Cargando…
Screening of Sepsis Biomarkers Based on Bioinformatics Data Analysis
METHODS: Gene expression profiles of GSE13904, GSE26378, GSE26440, GSE65682, and GSE69528 were obtained from the National Center for Biotechnology Information (NCBI). The differentially expressed genes (DEGs) were searched using limma software package. Gene Ontology (GO) functional analysis, Kyoto E...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9529510/ https://www.ncbi.nlm.nih.gov/pubmed/36199375 http://dx.doi.org/10.1155/2022/6788569 |
_version_ | 1784801510889095168 |
---|---|
author | Liang, Guibin Li, Jiuang Pu, Shiqian He, Zhihui |
author_facet | Liang, Guibin Li, Jiuang Pu, Shiqian He, Zhihui |
author_sort | Liang, Guibin |
collection | PubMed |
description | METHODS: Gene expression profiles of GSE13904, GSE26378, GSE26440, GSE65682, and GSE69528 were obtained from the National Center for Biotechnology Information (NCBI). The differentially expressed genes (DEGs) were searched using limma software package. Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs and screen hub genes. RESULTS: A total of 108 DEGs were identified in the study, of which 67 were upregulated and 41 were downregulated. 15 superlative diagnostic biomarkers (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) for sepsis were identified by bioinformatics analysis. CONCLUSION: 15 hub genes (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) have been elucidated in this study, and these biomarkers may be helpful in the diagnosis and therapy of patients with sepsis. |
format | Online Article Text |
id | pubmed-9529510 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-95295102022-10-04 Screening of Sepsis Biomarkers Based on Bioinformatics Data Analysis Liang, Guibin Li, Jiuang Pu, Shiqian He, Zhihui J Healthc Eng Research Article METHODS: Gene expression profiles of GSE13904, GSE26378, GSE26440, GSE65682, and GSE69528 were obtained from the National Center for Biotechnology Information (NCBI). The differentially expressed genes (DEGs) were searched using limma software package. Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs and screen hub genes. RESULTS: A total of 108 DEGs were identified in the study, of which 67 were upregulated and 41 were downregulated. 15 superlative diagnostic biomarkers (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) for sepsis were identified by bioinformatics analysis. CONCLUSION: 15 hub genes (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) have been elucidated in this study, and these biomarkers may be helpful in the diagnosis and therapy of patients with sepsis. Hindawi 2022-09-26 /pmc/articles/PMC9529510/ /pubmed/36199375 http://dx.doi.org/10.1155/2022/6788569 Text en Copyright © 2022 Guibin Liang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Liang, Guibin Li, Jiuang Pu, Shiqian He, Zhihui Screening of Sepsis Biomarkers Based on Bioinformatics Data Analysis |
title | Screening of Sepsis Biomarkers Based on Bioinformatics Data Analysis |
title_full | Screening of Sepsis Biomarkers Based on Bioinformatics Data Analysis |
title_fullStr | Screening of Sepsis Biomarkers Based on Bioinformatics Data Analysis |
title_full_unstemmed | Screening of Sepsis Biomarkers Based on Bioinformatics Data Analysis |
title_short | Screening of Sepsis Biomarkers Based on Bioinformatics Data Analysis |
title_sort | screening of sepsis biomarkers based on bioinformatics data analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9529510/ https://www.ncbi.nlm.nih.gov/pubmed/36199375 http://dx.doi.org/10.1155/2022/6788569 |
work_keys_str_mv | AT liangguibin screeningofsepsisbiomarkersbasedonbioinformaticsdataanalysis AT lijiuang screeningofsepsisbiomarkersbasedonbioinformaticsdataanalysis AT pushiqian screeningofsepsisbiomarkersbasedonbioinformaticsdataanalysis AT hezhihui screeningofsepsisbiomarkersbasedonbioinformaticsdataanalysis |