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Identification of Potential Early Diagnostic Biomarkers of Sepsis
OBJECTIVE: The goal of this article was to identify potential biomarkers for early diagnosis of sepsis in order to improve their survival. METHODS: We analyzed differential gene expression between adult sepsis patients and controls in the GSE54514 dataset. Coexpression analysis was used to cluster c...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7937397/ https://www.ncbi.nlm.nih.gov/pubmed/33688234 http://dx.doi.org/10.2147/JIR.S298604 |
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author | Li, Zhenhua Huang, Bin Yi, Wenfeng Wang, Fei Wei, Shizhuang Yan, Huaixing Qin, Pan Zou, Donghua Wei, Rongguo Chen, Nian |
author_facet | Li, Zhenhua Huang, Bin Yi, Wenfeng Wang, Fei Wei, Shizhuang Yan, Huaixing Qin, Pan Zou, Donghua Wei, Rongguo Chen, Nian |
author_sort | Li, Zhenhua |
collection | PubMed |
description | OBJECTIVE: The goal of this article was to identify potential biomarkers for early diagnosis of sepsis in order to improve their survival. METHODS: We analyzed differential gene expression between adult sepsis patients and controls in the GSE54514 dataset. Coexpression analysis was used to cluster coexpression modules, and enrichment analysis was performed on module genes. We also analyzed differential gene expression between neonatal sepsis patients and controls in the GSE25504 dataset, and we identified the subset of differentially expressed genes (DEGs) common to neonates and adults. All samples in the GSE54514 dataset were randomly divided into training and validation sets, and diagnostic signatures were constructed using least absolute shrink and selection operator (LASSO) regression. The key gene signature was screened for diagnostic value based on area under the receiver operating characteristic curve (AUC). STEM software identified dysregulated genes associated with sepsis-associated mortality. The ssGSEA method was used to quantify differences in immune cell infiltration between sepsis and control samples. RESULTS: A total of 6316 DEGs in GSE54514 were obtained spanning 10 modules. Module genes were mainly enriched in immune and metabolic responses. Screening 51 genes from among common genes based on AUC > 0.7 led to a LASSO model for the training set. We obtained a 25-gene signature, which we validated in the validation set and in the GSE25504 dataset. Among the signature genes, SLC2A6, C1ORF55, DUSP5 and RHOB were recognized as key genes (AUC > 0.75) in both the GSE54514 and GSE25504 datasets. SLC2A6 was identified by STEM as associated with sepsis-associated mortality and showed the strongest positive correlation with infiltration levels of Th1 cells. CONCLUSION: In summary, our four key genes may have important implications for the early diagnosis of sepsis patients. In particular, SLC2A6 may be a critical biomarker for predicting survival in sepsis. |
format | Online Article Text |
id | pubmed-7937397 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-79373972021-03-08 Identification of Potential Early Diagnostic Biomarkers of Sepsis Li, Zhenhua Huang, Bin Yi, Wenfeng Wang, Fei Wei, Shizhuang Yan, Huaixing Qin, Pan Zou, Donghua Wei, Rongguo Chen, Nian J Inflamm Res Original Research OBJECTIVE: The goal of this article was to identify potential biomarkers for early diagnosis of sepsis in order to improve their survival. METHODS: We analyzed differential gene expression between adult sepsis patients and controls in the GSE54514 dataset. Coexpression analysis was used to cluster coexpression modules, and enrichment analysis was performed on module genes. We also analyzed differential gene expression between neonatal sepsis patients and controls in the GSE25504 dataset, and we identified the subset of differentially expressed genes (DEGs) common to neonates and adults. All samples in the GSE54514 dataset were randomly divided into training and validation sets, and diagnostic signatures were constructed using least absolute shrink and selection operator (LASSO) regression. The key gene signature was screened for diagnostic value based on area under the receiver operating characteristic curve (AUC). STEM software identified dysregulated genes associated with sepsis-associated mortality. The ssGSEA method was used to quantify differences in immune cell infiltration between sepsis and control samples. RESULTS: A total of 6316 DEGs in GSE54514 were obtained spanning 10 modules. Module genes were mainly enriched in immune and metabolic responses. Screening 51 genes from among common genes based on AUC > 0.7 led to a LASSO model for the training set. We obtained a 25-gene signature, which we validated in the validation set and in the GSE25504 dataset. Among the signature genes, SLC2A6, C1ORF55, DUSP5 and RHOB were recognized as key genes (AUC > 0.75) in both the GSE54514 and GSE25504 datasets. SLC2A6 was identified by STEM as associated with sepsis-associated mortality and showed the strongest positive correlation with infiltration levels of Th1 cells. CONCLUSION: In summary, our four key genes may have important implications for the early diagnosis of sepsis patients. In particular, SLC2A6 may be a critical biomarker for predicting survival in sepsis. Dove 2021-03-03 /pmc/articles/PMC7937397/ /pubmed/33688234 http://dx.doi.org/10.2147/JIR.S298604 Text en © 2021 Li et al. http://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/). 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 Li, Zhenhua Huang, Bin Yi, Wenfeng Wang, Fei Wei, Shizhuang Yan, Huaixing Qin, Pan Zou, Donghua Wei, Rongguo Chen, Nian Identification of Potential Early Diagnostic Biomarkers of Sepsis |
title | Identification of Potential Early Diagnostic Biomarkers of Sepsis |
title_full | Identification of Potential Early Diagnostic Biomarkers of Sepsis |
title_fullStr | Identification of Potential Early Diagnostic Biomarkers of Sepsis |
title_full_unstemmed | Identification of Potential Early Diagnostic Biomarkers of Sepsis |
title_short | Identification of Potential Early Diagnostic Biomarkers of Sepsis |
title_sort | identification of potential early diagnostic biomarkers of sepsis |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7937397/ https://www.ncbi.nlm.nih.gov/pubmed/33688234 http://dx.doi.org/10.2147/JIR.S298604 |
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