Cargando…

CONSTRUCTION OF SEPSIS DIAGNOSTIC MODELS AND IDENTIFICATION OF MACROPHAGE SUBPOPULATIONS BASED ON PYROPTOSIS-RELATED GENES

Background: Numerous studies have shown that pyroptosis is associated with sepsis progression, which can lead to dysregulated host immune responses and organ dysfunction. Therefore, investigating the potential prognostic and diagnostic values of pyroptosis in patients with sepsis is essential. Metho...

Descripción completa

Detalles Bibliográficos
Autores principales: Sun, Zefang, Zhang, Tao, Ning, Caihong, Shen, Dingcheng, Pei, Wenwu, Zhou, Rui, Zhu, Shuai, Huang, Gengwen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417255/
https://www.ncbi.nlm.nih.gov/pubmed/37179249
http://dx.doi.org/10.1097/SHK.0000000000002137
_version_ 1785087982062010368
author Sun, Zefang
Zhang, Tao
Ning, Caihong
Shen, Dingcheng
Pei, Wenwu
Zhou, Rui
Zhu, Shuai
Huang, Gengwen
author_facet Sun, Zefang
Zhang, Tao
Ning, Caihong
Shen, Dingcheng
Pei, Wenwu
Zhou, Rui
Zhu, Shuai
Huang, Gengwen
author_sort Sun, Zefang
collection PubMed
description Background: Numerous studies have shown that pyroptosis is associated with sepsis progression, which can lead to dysregulated host immune responses and organ dysfunction. Therefore, investigating the potential prognostic and diagnostic values of pyroptosis in patients with sepsis is essential. Methods: We conducted a study using bulk and single-cell RNA sequencing (scRNA-seq) from the Gene Expression Omnibus database to examine the role of pyroptosis in sepsis. Univariate logistic analysis, least absolute shrinkage, and selection operator regression analysis were used to identify pyroptosis-related genes (PRGs), construct a diagnostic risk score model, and evaluate the selected genes' diagnostic value. Consensus clustering analysis was used to identify the PRG-related sepsis subtypes with varying prognoses. Functional and immune infiltration analyses were used to explain the subtypes' distinct prognoses, and scRNA-seq data were used to differentiate immune-infiltrating cells and macrophage subsets and study cell-cell communication. Results: A risk model was established based on 10 key PRGs (NAIP, ELANE, GSDMB, DHX9, NLRP3, CASP8, GSDMD, CASP4, APIP, and DPP9), of which four (ELANE, DHX9, GSDMD, and CASP4) were associated with prognosis. Two subtypes with different prognoses were identified based on the key PRG expressions. Functional enrichment analysis revealed diminished nucleotide oligomerization domain–like receptor pathway activity and enhanced neutrophil extracellular trap formation in the subtype with a poor prognosis. Immune infiltration analysis suggested a different immune status between the two sepsis subtypes, with the subtype with a poor prognosis exhibiting stronger immunosuppression. The single-cell analysis identified a macrophage subpopulation characterized by gasdermin D (GSDMD) expression that may be involved in pyroptosis regulation, which was associated with the prognosis of sepsis. Conclusion: We developed and validated a risk score for sepsis identification based on 10 PRGs, four of which also have potential value in the prognosis of sepsis. We identified a subset of gasdermin D macrophages associated with poor prognosis, providing new insights into the role of pyroptosis in sepsis.
format Online
Article
Text
id pubmed-10417255
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Lippincott Williams & Wilkins
record_format MEDLINE/PubMed
spelling pubmed-104172552023-08-12 CONSTRUCTION OF SEPSIS DIAGNOSTIC MODELS AND IDENTIFICATION OF MACROPHAGE SUBPOPULATIONS BASED ON PYROPTOSIS-RELATED GENES Sun, Zefang Zhang, Tao Ning, Caihong Shen, Dingcheng Pei, Wenwu Zhou, Rui Zhu, Shuai Huang, Gengwen Shock Clinical Sciences Background: Numerous studies have shown that pyroptosis is associated with sepsis progression, which can lead to dysregulated host immune responses and organ dysfunction. Therefore, investigating the potential prognostic and diagnostic values of pyroptosis in patients with sepsis is essential. Methods: We conducted a study using bulk and single-cell RNA sequencing (scRNA-seq) from the Gene Expression Omnibus database to examine the role of pyroptosis in sepsis. Univariate logistic