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Identification of an Immune-Related Gene Diagnostic Model and Potential Drugs in Sepsis Using Bioinformatics and Pharmacogenomics Approaches

PURPOSE: Sepsis is an organ dysfunction with high mortality. Early identification, diagnosis, and effective treatment of sepsis are beneficial to the survival of patients. This study aimed to find potential diagnosis and immune-related genes, and drug targets, which could provide novel diagnostic an...

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Autores principales: Chen, Peng, Chen, Juan, Ye, Jinghe, Yang, Limin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473429/
https://www.ncbi.nlm.nih.gov/pubmed/37662976
http://dx.doi.org/10.2147/IDR.S418176
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author Chen, Peng
Chen, Juan
Ye, Jinghe
Yang, Limin
author_facet Chen, Peng
Chen, Juan
Ye, Jinghe
Yang, Limin
author_sort Chen, Peng
collection PubMed
description PURPOSE: Sepsis is an organ dysfunction with high mortality. Early identification, diagnosis, and effective treatment of sepsis are beneficial to the survival of patients. This study aimed to find potential diagnosis and immune-related genes, and drug targets, which could provide novel diagnostic and therapeutic markers for sepsis. PATIENTS AND METHODS: The GSE69063, GSE154918 and GSE28750 datasets were integrated to evaluate immune infiltration and identify differentially expressed genes (DEGs) and immune-related genes. Weighted gene co-expression network analysis (WGCNA) was applied to find the hub module related to immune score and sepsis. Immune-related key genes were screened out by taking interaction of DEGs, immune-related genes, and genes in hub module. Protein–protein interaction (PPI) analysis was used to further screen immune-related hub genes, followed by construction of a diagnostic model based on immune-related hub genes. Functional analysis and drug prediction of immune-related hub genes were, respectively, performed by David software and DGIdb database, followed by expression validation by reverse transcriptase polymerase chain reaction (RT-PCR). RESULTS: Totally, 93 immune-related key genes were identified between 561 DEGs, 1793 immune-related genes and 12,459 genes in the hub module of WGCNA. Through PPI analysis, a total of 5 diagnose and immune-related hub genes were further obtained, including IL7R, IL10, CD40LG, CD28 and LCN2. Relationship pairs between these 5 genes and immune cell were identified, including LCN2/IL7R/CD28-activated dendritic cell and IL10-immature B cell. Based on pharmacogenomics, 17 candidate drugs might interact with IL 10, including CYCLOSPORINE. Six candidate drugs might interact with CD28 and 11 with CD40LG, CD40LG and CD28 were drug targets of ALDESLEUKIN. Four significantly enriched signaling pathways were identified, such as T cell receptor signaling pathway, NF-kappa B signaling pathway and JAK-STAT signaling pathway. CONCLUSION: The 5-gene diagnostic model could be used to diagnose and guide clinical immunotherapy for sepsis.
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spelling pubmed-104734292023-09-02 Identification of an Immune-Related Gene Diagnostic Model and Potential Drugs in Sepsis Using Bioinformatics and Pharmacogenomics Approaches Chen, Peng Chen, Juan Ye, Jinghe Yang, Limin Infect Drug Resist Original Research PURPOSE: Sepsis is an organ dysfunction with high mortality. Early identification, diagnosis, and effective treatment of sepsis are beneficial to the survival of patients. This study aimed to find potential diagnosis and immune-related genes, and drug targets, which could provide novel diagnostic and therapeutic markers for sepsis. PATIENTS AND METHODS: The GSE69063, GSE154918 and GSE28750 datasets were integrated to evaluate immune infiltration and identify differentially expressed genes (DEGs) and immune-related genes. Weighted gene co-expression network analysis (WGCNA) was applied to find the hub module related to immune score and sepsis. Immune-related key genes were screened out by taking interaction of DEGs, immune-related genes, and genes in hub module. Protein–protein interaction (PPI) analysis was used to further screen immune-related hub genes, followed by construction of a diagnostic model based on immune-related hub genes. Functional analysis and drug prediction of immune-related hub genes were, respectively, performed by David software and DGIdb database, followed by expression validation by reverse transcriptase polymerase chain reaction (RT-PCR). RESULTS: Totally, 93 immune-related key genes were identified between 561 DEGs, 1793 immune-related genes and 12,459 genes in the hub module of WGCNA. Through PPI analysis, a total of 5 diagnose and immune-related hub genes were further obtained, including IL7R, IL10, CD40LG, CD28 and LCN2. Relationship pairs between these 5 genes and immune cell were identified, including LCN2/IL7R/CD28-activated dendritic cell and IL10-immature B cell. Based on pharmacogenomics, 17 candidate drugs might interact with IL 10, including CYCLOSPORINE. Six candidate drugs might interact with CD28 and 11 with CD40LG, CD40LG and CD28 were drug targets of ALDESLEUKIN. Four significantly enriched signaling pathways were identified, such as T cell receptor signaling pathway, NF-kappa B signaling pathway and JAK-STAT signaling pathway. CONCLUSION: The 5-gene diagnostic model could be used to diagnose and guide clinical immunotherapy for sepsis. Dove 2023-08-28 /pmc/articles/PMC10473429/ /pubmed/37662976 http://dx.doi.org/10.2147/IDR.S418176 Text en © 2023 Chen et al. https://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/ (https://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
Chen, Peng
Chen, Juan
Ye, Jinghe
Yang, Limin
Identification of an Immune-Related Gene Diagnostic Model and Potential Drugs in Sepsis Using Bioinformatics and Pharmacogenomics Approaches
title Identification of an Immune-Related Gene Diagnostic Model and Potential Drugs in Sepsis Using Bioinformatics and Pharmacogenomics Approaches
title_full Identification of an Immune-Related Gene Diagnostic Model and Potential Drugs in Sepsis Using Bioinformatics and Pharmacogenomics Approaches
title_fullStr Identification of an Immune-Related Gene Diagnostic Model and Potential Drugs in Sepsis Using Bioinformatics and Pharmacogenomics Approaches
title_full_unstemmed Identification of an Immune-Related Gene Diagnostic Model and Potential Drugs in Sepsis Using Bioinformatics and Pharmacogenomics Approaches
title_short Identification of an Immune-Related Gene Diagnostic Model and Potential Drugs in Sepsis Using Bioinformatics and Pharmacogenomics Approaches
title_sort identification of an immune-related gene diagnostic model and potential drugs in sepsis using bioinformatics and pharmacogenomics approaches
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473429/
https://www.ncbi.nlm.nih.gov/pubmed/37662976
http://dx.doi.org/10.2147/IDR.S418176
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