A Predictive Disease Risk Model for Ankylosing Spondylitis: Based on Integrated Bioinformatic Analysis and Identification of Potential Biomarkers Most Related to Immunity
Background. The pathogenesis of ankylosing spondylitis (AS) is still not clear, and immune-related genes have not been systematically explored in AS. The purpose of this paper was to identify the potential early biomarkers most related to immunity in AS and develop a predictive disease risk model wi...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2023-01-01
|
| Series: | Mediators of Inflammation |
| Online Access: | http://dx.doi.org/10.1155/2023/3220235 |
| _version_ | 1827000308118585344 |
|---|---|
| author | Wenxin Gao Ruirui Hou Yungang Chen Xiaoying Wang Guoyan Liu Wanli Hu Kang Yao Yanke Hao |
| author_facet | Wenxin Gao Ruirui Hou Yungang Chen Xiaoying Wang Guoyan Liu Wanli Hu Kang Yao Yanke Hao |
| author_sort | Wenxin Gao |
| collection | DOAJ |
| description | Background. The pathogenesis of ankylosing spondylitis (AS) is still not clear, and immune-related genes have not been systematically explored in AS. The purpose of this paper was to identify the potential early biomarkers most related to immunity in AS and develop a predictive disease risk model with bioinformatic methods and the Gene Expression Omnibus database (GEO) to improve diagnostic and therapeutic efficiency. Methods. To identify differentially expressed genes and create a gene coexpression network between AS and healthy samples, we downloaded the AS-related datasets GSE25101 and GSE73754 from the GEO database and employed weighted gene coexpression network analysis (WGCNA). We used the GSVA, GSEABase, limma, ggpubr, and reshape2 packages to score immune data and investigated the links between immune cells and immunological functions by using single-sample gene set enrichment analysis (ssGSEA). The value of the core gene set and constructed model for early AS diagnosis was investigated by using receiver operating characteristic (ROC) curve analysis. Results. Biological function and immune score analyses identified central genes related to immunity, key immune cells, key related pathways, gene modules, and the coexpression network in AS. Granulysin (GNLY), Granulysin (GZMK), CX3CR1, IL2RB, dysferlin (DYSF), and S100A12 may participate in AS development through NK cells, CD8+ T cells, Th1 cells, and other immune cells and represent potential biomarkers for the early diagnosis of AS occurrence and progression. Furthermore, the T cell coinhibitory pathway may be involved in AS pathogenesis. Conclusion. The AS disease risk model constructed based on immune-related genes can guide clinical diagnosis and treatment and may help in the development of personalized immunotherapy. |
| first_indexed | 2024-04-09T14:21:07Z |
| format | Article |
| id | doaj.art-d7e8bfe4f7564a1997a2db7fe59e70df |
| institution | Directory Open Access Journal |
| issn | 1466-1861 |
| language | English |
| last_indexed | 2025-02-18T10:43:13Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi Limited |
| record_format | Article |
| series | Mediators of Inflammation |
| spelling | doaj.art-d7e8bfe4f7564a1997a2db7fe59e70df2024-11-02T05:27:59ZengHindawi LimitedMediators of Inflammation1466-18612023-01-01202310.1155/2023/3220235A Predictive Disease Risk Model for Ankylosing Spondylitis: Based on Integrated Bioinformatic Analysis and Identification of Potential Biomarkers Most Related to ImmunityWenxin Gao0Ruirui Hou1Yungang Chen2Xiaoying Wang3Guoyan Liu4Wanli Hu5Kang Yao6Yanke Hao7Shandong University of Traditional Chinese MedicineShandong University of Traditional Chinese MedicineShandong University of Traditional Chinese MedicineJinan Vocational College of NursingAffiliated Hospital of Shandong University of Traditional Chinese MedicineThe Second Affiliated Hospital of Shandong University of Traditional Chinese MedicineShandong University of Traditional Chinese MedicineAffiliated Hospital of Shandong University of Traditional Chinese MedicineBackground. The pathogenesis of ankylosing spondylitis (AS) is still not clear, and immune-related genes have not been systematically explored in AS. The purpose of this paper was to identify the potential early biomarkers most related to immunity in AS and develop a predictive disease risk model with bioinformatic methods and the Gene Expression Omnibus database (GEO) to improve diagnostic and therapeutic efficiency. Methods. To identify differentially expressed genes and create a gene coexpression network between AS and healthy samples, we downloaded the AS-related datasets GSE25101 and GSE73754 from the GEO database and employed weighted gene coexpression network analysis (WGCNA). We used the GSVA, GSEABase, limma, ggpubr, and reshape2 packages to score immune data and investigated the links between immune cells and immunological functions by using single-sample gene set enrichment analysis (ssGSEA). The value of the core gene set and