Gene Expression Omnibus database and screened the immune-related differential genes (IRDEGs). The related functions of IRDEGs were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). CIBERSORT was used to evaluate the infiltration of the immune cells, and Pearson algorithm of R software was used to calculate the correlation between the immune cell content and gene expression to screen the genes most related to immune cells in sepsis group, which were intersected with IRDEGs to obtain common genes. Key genes were identified from common genes based on the area under the receiver operating characteristic curve (AUC) greater than 0.8 in the 6 datasets. We then analyzed the predictive value of key genes in sepsis survival. Finally, we verified the expression of key genes in patients with sepsis by PCR analysis. RESULTS: A total of 164 IRDEGs were obtained, which were associated mainly with inflammatory and immunometabolic responses. Ten key genes (IL1R2, LTB4R, S100A11, S100A12, SORT1, RASGRP1, CD3G, CD40LG, CD8A and PPP3CC) were identified with high diagnostic efficacy. Logistic regression analysis revealed that six of the key genes (LTB4R, S100A11, SORT1, RASGRP1, CD3G and CD8A) may affect the survival prognosis of sepsis. PCR analysis confirmed that the expression of seven key genes (IL1R2, S100A12, RASGRP1, CD3G, CD40LG, CD8A and PPP3CC) was consistent with microarray outcome. CONCLUSION: This study explored the immune and metabolic response mechanisms associated with sepsis, and identified ten potential diagnostic and six prognostic genes. |
format | Online Article Text |
id | pubmed-9271908 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-92719082022-07-12 Identifying Potential Effective Diagnostic and Prognostic Biomarkers in Sepsis by Bioinformatics Analysis and Validation Huang, Xu Tan, Jixiang Chen, Xiaoying Zhao, Lin Int J Gen Med Original Research PURPOSE: Sepsis is a serious life-threatening condition characterised by multi-organ failure due to a disturbed immune response caused by severe infection. The pathogenesis of sepsis is unclear. The aim of this article is to identify potential diagnostic and prognostic biomarkers of sepsis to improve the survival of patients with sepsis. METHODS: We downloaded 7 datasets from Gene Expression Omnibus database and screened the immune-related differential genes (IRDEGs). The related functions of IRDEGs were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). CIBERSORT was used to evaluate the infiltration of the immune cells, and Pearson algorithm of R software was used to calculate the correlation between the immune cell content and gene expression to screen the genes most related to immune cells in sepsis group, which were intersected with IRDEGs to obtain common genes. Key genes were identified from common genes based on the area under the receiver operating characteristic curve (AUC) greater than 0.8 in the 6 datasets. We then analyzed the predictive value of key genes in sepsis survival. Finally, we verified the expression of key genes in patients with sepsis by PCR analysis. RESULTS: A total of 164 IRDEGs were obtained, which were associated mainly with inflammatory and immunometabolic responses. Ten key genes (IL1R2, LTB4R, S100A11, S100A12, SORT1, RASGRP1, CD3G, CD40LG, CD8A and PPP3CC) were identified with high diagnostic efficacy. Logistic regression analysis revealed that six of the key genes (LTB4R, S100A11, SORT1, RASGRP1, CD3G and CD8A) may affect the survival prognosis of sepsis. PCR analysis confirmed that the expression of seven key genes (IL1R2, S100A12, RASGRP1, CD3G, CD40LG, CD8A and PPP3CC) was consistent with microarray outcome. CONCLUSION: This study explored the immune and metabolic response mechanisms associated with sepsis, and identified ten potential diagnostic and six prognostic genes. Dove 2022-07-06 /pmc/articles/PMC9271908/ /pubmed/35832399 http://dx.doi.org/10.2147/IJGM.S368782 Text en © 2022 Huang et al. https://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/ (https://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 Huang, Xu Tan, Jixiang Chen, Xiaoying Zhao, Lin Identifying Potential Effective Diagnostic and Prognostic Biomarkers in Sepsis by Bioinformatics Analysis and Validation |
title | Identifying Potential Effective Diagnostic and Prognostic Biomarkers in Sepsis by Bioinformatics Analysis and Validation |
title_full | Identifying Potential Effective Diagnostic and Prognostic Biomarkers in Sepsis by Bioinformatics Analysis and Validation |
title_fullStr | Identifying Potential Effective Diagnostic and Prognostic Biomarkers in Sepsis by Bioinformatics Analysis and Validation |
title_full_unstemmed | Identifying Potential Effective Diagnostic and Prognostic Biomarkers in Sepsis by Bioinformatics Analysis and Validation |
title_short | Identifying Potential Effective Diagnostic and Prognostic Biomarkers in Sepsis by Bioinformatics Analysis and Validation |
title_sort | identifying potential effective diagnostic and prognostic biomarkers in sepsis by bioinformatics analysis and validation |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271908/ https://www.ncbi.nlm.nih.gov/pubmed/35832399 http://dx.doi.org/10.2147/IJGM.S368782 |
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