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Identification and validation of key biomarkers based on RNA methylation genes in sepsis

BACKGROUND: RNA methylation is closely involved in immune regulation, but its role in sepsis remains unknown. Here, we aim to investigate the role of RNA methylation-associated genes (RMGs) in classifying and diagnosing of sepsis. METHODS: Five types of RMGs (m1A, m5C, m6Am, m7G and Ψ) were used to...

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Autores principales: Zhang, Qianqian, Bao, Xiaowei, Cui, Mintian, Wang, Chunxue, Ji, Jinlu, Jing, Jiongjie, Zhou, Xiaohui, Chen, Kun, Tang, Lunxian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10493392/
https://www.ncbi.nlm.nih.gov/pubmed/37701433
http://dx.doi.org/10.3389/fimmu.2023.1231898
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author Zhang, Qianqian
Bao, Xiaowei
Cui, Mintian
Wang, Chunxue
Ji, Jinlu
Jing, Jiongjie
Zhou, Xiaohui
Chen, Kun
Tang, Lunxian
author_facet Zhang, Qianqian
Bao, Xiaowei
Cui, Mintian
Wang, Chunxue
Ji, Jinlu
Jing, Jiongjie
Zhou, Xiaohui
Chen, Kun
Tang, Lunxian
author_sort Zhang, Qianqian
collection PubMed
description BACKGROUND: RNA methylation is closely involved in immune regulation, but its role in sepsis remains unknown. Here, we aim to investigate the role of RNA methylation-associated genes (RMGs) in classifying and diagnosing of sepsis. METHODS: Five types of RMGs (m1A, m5C, m6Am, m7G and Ψ) were used to identify sepsis subgroups based on gene expression profile data obtained from the GEO database (GSE57065, GSE65682, and GSE95233). Unsupervised clustering analysis was used to identify distinct RNA modification subtypes. The CIBERSORT, WGCNA, GO and KEGG analysis were performed to explore immune infiltration pattern and biological function of each cluster. RF, SVM, XGB, and GLM algorithm were applied to identify the diagnostic RMGs in sepsis. Finally, the expression levels of the five key RMGs were verified by collecting PBMCs from septic patients using qRT-PCR, and their diagnostic efficacy for sepsis was verified in combination with clinical data using ROC analysis. RESULTS: Sepsis was divided into three subtypes (cluster 1 to 3). Cluster 1 highly expressed NSUN7 and TRMT6, with the characteristic of neutrophil activation and upregulation of MAPK signaling pathways. Cluster 2 highly expressed NSUN3, and was featured by the regulation of mRNA stability and amino acid metabolism. NSUN5 and NSUN6 were upregulated in cluster 3 which was involved in ribonucleoprotein complex biogenesis and carbohydrate metabolism pathways. In addition, we identified that five RMGs (NSUN7, NOP2, PUS1, PUS3 and FTO) could function as biomarkers for clinic diagnose of sepsis. For validation, we determined that the relative expressions of NSUN7, NOP2, PUS1 and PUS3 were upregulated, while FTO was downregulated in septic patients. The area under the ROC curve (AUC) of NSUN7, NOP2, PUS1, PUS3 and FTO was 0.828, 0.707, 0.846, 0.834 and 0.976, respectively. CONCLUSIONS: Our study uncovered that dysregulation of RNA methylation genes (m1A, m5C, m6Am, m7G and Ψ) was closely involved in the pathogenesis of sepsis, providing new insights into the classification of sepsis endotypes. We also revealed that five hub RMGs could function as novel diagnostic biomarkers and potential targets for treatment.
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spelling pubmed-104933922023-09-12 Identification and validation of key biomarkers based on RNA methylation genes in sepsis Zhang, Qianqian Bao, Xiaowei Cui, Mintian Wang, Chunxue Ji, Jinlu Jing, Jiongjie Zhou, Xiaohui Chen, Kun Tang, Lunxian Front Immunol Immunology BACKGROUND: RNA methylation is closely involved in immune regulation, but its role in sepsis remains unknown. Here, we aim to investigate the role of RNA methylation-associated genes (RMGs) in classifying and diagnosing of sepsis. METHODS: Five types of RMGs (m1A, m5C, m6Am, m7G and Ψ) were used to identify sepsis subgroups based on gene expression profile data obtained from the GEO database (GSE57065, GSE65682, and GSE95233). Unsupervised clustering analysis was used to identify distinct RNA modification subtypes. The CIBERSORT, WGCNA, GO and KEGG analysis were performed to explore immune infiltration pattern and biological function of each cluster. RF, SVM, XGB, and GLM algorithm were applied to identify the diagnostic RMGs in sepsis. Finally, the expression levels of the five key RMGs were verified by collecting PBMCs from septic patients using qRT-PCR, and their diagnostic efficacy for sepsis was verified in combination with clinical data using ROC analysis. RESULTS: Sepsis was divided into three subtypes (cluster 1 to 3). Cluster 1 highly expressed NSUN7 and TRMT6, with the characteristic of neutrophil activation and upregulation of MAPK signaling pathways. Cluster 2 highly expressed NSUN3, and was featured by the regulation of mRNA stability and amino acid metabolism. NSUN5 and NSUN6 were upregulated in cluster 3 which was involved in ribonucleoprotein complex biogenesis and carbohydrate metabolism pathways. In addition, we identified that five RMGs (NSUN7, NOP2, PUS1, PUS3 and FTO) could function as biomarkers for clinic diagnose of sepsis. For validation, we determined that the relative expressions of NSUN7, NOP2, PUS1 and PUS3 were upregulated, while FTO was downregulated in septic patients. The area under the ROC curve (AUC) of NSUN7, NOP2, PUS1, PUS3 and FTO was 0.828, 0.707, 0.846, 0.834 and 0.976, respectively. CONCLUSIONS: Our study uncovered that dysregulation of RNA methylation genes (m1A, m5C, m6Am, m7G and Ψ) was closely involved in the pathogenesis of sepsis, providing new insights into the classification of sepsis endotypes. We also revealed that five hub RMGs could function as novel diagnostic biomarkers and potential targets for treatment. Frontiers Media S.A. 2023-08-28 /pmc/articles/PMC10493392/ /pubmed/37701433 http://dx.doi.org/10.3389/fimmu.2023.1231898 Text en Copyright © 2023 Zhang, Bao, Cui, Wang, Ji, Jing, Zhou, Chen and Tang 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
Zhang, Qianqian
Bao, Xiaowei
Cui, Mintian
Wang, Chunxue
Ji, Jinlu
Jing, Jiongjie
Zhou, Xiaohui
Chen, Kun
Tang, Lunxian
Identification and validation of key biomarkers based on RNA methylation genes in sepsis
title Identification and validation of key biomarkers based on RNA methylation genes in sepsis
title_full Identification and validation of key biomarkers based on RNA methylation genes in sepsis
title_fullStr Identification and validation of key biomarkers based on RNA methylation genes in sepsis
title_full_unstemmed Identification and validation of key biomarkers based on RNA methylation genes in sepsis
title_short Identification and validation of key biomarkers based on RNA methylation genes in sepsis
title_sort identification and validation of key biomarkers based on rna methylation genes in sepsis
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10493392/
https://www.ncbi.nlm.nih.gov/pubmed/37701433
http://dx.doi.org/10.3389/fimmu.2023.1231898
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