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Author Spotlight: Evaluating Traditional Chinese Therapy for Ankylosing Spondylitis in Mice
Published on: October 27, 2023
Exploring hub pyroptosis-related genes, molecular subtypes, and potential drugs in ankylosing spondylitis by
Xin Li1, Xiangying Li1, Hongqiang Wang2
1Henan University People's Hospital, Henan Provincial People's Hospital, Zhengzhou, China.
Background:
Ankylosing spondylitis (AS) is a chronic inflammatory autoimmune disease, and the diagnosis and treatment of AS have been limited because its pathogenesis is still unclear. Pyroptosis is a proinflammatory type of cell death that plays an important role in the immune system. However, the relationship between pyroptosis genes and AS has never been elucidated.
Methods:
GSE73754, GSE25101, and GSE221786 datasets were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed pyroptosis-related genes (DE-PRGs) were identified by R software. Machine learning and PPI networks were used to screen key genes to construct a diagnostic model of AS. AS patients were clustered into different pyroptosis subtypes according to DE-PRGs using consensus cluster analysis and validated using principal component analysis (PCA). WGCNA was used for screening hub gene modules between two subtypes. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used for enrichment analysis to elucidate underlying mechanisms. The ESTIMATE and CIBERSORT algorithms were used to reveal immune signatures. The connectivity map (CMAP) database was used to predict potential drugs for the treatment of AS. Molecular docking was used to calculate the binding affinity between potential drugs and the hub gene.
Results:
Sixteen DE-PRGs were detected in AS compared to healthy controls, and some of these genes showed a significant correlation with immune cells such as neutrophils, CD8 + T cells, and resting NK cells. Enrichment analysis showed that DE-PRGs were mainly related to pyroptosis, IL-1β, and TNF signaling pathways. The key genes (TNF, NLRC4, and GZMB) screened by machine learning and the protein-protein interaction (PPI) network were used to establish the diagnostic model of AS. ROC analysis showed that the diagnostic model had good diagnostic properties in GSE73754 (AUC: 0.881), GSE25101 (AUC: 0.797), and GSE221786 (AUC: 0.713). Using 16 DE-PRGs, AS patients were divided into C1 and C2 subtypes, and these two subtypes showed significant differences in immune infiltration. A key gene module was identified from the two subtypes using WGCNA, and enrichment analysis suggested that the module was mainly related to immune function. Three potential drugs, including ascorbic acid, RO 90-7501, and celastrol, were selected based on CMAP analysis. Cytoscape showed GZMB as the highest-scoring hub gene. Finally, molecular docking results showed that GZMB and ascorbic acid formed three hydrogen bonds, including ARG-41, LYS-40, and HIS-57 (affinity: -5.3 kcal/mol). GZMB and RO-90-7501 formed one hydrogen bond, including CYS-136 (affinity: -8.8 kcal/mol). GZMB and celastrol formed three hydrogen bonds, including TYR-94, HIS-57, and LYS-40 (affinity: -9.4 kcal/mol).
Conclusions:
Our research systematically analyzed the relationship between pyroptosis and AS. Pyroptosis may play an essential role in the immune microenvironment of AS. Our findings will contribute to a further understanding of the pathogenesis of AS.
Insights
This study reveals pyroptosis genes are crucial in ankylosing spondylitis (AS) pathogenesis and immune microenvironment. Findings identify key pyroptosis genes for AS diagnosis and potential therapeutic targets, advancing understanding of this chronic inflammatory disease.
Area of Science:
- Immunology
- Genetics
- Computational Biology
Background:
- Ankylosing spondylitis (AS) is a chronic inflammatory autoimmune disease with unclear pathogenesis, limiting diagnosis and treatment.
- Pyroptosis, a pro-inflammatory cell death, is vital in immunity, but its role in AS remains unelucidated.
Purpose of the Study:
- To investigate the relationship between pyroptosis-related genes (PRGs) and ankylosing spondylitis (AS).
- To identify key PRGs for AS diagnosis and explore potential therapeutic strategies targeting pyroptosis pathways.
Main Methods:
- Utilized GEO datasets (GSE73754, GSE25101, GSE221786) to identify differentially expressed PRGs (DE-PRGs) in AS.
- Employed machine learning, PPI networks, WGCNA, and consensus clustering to screen key genes, construct diagnostic models, and classify AS subtypes.
- Performed enrichment analysis (GO, KEGG), immune signature analysis (ESTIMATE, CIBERSORT), and drug prediction (CMAP) with molecular docking.
Main Results:
- Identified 16 DE-PRGs in AS, correlating with immune cells like neutrophils and T cells.
- Developed a diagnostic model using key genes (TNF, NLRC4, GZMB) with high AUC values (0.713-0.881) across datasets.
- Classified AS patients into two subtypes with distinct immune infiltration patterns and identified potential drugs (ascorbic acid, RO 90-7501, celastrol) targeting GZMB.
Conclusions:
- Pyroptosis plays a significant role in the immune microenvironment of AS.
- The identified DE-PRGs and diagnostic model offer insights into AS pathogenesis and potential therapeutic interventions.
- This research provides a foundation for further understanding and managing ankylosing spondylitis.
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