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      Exploration of the combined role of immune checkpoints and immune cells in the diagnosis and treatment of ankylosing spondylitis: a preliminary study immune checkpoints in ankylosing spondylitis


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          Immune checkpoints have emerged as promising therapeutic targets for autoimmune diseases. However, the specific roles of immune checkpoints in the pathophysiology of ankylosing spondylitis (AS) remain unclear.


          Hip ligament samples were obtained from two patient groups: those with AS and femoral head deformity, and those with femoral head necrosis but without AS, undergoing hip arthroplasty. Label-Free Quantification (LFQ) Protein Park Analysis was used to identify the protein composition of the ligaments. Peripheral blood samples of 104 AS patients from public database were used to validate the expression of key proteins. KEGG, GO, and GSVA were employed to explore potential pathways regulated by immune checkpoints in AS progression. xCell was used to calculate cell infiltration levels, LASSO regression was applied to select key cells, and the correlation between immune checkpoints and immune cells was analyzed. Drug sensitivity analysis was conducted to identify potential therapeutic drugs targeting immune checkpoints in AS. The expression of key genes was validated through immunohistochemistry (IHC).


          HLA-DMB and HLA-DPA1 were downregulated in the ligaments of AS and this has been validated through peripheral blood datasets and IHC. Significant differences in expression were observed in CD8 + Tcm, CD8 + T cells, CD8 + Tem, osteoblasts, Th1 cells, and CD8 + naive T cells in AS. The infiltration levels of CD8 + Tcm and CD8 + naive T cells were significantly positively correlated with the expression levels of HLA-DMB and HLA-DPA1. Immune cell selection using LASSO regression showed good predictive ability for AS, with AUC values of 0.98, 0.81, and 0.75 for the three prediction models, respectively. Furthermore, this study found that HLA-DMB and HLA-DPA1 are involved in Th17 cell differentiation, and both Th17 cell differentiation and the NF-kappa B signaling pathway are activated in the AS group. Drug sensitivity analysis showed that AS patients are more sensitive to drugs such as doramapimod and GSK269962A.


          Immune checkpoints and immune cells could serve as avenues for exploring diagnostic and therapeutic strategies for AS.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13075-024-03341-6.

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          Most cited references39

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          WGCNA: an R package for weighted correlation network analysis

          Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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            oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data.

            Cell line drug screening datasets can be utilized for a range of different drug discovery applications from drug biomarker discovery to building translational models of drug response. Previously, we described three separate methodologies to (1) correct for general levels of drug sensitivity to enable drug-specific biomarker discovery, (2) predict clinical drug response in patients and (3) associate these predictions with clinical features to perform in vivo drug biomarker discovery. Here, we unite and update these methodologies into one R package (oncoPredict) to facilitate the development and adoption of these tools. This new OncoPredict R package can be applied to various in vitro and in vivo contexts for drug and biomarker discovery.
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              The interaction between signal regulatory protein alpha (SIRPα) and CD47: structure, function, and therapeutic target.

              CD47 is a broadly expressed membrane protein that interacts with the myeloid inhibitory immunoreceptor SIRPα (also termed CD172a or SHPS-1). SIRPα is the prototypic member of the SIRP paired receptor family of closely related SIRP proteins. Engagement of SIRPα by CD47 provides a downregulatory signal that inhibits host cell phagocytosis, and CD47 therefore functions as a "don't-eat-me" signal. Here, we discuss recent structural analysis of CD47-SIRPα interactions and implications of this for the function and evolution of SIRPα and paired receptors in general. Furthermore, we review the proposed roles of CD47-SIRPα interactions in phagocytosis, (auto)immunity, and host defense, as well as its potential significance as a therapeutic target in cancer and inflammation and for improving graft survival in xenotransplantation.

                Author and article information

                Arthritis Res Ther
                Arthritis Res Ther
                Arthritis Research & Therapy
                BioMed Central (London )
                4 June 2024
                4 June 2024
                : 26
                : 115
                [1 ]GRID grid.256607.0, ISNI 0000 0004 1798 2653, Department of Bone and Soft Tissue Surgery, , Guangxi Medical University Cancer Hospital, ; Nanning, Guangxi Zhuang Autonomous Region 530021 China
                [2 ]GRID grid.256607.0, ISNI 0000 0004 1798 2653, Guangxi Medical University, ; Nanning, Guangxi Zhuang Autonomous Region 530021 China
                [3 ]GRID grid.256607.0, ISNI 0000 0004 1798 2653, Department of Radiation Oncology, , Guangxi Medical University Cancer Hospital, ; Nanning, Guangxi Zhuang Autonomous Region 530021 China
                [4 ]GRID grid.412594.f, ISNI 0000 0004 1757 2961, Spine and Osteopathy Ward, , The First Affiliated Hospital of Guangxi Medical University, ; Nanning, Guangxi Zhuang Autonomous Region 530021 China
                [5 ]GRID grid.412594.f, ISNI 0000 0004 1757 2961, Guangxi Key Laboratory of Regenerative Medicine, Orthopaedic Department, , The First Affiliated Hospital of Guangxi Medical University, ; Nanning, Guangxi Zhuang Autonomous Region 530021 China
                © The Author(s) 2024

                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/. The Creative Commons Public Domain Dedication waiver ( http://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.

                : 16 January 2024
                : 12 May 2024
                Funded by: Guangxi Natural Science Foundation
                Award ID: No. 2023GXNSFBA026238
                Funded by: Guangxi Postdoctoral Special Foundation Project
                Funded by: Guangxi Zhuang Autonomous Region Health Commission Self-funded Research Project
                Award ID: No. Z-A20230715
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                © BioMed Central Ltd., part of Springer Nature 2024

                immune checkpoints,ankylosing spondylitis,immune cell,drug sensitivity,proteomic sequencing


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