Deciphering hub genes and immune landscapes related to neutrophil extracellular traps in rheumatoid arthritis: insights from integrated bioinformatics analyses and experiments
BackgroundRheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial inflammation and progressive joint destruction. Neutrophil extracellular traps (NETs), a microreticular structure formed after neutrophil death, have recently been implicated in RA pathogenesis and patholog...
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Frontiers Media S.A.
2025-01-01
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| author | Yang Li Yang Li Jian Liu Jian Liu Yue Sun Yue Sun Yuedi Hu Yuedi Hu Qiao Zhou Qiao Zhou Chengzhi Cong Chengzhi Cong Yiming Chen Yiming Chen |
| author_facet | Yang Li Yang Li Jian Liu Jian Liu Yue Sun Yue Sun Yuedi Hu Yuedi Hu Qiao Zhou Qiao Zhou Chengzhi Cong Chengzhi Cong Yiming Chen Yiming Chen |
| author_sort | Yang Li |
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| description | BackgroundRheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial inflammation and progressive joint destruction. Neutrophil extracellular traps (NETs), a microreticular structure formed after neutrophil death, have recently been implicated in RA pathogenesis and pathological mechanisms. However, the underlying molecular mechanisms and key genes involved in NET formation in RA remain largely unknown.MethodsWe obtained single-cell RNA sequencing data of synovial tissues from the Gene Expression Omnibus (GEO) database and performed cellular annotation and intercellular communication analyses. Subsequently, three microarray datasets were collected for a training cohort and correlated with a bulk RNA-seq dataset associated with NETs. Differentially expressed genes were identified, and weighted gene correlation network analysis was used to characterize gene association. Using three machine learning techniques, we identified the most important hub genes to develop and evaluate a nomogram diagnostic model. CIBERSORT was used to elucidate the relationship between hub genes and immune cells. An external validation dataset was used to verify pivotal gene expression and to construct co-regulatory networks using the NetworkAnalyst platform. We further investigated hub gene expression using immunohistochemistry (IHC) in an adjuvant-induced arthritis rat model and real-time quantitative polymerase chain reaction (RT-qPCR) in a clinical cohort.ResultsSeven cellular subpopulations were identified through downscaling and clustering, with neutrophils likely the most crucial cell clusters in RA. Intercellular communication analysis highlighted the network between neutrophils and fibroblasts. In this context, 4 key hub genes (CRYBG1, RMM2, MMP1, and SLC19A2) associated with NETs were identified. A nomogram model with a diagnostic value was developed and evaluated. Immune cell infiltration analysis indicated associations between the hub genes and the immune landscape in NETs and RA. IHC and RT-qPCR findings showed high expression of CRYBG1, RMM2, and MMP1 in synovial and neutrophilic cells, with lower expression of SLC19A2. Correlation analysis further emphasized close associations between hub genes and laboratory markers in patients with RA.ConclusionThis study first elucidated neutrophil heterogeneity in the RA synovial microenvironment and mechanisms of communication with fibroblasts. CRYBG1, RMM2, MMP1, and SLC19A2 were identified and validated as potential NET-associated biomarkers, offering insights for diagnostic tools and immunotherapeutic strategies in RA. |
| format | Article |
| id | doaj-art-f8e97e439cf24ec781ecb0e3ce953f4a |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
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| series | Frontiers in Immunology |
| spelling | doaj-art-f8e97e439cf24ec781ecb0e3ce953f4a2025-01-08T05:10:26ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011510.3389/fimmu.2024.15216341521634Deciphering hub genes and immune landscapes related to neutrophil extracellular traps in rheumatoid arthritis: insights from integrated bioinformatics analyses and experimentsYang Li0Yang Li1Jian Liu2Jian Liu3Yue Sun4Yue Sun5Yuedi Hu6Yuedi Hu7Qiao Zhou8Qiao Zhou9Chengzhi Cong10Chengzhi Cong11Yiming Chen12Yiming Chen13Department of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, ChinaFirst Clinical Medical School, Anhui University of Chinese Medicine, Hefei, Anhui, ChinaDepartment of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, ChinaInstitute of Rheumatology, Anhui Academy of Chinese Medicine, Hefei, Anhui, ChinaDepartment of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, ChinaInstitute of Rheumatology, Anhui Academy of Chinese Medicine, Hefei, Anhui, ChinaDepartment of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, ChinaFirst Clinical Medical School, Anhui University of Chinese Medicine, Hefei, Anhui, ChinaDepartment of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, ChinaFirst Clinical Medical School, Anhui University of Chinese