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An immune genes signature for predicting mortality in sepsis patients

A growing body of evidence indicates that the immune system plays a central role in sepsis. By analyzing immune genes, we sought to establish a robust gene signature and develop a nomogram that could predict mortality in patients with sepsis. Herein, data were extracted from the Gene Expression Omni...

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Autores principales: Lin, Shirong, Li, Ping, Yang, Jibin, Liu, Shiwen, Huang, Shaofang, Huang, Ziyan, Zhou, Congyang, Liu, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9968838/
https://www.ncbi.nlm.nih.gov/pubmed/36860871
http://dx.doi.org/10.3389/fimmu.2023.1000431
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author Lin, Shirong
Li, Ping
Yang, Jibin
Liu, Shiwen
Huang, Shaofang
Huang, Ziyan
Zhou, Congyang
Liu, Ying
author_facet Lin, Shirong
Li, Ping
Yang, Jibin
Liu, Shiwen
Huang, Shaofang
Huang, Ziyan
Zhou, Congyang
Liu, Ying
author_sort Lin, Shirong
collection PubMed
description A growing body of evidence indicates that the immune system plays a central role in sepsis. By analyzing immune genes, we sought to establish a robust gene signature and develop a nomogram that could predict mortality in patients with sepsis. Herein, data were extracted from the Gene Expression Omnibus and Biological Information Database of Sepsis (BIDOS) databases. We enrolled 479 participants with complete survival data using the GSE65682 dataset, and grouped them randomly into training (n = 240) and internal validation (n = 239) sets based on a 1:1 proportion. GSE95233 was set as the external validation dataset (n=51). We validated the expression and prognostic value of the immune genes using the BIDOS database. We established a prognostic immune genes signature (including ADRB2, CTSG, CX3CR1, CXCR6, IL4R, LTB, and TMSB10) via LASSO and Cox regression analyses in the training set. Based on the training and validation sets, the Receiver Operating Characteristic curves and Kaplan-Meier analysis revealed that the immune risk signature has good predictive power in predicting sepsis mortality risk. The external validation cases also showed that mortality rates in the high-risk group were higher than those in the low-risk group. Subsequently, a nomogram integrating the combined immune risk score and other clinical features was developed. Finally, a web-based calculator was built to facilitate a convenient clinical application of the nomogram. In summary, the signature based on the immune gene holds potential as a novel prognostic predictor for sepsis.
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spelling pubmed-99688382023-02-28 An immune genes signature for predicting mortality in sepsis patients Lin, Shirong Li, Ping Yang, Jibin Liu, Shiwen Huang, Shaofang Huang, Ziyan Zhou, Congyang Liu, Ying Front Immunol Immunology A growing body of evidence indicates that the immune system plays a central role in sepsis. By analyzing immune genes, we sought to establish a robust gene signature and develop a nomogram that could predict mortality in patients with sepsis. Herein, data were extracted from the Gene Expression Omnibus and Biological Information Database of Sepsis (BIDOS) databases. We enrolled 479 participants with complete survival data using the GSE65682 dataset, and grouped them randomly into training (n = 240) and internal validation (n = 239) sets based on a 1:1 proportion. GSE95233 was set as the external validation dataset (n=51). We validated the expression and prognostic value of the immune genes using the BIDOS database. We established a prognostic immune genes signature (including ADRB2, CTSG, CX3CR1, CXCR6, IL4R, LTB, and TMSB10) via LASSO and Cox regression analyses in the training set. Based on the training and validation sets, the Receiver Operating Characteristic curves and Kaplan-Meier analysis revealed that the immune risk signature has good predictive power in predicting sepsis mortality risk. The external validation cases also showed that mortality rates in the high-risk group were higher than those in the low-risk group. Subsequently, a nomogram integrating the combined immune risk score and other clinical features was developed. Finally, a web-based calculator was built to facilitate a convenient clinical application of the nomogram. In summary, the signature based on the immune gene holds potential as a novel prognostic predictor for sepsis. Frontiers Media S.A. 2023-02-13 /pmc/articles/PMC9968838/ /pubmed/36860871 http://dx.doi.org/10.3389/fimmu.2023.1000431 Text en Copyright © 2023 Lin, Li, Yang, Liu, Huang, Huang, Zhou and Liu 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
Lin, Shirong
Li, Ping
Yang, Jibin
Liu, Shiwen
Huang, Shaofang
Huang, Ziyan
Zhou, Congyang
Liu, Ying
An immune genes signature for predicting mortality in sepsis patients
title An immune genes signature for predicting mortality in sepsis patients
title_full An immune genes signature for predicting mortality in sepsis patients
title_fullStr An immune genes signature for predicting mortality in sepsis patients
title_full_unstemmed An immune genes signature for predicting mortality in sepsis patients
title_short An immune genes signature for predicting mortality in sepsis patients
title_sort immune genes signature for predicting mortality in sepsis patients
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9968838/
https://www.ncbi.nlm.nih.gov/pubmed/36860871
http://dx.doi.org/10.3389/fimmu.2023.1000431
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