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Identification of biomarkers related to sepsis diagnosis based on bioinformatics and machine learning and experimental verification
Sepsis is a systemic inflammatory response syndrome caused by bacteria and other pathogenic microorganisms. Every year, approximately 31.5 million patients are diagnosed with sepsis, and approximately 5.3 million patients succumb to the disease. In this study, we identified biomarkers for diagnosing...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337583/ https://www.ncbi.nlm.nih.gov/pubmed/37449204 http://dx.doi.org/10.3389/fimmu.2023.1087691 |
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author | Wang, Qianfei Wang, Chenxi Zhang, Weichao Tao, Yulei Guo, Junli Liu, Yuan Liu, Zhiliang Liu, Dong Mei, Jianqiang Chen, Fenqiao |
author_facet | Wang, Qianfei Wang, Chenxi Zhang, Weichao Tao, Yulei Guo, Junli Liu, Yuan Liu, Zhiliang Liu, Dong Mei, Jianqiang Chen, Fenqiao |
author_sort | Wang, Qianfei |
collection | PubMed |
description | Sepsis is a systemic inflammatory response syndrome caused by bacteria and other pathogenic microorganisms. Every year, approximately 31.5 million patients are diagnosed with sepsis, and approximately 5.3 million patients succumb to the disease. In this study, we identified biomarkers for diagnosing sepsis analyzed the relationships between genes and Immune cells that were differentially expressed in specimens from patients with sepsis compared to normal controls. Finally, We verified its effectiveness through animal experiments. Specifically, we analyzed datasets from four microarrays(GSE11755、GSE12624、GSE28750、GSE48080) that included 106 blood specimens from patients with sepsis and 69 normal human blood samples. SVM-RFE analysis and LASSO regression model were carried out to screen possible markers. The composition of 22 immune cell components in patients with sepsis were also determined using CIBERSORT. The expression level of the biomarkers in Sepsis was examined by the use of qRT-PCR and Western Blot (WB). We identified 50 differentially expressed genes between the cohorts, including 2 significantly upregulated and 48 significantly downregulated genes, and KEGG pathway analysis identified Salmonella infection, human T cell leukemia virus 1 infection, Epstein−Barr virus infection, hepatitis B, lysosome and other pathways that were significantly enriched in blood from patients with sepsis. Ultimately, we identified COMMD9, CSF3R, and NUB1 as genes that could potentially be used as biomarkers to predict sepsis, which we confirmed by ROC analysis. Further, we identified a correlation between the expression of these three genes and immune infiltrate composition. Immune cell infiltration analysis revealed that COMMD9 was correlated with T cells regulatory (Tregs), T cells follicular helper, T cells CD8, et al. CSF3R was correlated with T cells regulatory (Tregs), T cells follicular helper, T cells CD8, et al. NUB1 was correlated with T cells regulatory (Tregs), T cells gamma delta, T cells follicular helper, et al. Taken together, our findings identify potential new diagnostic markers for sepsis that shed light on novel mechanisms of disease pathogenesis and, therefore, may offer opportunities for therapeutic intervention. |
format | Online Article Text |
id | pubmed-10337583 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103375832023-07-13 Identification of biomarkers related to sepsis diagnosis based on bioinformatics and machine learning and experimental verification Wang, Qianfei Wang, Chenxi Zhang, Weichao Tao, Yulei Guo, Junli Liu, Yuan Liu, Zhiliang Liu, Dong Mei, Jianqiang Chen, Fenqiao Front Immunol Immunology Sepsis is a systemic inflammatory response syndrome caused by bacteria and other pathogenic microorganisms. Every year, approximately 31.5 million patients are diagnosed with sepsis, and approximately 5.3 million patients succumb to the disease. In this study, we identified biomarkers for diagnosing sepsis analyzed the relationships between genes and Immune cells that were differentially expressed in specimens from patients with sepsis compared to normal controls. Finally, We verified its effectiveness through animal experiments. Specifically, we analyzed datasets from four microarrays(GSE11755、GSE12624、GSE28750、GSE48080) that included 106 blood specimens from patients with sepsis and 69 normal human blood samples. SVM-RFE analysis and LASSO regression model were carried out to screen possible markers. The composition of 22 immune cell components in patients with sepsis were also determined using CIBERSORT. The expression level of the biomarkers in Sepsis was examined by the use of qRT-PCR and Western Blot (WB). We identified 50 differentially expressed genes between the cohorts, including 2 significantly upregulated and 48 significantly downregulated genes, and KEGG pathway analysis identified Salmonella infection, human T cell leukemia virus 1 infection, Epstein−Barr virus infection, hepatitis B, lysosome and other pathways that were significantly enriched in blood from patients with sepsis. Ultimately, we identified COMMD9, CSF3R, and NUB1 as genes that could potentially be used as biomarkers to predict sepsis, which we confirmed by ROC analysis. Further, we identified a correlation between the expression of these three genes and immune infiltrate composition. Immune cell infiltration analysis revealed that COMMD9 was correlated with T cells regulatory (Tregs), T cells follicular helper, T cells CD8, et al. CSF3R was correlated with T cells regulatory (Tregs), T cells follicular helper, T cells CD8, et al. NUB1 was correlated with T cells regulatory (Tregs), T cells gamma delta, T cells follicular helper, et al. Taken together, our findings identify potential new diagnostic markers for sepsis that shed light on novel mechanisms of disease pathogenesis and, therefore, may offer opportunities for therapeutic intervention. Frontiers Media S.A. 2023-06-28 /pmc/articles/PMC10337583/ /pubmed/37449204 http://dx.doi.org/10.3389/fimmu.2023.1087691 Text en Copyright © 2023 Wang, Wang, Zhang, Tao, Guo, Liu, Liu, Liu, Mei and Chen 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 | Immunology Wang, Qianfei Wang, Chenxi Zhang, Weichao Tao, Yulei Guo, Junli Liu, Yuan Liu, Zhiliang Liu, Dong Mei, Jianqiang Chen, Fenqiao Identification of biomarkers related to sepsis diagnosis based on bioinformatics and machine learning and experimental verification |
title | Identification of biomarkers related to sepsis diagnosis based on bioinformatics and machine learning and experimental verification |
title_full | Identification of biomarkers related to sepsis diagnosis based on bioinformatics and machine learning and experimental verification |
title_fullStr | Identification of biomarkers related to sepsis diagnosis based on bioinformatics and machine learning and experimental verification |
title_full_unstemmed | Identification of biomarkers related to sepsis diagnosis based on bioinformatics and machine learning and experimental verification |
title_short | Identification of biomarkers related to sepsis diagnosis based on bioinformatics and machine learning and experimental verification |
title_sort | identification of biomarkers related to sepsis diagnosis based on bioinformatics and machine learning and experimental verification |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337583/ https://www.ncbi.nlm.nih.gov/pubmed/37449204 http://dx.doi.org/10.3389/fimmu.2023.1087691 |
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