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Dry and wet experiments reveal diagnostic clustering and immune landscapes of cuproptosis patterns in patients with ankylosing spondylitis

免疫系统 强直性脊柱炎 免疫学 生物 免疫 医学
作者
Peng Zhang,Honglin Chen,You Zhang,Yu Liu,Guangye Zhu,Wenhua Zhao,Qi Shang,Jiahui He,Zelin Zhou,Gengyang Shen,Xiang Yu,Zhida Zhang,Guifeng Chen,Fuyong Yu,De Liang,Jingjing Tang,Zhixiang Liu,Jianchao Cui,Xiaobing Jiang,Hui Ren
出处
期刊:International Immunopharmacology [Elsevier BV]
卷期号:127: 111326-111326 被引量:1
标识
DOI:10.1016/j.intimp.2023.111326
摘要

Cuproptosis is a new manner of mitochondrial cell death induced by copper. There is evidence that serum copper has a crucial impact on ankylosing spondylitis (AS) by copper-induced inflammatory response. However, the molecular mechanisms of cuproptosis modulators in AS remain unknown. We aimed to use a bioinformatics-based method to comprehensively investigate cuproptosis-related subtype identification and immune microenvironment infiltration of AS. Additionally, we further verified the results by in vitro experiments, in which peripheral blood and fibroblast cells from AS patients were used to evaluate the functions of significant cuproptosis modulators on AS. Finally, eight significant cuproptosis modulators were identified by analysis of differences between controls and AS cases from GSE73754 dataset. Eight prognostic cuproptosis modulators (LIPT1, DLD, PDHA1, PDHB, SLC31A1, ATP7A, MTF1, CDKN2A) were identified using a random forest model for prediction of AS risk. A nomogram model of the 8 prognostic cuproptosis modulators was then constructed; the model could be beneficial in clinical settings, as indicated by decision curve analysis. Consensus clustering analysis was used to divide AS patients into two cuproptosis subtypes (clusterA & B) according to significant cuproptosis modulators. The cuproptosis score of each sample was calculated by principal component analysis to quantify cuproptosis subtypes. The cuproptosis scores were higher in clusterB than in clusterA. Additionally, cases in clusterA were closely associated with the immunity of activated B cells, Activated CD4 T cell, Type17 T helper cell and Type2 T helper cell, while cases in clusterB were linked to Mast cell, Neutrophil, Plasmacytoid dendritic cell immunity, indicating that clusterB may be more correlated with AS. Notably, key cuproptosis genes including ATP7A, MTF1, SLC31A1 detected by RT-qPCR with peripheral blood exhibited significantly higher expression levels in AS cases than controls; LIPT1 showed the opposite results; High MTF1 expression is correlated with increased osteogenic capacity. In general, this study of cuproptosis patterns may provide promising biomarkers and immunotherapeutic strategies for future AS treatment.
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