Department of Orthopedics, The First Affiliated Hospital of Chongqing Medical University, Yuzhong, Chongqing, China.
Orthopedic Laboratory of Chongqing Medical University, Yuzhong, Chongqing, China.
BMC Musculoskelet Disord. 2023 May 24;24(1):413. doi: 10.1186/s12891-023-06550-3.
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.
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.
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.
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.
通过综合生物信息学分析，寻找强直性脊柱炎（AS）的关键基因，为 AS 的未来诊断和治疗及进一步研究提供一些理论支持。
通过在基因表达综合数据库（GEO，http://www.ncbi.nlm.nih.gov/geo/）中搜索“ankylosing spondylitis”一词，收集基因表达谱。最终从 GEO 数据库中下载了两个微阵列数据集（GSE73754 和 GSE11886）。采用生物信息学方法筛选差异表达基因，并进行功能富集分析，获得与疾病相关的生物学功能和信号通路。利用加权相关网络分析（WGCNA）进一步获得关键基因。采用 CIBERSORT 算法进行免疫浸润分析，对关键基因与免疫细胞进行相关性分析。分析 AS 的 GWAS 数据，识别 AS 关键基因的致病区域。最后，利用这些关键基因预测 AS 的潜在治疗药物。
共鉴定出 7 个潜在的生物标志物：DYSF、BASP1、PYGL、SPI1、C5AR1、ANPEP 和 SORL1。ROC 曲线显示每个基因的预测效果均较好。与配对的正常组相比，疾病组的 T 细胞、CD4 幼稚细胞和中性粒细胞水平显著升高，关键基因表达与免疫细胞呈强烈正相关。CMap 结果表明，布洛芬、 forskolin、布格克酸和辛贝特的表达谱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 的治疗中发挥作用。
本研究筛选出的 AS 潜在生物标志物与免疫细胞浸润水平密切相关，在免疫微环境中发挥重要作用。这可能有助于 AS 的临床诊断和治疗，并为进一步研究提供新的思路。