Medicine, Hefei, Anhui, ChinaDepartment of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, ChinaFirst Clinical Medical School, Anhui University of Chinese Medicine, Hefei, Anhui, ChinaDepartment of Rheumatology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, Anhui, ChinaFirst Clinical Medical School, Anhui University of Chinese Medicine, Hefei, Anhui, ChinaBackgroundRheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial inflammation and progressive joint destruction. Neutrophil extracellular traps (NETs), a microreticular structure formed after neutrophil death, have recently been implicated in RA pathogenesis and pathological mechanisms. However, the underlying molecular mechanisms and key genes involved in NET formation in RA remain largely unknown.MethodsWe obtained single-cell RNA sequencing data of synovial tissues from the Gene Expression Omnibus (GEO) database and performed cellular annotation and intercellular communication analyses. Subsequently, three microarray datasets were collected for a training cohort and correlated with a bulk RNA-seq dataset associated with NETs. Differentially expressed genes were identified, and weighted gene correlation network analysis was used to characterize gene association. Using three machine learning techniques, we identified the most important hub genes to develop and evaluate a nomogram diagnostic model. CIBERSORT was used to elucidate the relationship between hub genes and immune cells. An external validation dataset was used to verify pivotal gene expression and to construct co-regulatory networks using the NetworkAnalyst platform. We further investigated hub gene expression using immunohistochemistry (IHC) in an adjuvant-induced arthritis rat model and real-time quantitative polymerase chain reaction (RT-qPCR) in a clinical cohort.ResultsSeven cellular subpopulations were identified through downscaling and clustering, with neutrophils likely the most crucial cell clusters in RA. Intercellular communication analysis highlighted the network between neutrophils and fibroblasts. In this context, 4 key hub genes (CRYBG1, RMM2, MMP1, and SLC19A2) associated with NETs were identified. A nomogram model with a diagnostic value was developed and evaluated. Immune cell infiltration analysis indicated associations between the hub genes and the immune landscape in NETs and RA. IHC and RT-qPCR findings showed high expression of CRYBG1, RMM2, and MMP1 in synovial and neutrophilic cells, with lower expression of SLC19A2. Correlation analysis further emphasized close associations between hub genes and laboratory markers in patients with RA.ConclusionThis study first elucidated neutrophil heterogeneity in the RA synovial microenvironment and mechanisms of communication with fibroblasts. CRYBG1, RMM2, MMP1, and SLC19A2 were identified and validated as potential NET-associated biomarkers, offering insights for diagnostic tools and immunotherapeutic strategies in RA.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1521634/fullrheumatoid arthritisneutrophil extracellular trapsneutrophilssynovial microenvironmentinflammationsystems bioinformatics |
| spellingShingle | Yang Li Yang Li Jian Liu Jian Liu Yue Sun Yue Sun Yuedi Hu Yuedi Hu Qiao Zhou Qiao Zhou Chengzhi Cong Chengzhi Cong Yiming Chen Yiming Chen Deciphering hub genes and immune landscapes related to neutrophil extracellular traps in rheumatoid arthritis: insights from integrated bioinformatics analyses and experiments Frontiers in Immunology rheumatoid arthritis neutrophil extracellular traps neutrophils synovial microenvironment inflammation systems bioinformatics |
| title | Deciphering hub genes and immune landscapes related to neutrophil extracellular traps in rheumatoid arthritis: insights from integrated bioinformatics analyses and experiments |
| title_full | Deciphering hub genes and immune landscapes related to neutrophil extracellular traps in rheumatoid arthritis: insights from integrated bioinformatics analyses and experiments |
| title_fullStr | Deciphering hub genes and immune landscapes related to neutrophil extracellular traps in rheumatoid arthritis: insights from integrated bioinformatics analyses and experiments |
| title_full_unstemmed | Deciphering hub genes and immune landscapes related to neutrophil extracellular traps in rheumatoid arthritis: insights from integrated bioinformatics analyses and experiments |
| title_short | Deciphering hub genes and immune landscapes related to neutrophil extracellular traps in rheumatoid arthritis: insights from integrated bioinformatics analyses and experiments |
| title_sort | deciphering hub genes and immune landscapes related to neutrophil extracellular traps in rheumatoid arthritis insights from integrated bioinformatics analyses and experiments |
| topic | rheumatoid arthritis neutrophil extracellular traps neutrophils synovial microenvironment inflammation systems bioinformatics |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1521634/full |
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