目的:本研究的目的是通过全面的生物信息学分析来寻找强直性脊柱炎(AS)的关键基因,从而为今后AS的诊断和治疗及进一步的研究提供一定的理论支持。
方法:从基因表达Omnibus收集基因表达谱(GEO,http://www。ncbi.nlm.nih.gov/geo/)通过搜索术语“强直性脊柱炎”。最终,从GEO数据库下载两个微阵列数据集(GSE73754和GSE11886)。使用生物信息学方法筛选差异表达的基因并进行功能富集分析以获得与疾病相关的生物学功能和信号通路。采用加权相关网络分析(WGCNA)进一步获得关键基因。免疫浸润分析采用CIBERSORT算法进行关键基因与免疫细胞的相关性分析。分析AS的GWAS数据,以确定AS关键基因的致病区域。最后,使用这些关键基因预测了AS的潜在治疗药物。
结果:总共确定了7种潜在的生物标志物:DYSF,BASP1,PYGL,SPI1、C5AR1、ANPEP和SORL1。ROC曲线显示每个基因的良好预测。T细胞,CD4幼稚细胞,疾病组的中性粒细胞水平明显高于配对正常组,关键基因表达与免疫细胞密切相关。CMap结果显示布洛芬的表达谱,forskolin,bongkrek-acid,西马特罗与疾病扰动的表达谱呈最显著的负相关,提示这些药物可能在AS治疗中发挥作用。
结论:本研究筛选的AS潜在生物标志物与免疫细胞浸润水平密切相关,在免疫微环境中起重要作用。这可能为AS的临床诊断和治疗提供帮助,为进一步的研究提供新思路。
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 naïve 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.
CONCLUSIONS: 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.