constructed model for early AS diagnosis was investigated by using receiver operating characteristic (ROC) curve analysis. Results. Biological function and immune score analyses identified central genes related to immunity, key immune cells, key related pathways, gene modules, and the coexpression network in AS. Granulysin (GNLY), Granulysin (GZMK), CX3CR1, IL2RB, dysferlin (DYSF), and S100A12 may participate in AS development through NK cells, CD8+ T cells, Th1 cells, and other immune cells and represent potential biomarkers for the early diagnosis of AS occurrence and progression. Furthermore, the T cell coinhibitory pathway may be involved in AS pathogenesis. Conclusion. The AS disease risk model constructed based on immune-related genes can guide clinical diagnosis and treatment and may help in the development of personalized immunotherapy.http://dx.doi.org/10.1155/2023/3220235 |
| spellingShingle | Wenxin Gao Ruirui Hou Yungang Chen Xiaoying Wang Guoyan Liu Wanli Hu Kang Yao Yanke Hao A Predictive Disease Risk Model for Ankylosing Spondylitis: Based on Integrated Bioinformatic Analysis and Identification of Potential Biomarkers Most Related to Immunity Mediators of Inflammation |
| title | A Predictive Disease Risk Model for Ankylosing Spondylitis: Based on Integrated Bioinformatic Analysis and Identification of Potential Biomarkers Most Related to Immunity |
| title_full | A Predictive Disease Risk Model for Ankylosing Spondylitis: Based on Integrated Bioinformatic Analysis and Identification of Potential Biomarkers Most Related to Immunity |
| title_fullStr | A Predictive Disease Risk Model for Ankylosing Spondylitis: Based on Integrated Bioinformatic Analysis and Identification of Potential Biomarkers Most Related to Immunity |
| title_full_unstemmed | A Predictive Disease Risk Model for Ankylosing Spondylitis: Based on Integrated Bioinformatic Analysis and Identification of Potential Biomarkers Most Related to Immunity |
| title_short | A Predictive Disease Risk Model for Ankylosing Spondylitis: Based on Integrated Bioinformatic Analysis and Identification of Potential Biomarkers Most Related to Immunity |
| title_sort | predictive disease risk model for ankylosing spondylitis based on integrated bioinformatic analysis and identification of potential biomarkers most related to immunity |
| url | http://dx.doi.org/10.1155/2023/3220235 |
| work_keys_str_mv | AT wenxingao apredictivediseaseriskmodelforankylosingspondylitisbasedonintegratedbioinformaticanalysisandidentificationofpotentialbiomarkersmostrelatedtoimmunity AT ruiruihou apredictivediseaseriskmodelforankylosingspondylitisbasedonintegratedbioinformaticanalysisandidentificationofpotentialbiomarkersmostrelatedtoimmunity AT yungangchen apredictivediseaseriskmodelforankylosingspondylitisbasedonintegratedbioinformaticanalysisandidentificationofpotentialbiomarkersmostrelatedtoimmunity AT xiaoyingwang apredictivediseaseriskmodelforankylosingspondylitisbasedonintegratedbioinformaticanalysisandidentificationofpotentialbiomarkersmostrelatedtoimmunity AT guoyanliu apredictivediseaseriskmodelforankylosingspondylitisbasedonintegratedbioinformaticanalysisandidentificationofpotentialbiomarkersmostrelatedtoimmunity AT wanlihu apredictivediseaseriskmodelforankylosingspondylitisbasedonintegratedbioinformaticanalysisandidentificationofpotentialbiomarkersmostrelatedtoimmunity AT kangyao apredictivediseaseriskmodelforankylosingspondylitisbasedonintegratedbioinformaticanalysisandidentificationofpotentialbiomarkersmostrelatedtoimmunity AT yankehao apredictivediseaseriskmodelforankylosingspondylitisbasedonintegratedbioinformaticanalysisandidentificationofpotentialbiomarkersmostrelatedtoimmunity AT wenxingao predictivediseaseriskmodelforankylosingspondylitisbasedonintegratedbioinformaticanalysisandidentificationofpotentialbiomarkersmostrelatedtoimmunity AT ruiruihou predictivediseaseriskmodelforankylosingspondylitisbasedonintegratedbioinformaticanalysisandidentificationofpotentialbiomarkersmostrelatedtoimmunity AT yungangchen predictivediseaseriskmodelforankylosingspondylitisbasedonintegratedbioinformaticanalysisandidentificationofpotentialbiomarkersmostrelatedtoimmunity AT xiaoyingwang predictivediseaseriskmodelforankylosingspondylitisbasedonintegratedbioinformaticanalysisandidentificationofpotentialbiomarkersmostrelatedtoimmunity AT guoyanliu predictivediseaseriskmodelforankylosingspondylitisbasedonintegratedbioinformaticanalysisandidentificationofpotentialbiomarkersmostrelatedtoimmunity AT wanlihu predictivediseaseriskmodelforankylosingspondylitisbasedonintegratedbioinformaticanalysisandidentificationofpotentialbiomarkersmostrelatedtoimmunity AT kangyao predictivediseaseriskmodelforankylosingspondylitisbasedonintegratedbioinformaticanalysisandidentificationofpotentialbiomarkersmostrelatedtoimmunity AT yankehao predictivediseaseriskmodelforankylosingspondylitisbasedonintegratedbioinformaticanalysisandidentificationofpotentialbiomarkersmostrelatedtoimmunity |