analysis, least absolute shrinkage, and selection operator regression analysis were used to identify pyroptosis-related genes (PRGs), construct a diagnostic risk score model, and evaluate the selected genes' diagnostic value. Consensus clustering analysis was used to identify the PRG-related sepsis subtypes with varying prognoses. Functional and immune infiltration analyses were used to explain the subtypes' distinct prognoses, and scRNA-seq data were used to differentiate immune-infiltrating cells and macrophage subsets and study cell-cell communication. Results: A risk model was established based on 10 key PRGs (NAIP, ELANE, GSDMB, DHX9, NLRP3, CASP8, GSDMD, CASP4, APIP, and DPP9), of which four (ELANE, DHX9, GSDMD, and CASP4) were associated with prognosis. Two subtypes with different prognoses were identified based on the key PRG expressions. Functional enrichment analysis revealed diminished nucleotide oligomerization domain–like receptor pathway activity and enhanced neutrophil extracellular trap formation in the subtype with a poor prognosis. Immune infiltration analysis suggested a different immune status between the two sepsis subtypes, with the subtype with a poor prognosis exhibiting stronger immunosuppression. The single-cell analysis identified a macrophage subpopulation characterized by gasdermin D (GSDMD) expression that may be involved in pyroptosis regulation, which was associated with the prognosis of sepsis. Conclusion: We developed and validated a risk score for sepsis identification based on 10 PRGs, four of which also have potential value in the prognosis of sepsis. We identified a subset of gasdermin D macrophages associated with poor prognosis, providing new insights into the role of pyroptosis in sepsis. Lippincott Williams & Wilkins 2023-07 2023-05-16 /pmc/articles/PMC10417255/ /pubmed/37179249 http://dx.doi.org/10.1097/SHK.0000000000002137 Text en Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Shock Society. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Clinical Sciences
Sun, Zefang
Zhang, Tao
Ning, Caihong
Shen, Dingcheng
Pei, Wenwu
Zhou, Rui
Zhu, Shuai
Huang, Gengwen
CONSTRUCTION OF SEPSIS DIAGNOSTIC MODELS AND IDENTIFICATION OF MACROPHAGE SUBPOPULATIONS BASED ON PYROPTOSIS-RELATED GENES
title CONSTRUCTION OF SEPSIS DIAGNOSTIC MODELS AND IDENTIFICATION OF MACROPHAGE SUBPOPULATIONS BASED ON PYROPTOSIS-RELATED GENES
title_full CONSTRUCTION OF SEPSIS DIAGNOSTIC MODELS AND IDENTIFICATION OF MACROPHAGE SUBPOPULATIONS BASED ON PYROPTOSIS-RELATED GENES
title_fullStr CONSTRUCTION OF SEPSIS DIAGNOSTIC MODELS AND IDENTIFICATION OF MACROPHAGE SUBPOPULATIONS BASED ON PYROPTOSIS-RELATED GENES
title_full_unstemmed CONSTRUCTION OF SEPSIS DIAGNOSTIC MODELS AND IDENTIFICATION OF MACROPHAGE SUBPOPULATIONS BASED ON PYROPTOSIS-RELATED GENES
title_short CONSTRUCTION OF SEPSIS DIAGNOSTIC MODELS AND IDENTIFICATION OF MACROPHAGE SUBPOPULATIONS BASED ON PYROPTOSIS-RELATED GENES
title_sort construction of sepsis diagnostic models and identification of macrophage subpopulations based on pyroptosis-related genes
topic Clinical Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10417255/
https://www.ncbi.nlm.nih.gov/pubmed/37179249
http://dx.doi.org/10.1097/SHK.0000000000002137
work_keys_str_mv AT sunzefang constructionofsepsisdiagnosticmodelsandidentificationofmacrophagesubpopulationsbasedonpyroptosisrelatedgenes
AT zhangtao constructionofsepsisdiagnosticmodelsandidentificationofmacrophagesubpopulationsbasedonpyroptosisrelatedgenes
AT ningcaihong constructionofsepsisdiagnosticmodelsandidentificationofmacrophagesubpopulationsbasedonpyroptosisrelatedgenes
AT shendingcheng constructionofsepsisdiagnosticmodelsandidentificationofmacrophagesubpopulationsbasedonpyroptosisrelatedgenes
AT peiwenwu constructionofsepsisdiagnosticmodelsandidentificationofmacrophagesubpopulationsbasedonpyroptosisrelatedgenes
AT zhourui constructionofsepsisdiagnosticmodelsandidentificationofmacrophagesubpopulationsbasedonpyroptosisrelatedgenes
AT zhushuai constructionofsepsisdiagnosticmodelsandidentificationofmacrophagesubpopulationsbasedonpyroptosisrelatedgenes
AT huanggengwen constructionofsepsisdiagnosticmodelsandidentificationofmacrophagesubpopulationsbasedonpyroptosisrelatedgenes