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Identification of Ferroptosis-Related Biomarkers for Diagnosis and Molecular Classification of Staphylococcus aureus-Induced Osteomyelitis

OBJECTIVE: Staphylococcus aureus (SA)-induced osteomyelitis (OM) is one of the most common refractory diseases in orthopedics. Early diagnosis is beneficial to improve the prognosis of patients. Ferroptosis plays a key role in inflammation and immune response, while the mechanism of ferroptosis-rela...

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Autores principales: Shi, Xiangwen, Tang, Linmeng, Ni, Haonan, Li, Mingjun, Wu, Yipeng, Xu, Yongqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10149083/
https://www.ncbi.nlm.nih.gov/pubmed/37131411
http://dx.doi.org/10.2147/JIR.S406562
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author Shi, Xiangwen
Tang, Linmeng
Ni, Haonan
Li, Mingjun
Wu, Yipeng
Xu, Yongqing
author_facet Shi, Xiangwen
Tang, Linmeng
Ni, Haonan
Li, Mingjun
Wu, Yipeng
Xu, Yongqing
author_sort Shi, Xiangwen
collection PubMed
description OBJECTIVE: Staphylococcus aureus (SA)-induced osteomyelitis (OM) is one of the most common refractory diseases in orthopedics. Early diagnosis is beneficial to improve the prognosis of patients. Ferroptosis plays a key role in inflammation and immune response, while the mechanism of ferroptosis-related genes (FRGs) in SA-induced OM is still unclear. The purpose of this study was to determine the role of ferroptosis-related genes in the diagnosis, molecular classification and immune infiltration of SA-induced OM by bioinformatics. METHODS: Datasets related to SA-induced OM and ferroptosis were collected from the Gene Expression Omnibus (GEO) and ferroptosis databases, respectively. The least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were combined to screen out differentially expressed-FRGs (DE-FRGs) with diagnostic characteristics, and gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to explore specific biological functions and pathways. Based on these key DE-FRGs, a diagnostic model was established, and molecular subtypes were divided to explore the changes in the immune microenvironment between molecular subtypes. RESULTS: A total of 41 DE-FRGs were identified. After screening and intersecting with LASSO and SVM-RFE algorithms, 8 key DE-FRGs with diagnostic characteristics were obtained, which may regulate the pathogenesis of OM through the immune response and amino acid metabolism. The ROC curve indicated that the 8 DE-FRGs had excellent diagnostic ability for SA-induced OM (AUC=0.993). Two different molecular subtypes (subtype 1 and subtype 2) were identified by unsupervised cluster analysis. The CIBERSORT analysis revealed that the subtype 1 OM had higher immune cell infiltration rates, mainly in T cells CD4 memory resting, macrophages M0, macrophages M2, dendritic cells resting, and dendritic cells activated. CONCLUSION: We developed a diagnostic model related to ferroptosis and molecular subtypes significantly related to immune infiltration, which may provide a novel insight for exploring the pathogenesis and immunotherapy of SA-induced OM.
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spelling pubmed-101490832023-05-01 Identification of Ferroptosis-Related Biomarkers for Diagnosis and Molecular Classification of Staphylococcus aureus-Induced Osteomyelitis Shi, Xiangwen Tang, Linmeng Ni, Haonan Li, Mingjun Wu, Yipeng Xu, Yongqing J Inflamm Res Original Research OBJECTIVE: Staphylococcus aureus (SA)-induced osteomyelitis (OM) is one of the most common refractory diseases in orthopedics. Early diagnosis is beneficial to improve the prognosis of patients. Ferroptosis plays a key role in inflammation and immune response, while the mechanism of ferroptosis-related genes (FRGs) in SA-induced OM is still unclear. The purpose of this study was to determine the role of ferroptosis-related genes in the diagnosis, molecular classification and immune infiltration of SA-induced OM by bioinformatics. METHODS: Datasets related to SA-induced OM and ferroptosis were collected from the Gene Expression Omnibus (GEO) and ferroptosis databases, respectively. The least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were combined to screen out differentially expressed-FRGs (DE-FRGs) with diagnostic characteristics, and gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to explore specific biological functions and pathways. Based on these key DE-FRGs, a diagnostic model was established, and molecular subtypes were divided to explore the changes in the immune microenvironment between molecular subtypes. RESULTS: A total of 41 DE-FRGs were identified. After screening and intersecting with LASSO and SVM-RFE algorithms, 8 key DE-FRGs with diagnostic characteristics were obtained, which may regulate the pathogenesis of OM through the immune response and amino acid metabolism. The ROC curve indicated that the 8 DE-FRGs had excellent diagnostic ability for SA-induced OM (AUC=0.993). Two different molecular subtypes (subtype 1 and subtype 2) were identified by unsupervised cluster analysis. The CIBERSORT analysis revealed that the subtype 1 OM had higher immune cell infiltration rates, mainly in T cells CD4 memory resting, macrophages M0, macrophages M2, dendritic cells resting, and dendritic cells activated. CONCLUSION: We developed a diagnostic model related to ferroptosis and molecular subtypes significantly related to immune infiltration, which may provide a novel insight for exploring the pathogenesis and immunotherapy of SA-induced OM. Dove 2023-04-26 /pmc/articles/PMC10149083/ /pubmed/37131411 http://dx.doi.org/10.2147/JIR.S406562 Text en © 2023 Shi 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
Shi, Xiangwen
Tang, Linmeng
Ni, Haonan
Li, Mingjun
Wu, Yipeng
Xu, Yongqing
Identification of Ferroptosis-Related Biomarkers for Diagnosis and Molecular Classification of Staphylococcus aureus-Induced Osteomyelitis
title Identification of Ferroptosis-Related Biomarkers for Diagnosis and Molecular Classification of Staphylococcus aureus-Induced Osteomyelitis
title_full Identification of Ferroptosis-Related Biomarkers for Diagnosis and Molecular Classification of Staphylococcus aureus-Induced Osteomyelitis
title_fullStr Identification of Ferroptosis-Related Biomarkers for Diagnosis and Molecular Classification of Staphylococcus aureus-Induced Osteomyelitis
title_full_unstemmed Identification of Ferroptosis-Related Biomarkers for Diagnosis and Molecular Classification of Staphylococcus aureus-Induced Osteomyelitis
title_short Identification of Ferroptosis-Related Biomarkers for Diagnosis and Molecular Classification of Staphylococcus aureus-Induced Osteomyelitis
title_sort identification of ferroptosis-related biomarkers for diagnosis and molecular classification of staphylococcus aureus-induced osteomyelitis
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10149083/
https://www.ncbi.nlm.nih.gov/pubmed/37131411
http://dx.doi.org/10.2147/JIR.S406562
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