Identification of potential biomarkers for ankylosing spondylitis based on bioinformatics analysis

作者全名："Li, Dongxu; Cao, Ruichao; Dong, Wei; Cheng, Minghuang; Pan, Xiaohan; Hu, Zhenming; Hao, Jie"

作者地址："[Li, Dongxu; Cao, Ruichao; Dong, Wei; Cheng, Minghuang; Pan, Xiaohan; Hu, Zhenming; Hao, Jie] Chongqing Med Univ, Affiliated Hosp 1, Dept Orthoped, Chongqing, Peoples R China; [Li, Dongxu; Cao, Ruichao; Dong, Wei; Cheng, Minghuang; Pan, Xiaohan; Hu, Zhenming; Hao, Jie] Chongqing Med Univ, Orthoped Lab, Chongqing, Peoples R China"

通信作者："Hao, J (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Orthoped, Chongqing, Peoples R China.; Hao, J (通讯作者)，Chongqing Med Univ, Orthoped Lab, Chongqing, Peoples R China."

来源：BMC MUSCULOSKELETAL DISORDERS

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000994663700002

JCR分区：Q2

影响因子：2.2

年份：2023

卷号：24

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：Ankylosing spondylitis; Immune infiltration; Bioinformatics; Biomarkers; GWAS

摘要："Objective The aim of this study was to search for key genes in ankylosing spondylitis (AS) through comprehensive bioinformatics analysis, thus providing some theoretical support for future diagnosis and treatment of AS and further research. Methods Gene expression profiles were collected from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih. gov/geo/) by searching for the term ""ankylosing spondylitis"". Ultimately, two microarray datasets (GSE73754 and GSE11886) were downloaded from the GEO database. A bioinformatic approach was used to screen differentially expressed genes and perform functional enrichment analysis to obtain biological functions and signalling pathways associated with the disease. Weighted correlation network analysis ( WGCNA) was used to further obtain key genes. Immune infiltration analysis was performed using the CIBERSORT algorithm to conduct a correlation analysis of key genes with immune cells. The GWAS data of AS were analysed to identify the pathogenic regions of key genes in AS. Finally, potential therapeutic agents for AS were predicted using these key genes. Results A total of 7 potential biomarkers were identified: DYSF, BASP1, PYGL, SPI1, C5AR1, ANPEP and SORL1. ROC curves showed good prediction for each gene. T cell, CD4 naive cell, and neutrophil levels were significantly higher in the disease group than in the paired normal group, and key gene expression was strongly correlated with immune cells. CMap results showed that the expression profiles of ibuprofen, forskolin, bongkrek-acid, and cimaterol showed the most significant negative correlation with the expression profiles of disease perturbations, suggesting that these drugs may play a role in AS treatment. Conclusion The potential biomarkers of AS screened in this study are closely related to the level of immune cell infiltration and play an important role in the immune microenvironment. This may provide help in the clinical diagnosis and treatment of AS and provide new ideas for further research."

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