Cargando…

Uncovering hub genes in sepsis through bioinformatics analysis

In-depth studies on the mechanisms of pathogenesis of sepsis and diagnostic biomarkers in the early stages may be the key to developing individualized and effective treatment strategies. This study aimed to identify sepsis-related hub genes and evaluate their diagnostic reliability. The gene express...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Zhao, Qiu, Eryue, Yang, Bihui, Zeng, Yiqian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10695588/
http://dx.doi.org/10.1097/MD.0000000000036237
_version_ 1785153598870519808
author Liu, Zhao
Qiu, Eryue
Yang, Bihui
Zeng, Yiqian
author_facet Liu, Zhao
Qiu, Eryue
Yang, Bihui
Zeng, Yiqian
author_sort Liu, Zhao
collection PubMed
description In-depth studies on the mechanisms of pathogenesis of sepsis and diagnostic biomarkers in the early stages may be the key to developing individualized and effective treatment strategies. This study aimed to identify sepsis-related hub genes and evaluate their diagnostic reliability. The gene expression profiles of GSE4607 and GSE131761 were obtained from the Gene Expression Omnibus. Differentially co-expressed genes between the sepsis and control groups were screened. Single-sample gene set enrichment analysis and gene set variation analysis were performed to investigate the biological functions of the hub genes. A receiver operating characteristic curve was used to evaluate diagnostic value. Datasets GSE154918 and GSE185263 were used as external validation datasets to verify the reliability of the hub genes. Four differentially co-expressed genes, FAM89A, FFAR3, G0S2, and FGF13, were extracted using a weighted gene co-expression network analysis and differential gene expression analysis methods. These 4 genes were upregulated in the sepsis group and were distinct from those in the controls. Moreover, the receiver operating characteristic curves of the 4 genes exhibited considerable diagnostic value in discriminating septic blood samples from those of the non-septic control group. The reliability and consistency of these 4 genes were externally validated. Single-sample gene set enrichment analysis and gene set variation analysis analyses indicated that the 4 hub genes were significantly correlated with the regulation of immunity and metabolism in sepsis. The identified FAM89A, FFAR3, G0S2, and FGF13 genes may help elucidate the molecular mechanisms underlying sepsis and drive the introduction of new biomarkers to advance diagnosis and treatment.
format Online
Article
Text
id pubmed-10695588
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Lippincott Williams & Wilkins
record_format MEDLINE/PubMed
spelling pubmed-106955882023-12-05 Uncovering hub genes in sepsis through bioinformatics analysis Liu, Zhao Qiu, Eryue Yang, Bihui Zeng, Yiqian Medicine (Baltimore) 3500 In-depth studies on the mechanisms of pathogenesis of sepsis and diagnostic biomarkers in the early stages may be the key to developing individualized and effective treatment strategies. This study aimed to identify sepsis-related hub genes and evaluate their diagnostic reliability. The gene expression profiles of GSE4607 and GSE131761 were obtained from the Gene Expression Omnibus. Differentially co-expressed genes between the sepsis and control groups were screened. Single-sample gene set enrichment analysis and gene set variation analysis were performed to investigate the biological functions of the hub genes. A receiver operating characteristic curve was used to evaluate diagnostic value. Datasets GSE154918 and GSE185263 were used as external validation datasets to verify the reliability of the hub genes. Four differentially co-expressed genes, FAM89A, FFAR3, G0S2, and FGF13, were extracted using a weighted gene co-expression network analysis and differential gene expression analysis methods. These 4 genes were upregulated in the sepsis group and were distinct from those in the controls. Moreover, the receiver operating characteristic curves of the 4 genes exhibited considerable diagnostic value in discriminating septic blood samples from those of the non-septic control group. The reliability and consistency of these 4 genes were externally validated. Single-sample gene set enrichment analysis and gene set variation analysis analyses indicated that the 4 hub genes were significantly correlated with the regulation of immunity and metabolism in sepsis. The identified FAM89A, FFAR3, G0S2, and FGF13 genes may help elucidate the molecular mechanisms underlying sepsis and drive the introduction of new biomarkers to advance diagnosis and treatment. Lippincott Williams & Wilkins 2023-12-01 /pmc/articles/PMC10695588/ http://dx.doi.org/10.1097/MD.0000000000036237 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle 3500
Liu, Zhao
Qiu, Eryue
Yang, Bihui
Zeng, Yiqian
Uncovering hub genes in sepsis through bioinformatics analysis
title Uncovering hub genes in sepsis through bioinformatics analysis
title_full Uncovering hub genes in sepsis through bioinformatics analysis
title_fullStr Uncovering hub genes in sepsis through bioinformatics analysis
title_full_unstemmed Uncovering hub genes in sepsis through bioinformatics analysis
title_short Uncovering hub genes in sepsis through bioinformatics analysis
title_sort uncovering hub genes in sepsis through bioinformatics analysis
topic 3500
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10695588/
http://dx.doi.org/10.1097/MD.0000000000036237
work_keys_str_mv AT liuzhao uncoveringhubgenesinsepsisthroughbioinformaticsanalysis
AT qiueryue uncoveringhubgenesinsepsisthroughbioinformaticsanalysis
AT yangbihui uncoveringhubgenesinsepsisthroughbioinformaticsanalysis
AT zengyiqian uncoveringhubgenesinsepsisthroughbioinformaticsanalysis