背景:强直性脊柱炎(AS)是一种慢性炎症性自身免疫性疾病,由于AS的发病机制尚不清楚,其诊断和治疗受到限制。细胞凋亡是一种促炎类型的细胞死亡,在免疫系统中起着重要作用。然而,焦亡基因与AS之间的关系尚未阐明。
方法:GSE73754、GSE25101和GSE221786数据集从基因表达综合(GEO)数据库收集。通过R软件鉴定差异表达的焦亡相关基因(DE-PRG)。利用机器学习和PPI网络筛选关键基因,构建AS诊断模型。根据DE-PRGs,使用共识聚类分析将AS患者分为不同的焦亡亚型,并使用主成分分析(PCA)进行验证。WGCNA用于筛选两种亚型之间的hub基因模块。基因本体论(GO)术语和京都基因和基因组百科全书(KEGG)途径用于富集分析以阐明潜在机制。ESTIMATE和CIBERSORT算法用于揭示免疫特征。连接图(CMAP)数据库用于预测治疗AS的潜在药物。分子对接用于计算潜在药物与hub基因之间的结合亲和力。
结果:与健康对照组相比,在AS中检测到16个DE-PRG,其中一些基因与中性粒细胞等免疫细胞有显著的相关性,CD8+T细胞,和静止的NK细胞。富集分析表明,DE-PRGs主要与焦亡有关,IL-1β,和TNF信号通路。关键基因(TNF,采用机器学习筛选的NLRC4、GZMB)和蛋白-蛋白相互作用(PPI)网络建立AS的诊断模型。ROC分析表明,该诊断模型在GSE73754(AUC:0.881)中具有良好的诊断特性,GSE25101(AUC:0.797),和GSE221786(AUC:0.713)。使用16个DE-PRG,AS患者分为C1和C2亚型,这两种亚型在免疫浸润方面表现出显著差异。使用WGCNA从两个亚型中鉴定出关键基因模块,富集分析表明该模块主要与免疫功能有关。三种潜在的药物,包括抗坏血酸,RO90-7501和celastrol,是根据CMAP分析选择的。Cytoscape显示GZMB是得分最高的hub基因。最后,分子对接结果表明,GZMB与抗坏血酸形成了三个氢键,包括ARG-41、LYS-40和HIS-57(亲和力:-5.3kcal/mol)。GZMB和RO-90-7501形成一个氢键,包括CYS-136(亲和力:-8.8kcal/mol)。GZMB和celastrol形成三个氢键,包括TYR-94、HIS-57和LYS-40(亲和力:-9.4kcal/mol)。
结论:我们的研究系统分析了焦亡与AS之间的关系。焦亡可能在AS的免疫微环境中起重要作用。我们的发现将有助于进一步了解AS的发病机制。
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.