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Micro-inflammation related gene signatures are associated with clinical features and immune status of fibromyalgia

BACKGROUND: Fibromyalgia (FM) is a multifaceted disease. Along with the genetic, environmental and neuro-hormonal factors, inflammation has been assumed to have role in the pathogenesis of FM. The aim of the present study was to explore the differences in clinical features and pathophysiology of FM...

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Autores principales: Yao, Menghui, Wang, Shuolin, Han, Yingdong, Zhao, He, Yin, Yue, Zhang, Yun, Zeng, Xuejun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10478377/
https://www.ncbi.nlm.nih.gov/pubmed/37670381
http://dx.doi.org/10.1186/s12967-023-04477-w
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author Yao, Menghui
Wang, Shuolin
Han, Yingdong
Zhao, He
Yin, Yue
Zhang, Yun
Zeng, Xuejun
author_facet Yao, Menghui
Wang, Shuolin
Han, Yingdong
Zhao, He
Yin, Yue
Zhang, Yun
Zeng, Xuejun
author_sort Yao, Menghui
collection PubMed
description BACKGROUND: Fibromyalgia (FM) is a multifaceted disease. Along with the genetic, environmental and neuro-hormonal factors, inflammation has been assumed to have role in the pathogenesis of FM. The aim of the present study was to explore the differences in clinical features and pathophysiology of FM patients under different inflammatory status. METHODS: The peripheral blood gene expression profile of FM patients in the Gene Expression Omnibus database was downloaded. Differentially expressed inflammatory genes were identified, and two molecular subtypes were constructed according to these genes used unsupervised clustering analysis. The clinical characteristics, immune features and pathways activities were compared further between the two subtypes. Then machine learning was used to perform the feature selection and construct a classification model. RESULTS: The patients with FM were divided into micro-inflammation and non-inflammation subtypes according to 54 differentially expressed inflammatory genes. The micro-inflammation group was characterized by more major depression (p = 0.049), higher BMI (p = 0.021), more active dendritic cells (p = 0.010) and neutrophils. Functional enrichment analysis showed that innate immune response and antibacterial response were significantly enriched in micro-inflammation subtype (p < 0.050). Then 5 hub genes (MMP8, ENPP3, MAP2K3, HGF, YES1) were screened thought three feature selection algorithms, an accurate classifier based on the 5 hub DEIGs and 2 clinical parameters were constructed using support vector machine model. Model scoring indicators such as AUC (0.945), accuracy (0.936), F1 score (0.941), Brier score (0.079) and Hosmer–Lemeshow goodness-of-fit test (χ(2) = 4.274, p = 0.832) proved that this SVM-based classifier was highly reliable. CONCLUSION: Micro-inflammation status in FM was significantly associated with the occurrence of depression and activated innate immune response. Our study calls attention to the pathogenesis of different subtypes of FM. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-04477-w.
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spelling pubmed-104783772023-09-06 Micro-inflammation related gene signatures are associated with clinical features and immune status of fibromyalgia Yao, Menghui Wang, Shuolin Han, Yingdong Zhao, He Yin, Yue Zhang, Yun Zeng, Xuejun J Transl Med Research BACKGROUND: Fibromyalgia (FM) is a multifaceted disease. Along with the genetic, environmental and neuro-hormonal factors, inflammation has been assumed to have role in the pathogenesis of FM. The aim of the present study was to explore the differences in clinical features and pathophysiology of FM patients under different inflammatory status. METHODS: The peripheral blood gene expression profile of FM patients in the Gene Expression Omnibus database was downloaded. Differentially expressed inflammatory genes were identified, and two molecular subtypes were constructed according to these genes used unsupervised clustering analysis. The clinical characteristics, immune features and pathways activities were compared further between the two subtypes. Then machine learning was used to perform the feature selection and construct a classification model. RESULTS: The patients with FM were divided into micro-inflammation and non-inflammation subtypes according to 54 differentially expressed inflammatory genes. The micro-inflammation group was characterized by more major depression (p = 0.049), higher BMI (p = 0.021), more active dendritic cells (p = 0.010) and neutrophils. Functional enrichment analysis showed that innate immune response and antibacterial response were significantly enriched in micro-inflammation subtype (p < 0.050). Then 5 hub genes (MMP8, ENPP3, MAP2K3, HGF, YES1) were screened thought three feature selection algorithms, an accurate classifier based on the 5 hub DEIGs and 2 clinical parameters were constructed using support vector machine model. Model scoring indicators such as AUC (0.945), accuracy (0.936), F1 score (0.941), Brier score (0.079) and Hosmer–Lemeshow goodness-of-fit test (χ(2) = 4.274, p = 0.832) proved that this SVM-based classifier was highly reliable. CONCLUSION: Micro-inflammation status in FM was significantly associated with the occurrence of depression and activated innate immune response. Our study calls attention to the pathogenesis of different subtypes of FM. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-04477-w. BioMed Central 2023-09-05 /pmc/articles/PMC10478377/ /pubmed/37670381 http://dx.doi.org/10.1186/s12967-023-04477-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Yao, Menghui
Wang, Shuolin
Han, Yingdong
Zhao, He
Yin, Yue
Zhang, Yun
Zeng, Xuejun
Micro-inflammation related gene signatures are associated with clinical features and immune status of fibromyalgia
title Micro-inflammation related gene signatures are associated with clinical features and immune status of fibromyalgia
title_full Micro-inflammation related gene signatures are associated with clinical features and immune status of fibromyalgia
title_fullStr Micro-inflammation related gene signatures are associated with clinical features and immune status of fibromyalgia
title_full_unstemmed Micro-inflammation related gene signatures are associated with clinical features and immune status of fibromyalgia
title_short Micro-inflammation related gene signatures are associated with clinical features and immune status of fibromyalgia
title_sort micro-inflammation related gene signatures are associated with clinical features and immune status of fibromyalgia
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10478377/
https://www.ncbi.nlm.nih.gov/pubmed/37670381
http://dx.doi.org/10.1186/s12967-023-04477-w
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