强直性脊柱炎关键基因的多芯片联合分析（开放获取）

(Open Access) Bioinformatic analysis of crucial genes in ankylosing spondylitis across multiple microarrays

摘要:

[目的] 分析基因表达综合（GEO） 数据库中的强直性脊柱炎（ankylosing spondylitis, AS） 及正常人的膝关节滑膜组织的RNA 测序结果，筛选出相关的差异表达基因（differentially expressed genes, DEGs），为AS 的诊疗提供新的生物学靶向策略。[方法] 从GEO 数据库下载GSE41038 和GSE39340 数据集，质控后筛选AS 的DEGs，并进行功能富集和通路分析。随后利用在线数据库（Search Tool for Retrieval of Interacting Genes, STRING） 构建已鉴定基因的蛋白-蛋白相互作用（protein-proteininteraction, PPI） 网络，并通过Cytoscape 软件筛选出连接度最高的基因，评估关键基因对AS 的诊断效能。[结果] 在AS 患者和正常人之间共鉴定出433 个DEGs，其中276 个上调，157 个下调，GO 分析显示这些DEGs 主要参与T 细胞激活的正向调控、细胞外基质结构成分；KEGG 富集结果主要与类NF-κB 信号通路、TNF 信号通路结合等功能相关；运用STRING 数据库构建PPI网络并筛选出10 个网络中的核心基因：CASP3、CD36、CXCR4、EGFR、FGF10、IL-1β、MMP1、MMP3、SELL、TLR2。[结论] 采用生物信息学方法分析AS 的潜在机制，并筛选出10 个重要分子，可能是AS 潜在的关键基因和生物学标志物。

Abstract:

[Objective] To utilize bioinformatics methods to screen for differentially expressed genes (DEGs) associated with ankylosingspondylitis (AS) in the Gene Expression Omnibus database (GEO), aiming to provide new biological targeting strategies for the clinical diag-nosis and treatment of AS. [Methods] Datasets GSE41038 and GSE39340 were downloaded from the GEO database. After data processing,DEGs related to AS were selected. Functional enrichment and pathway analyses were then performed on these DEGs. Subsequently, the pro-tein-protein interaction (PPI) network of the identified genes was constructed using the online database (Search Tool for Retrieval of Interact-ing Genes, STRING) and visualized using Cytoscape software. [Results] A total of 433 DEGs were identified between AS patients andhealthy individuals, with 276 upregulated and 157 downregulated. GO analysis revealed that these DEGs were mainly involved in positiveregulation of T cell activation and collagen-containing extracellular matrix. KEGG enrichment results were primarily associated with NFkappaB signaling pathway and TNF signaling pathway. Using the STRING database, a protein interaction network was constructed, with Cy-toscape identified the top 10 genes with the highest connectivity, including CASP3, CD36, CXCR4, EGFR, FGF10, IL-1β, MMP1, MMP3,SELL and TLR2. [Conclusion] In this study, the potential mechanism of AS was analyzed by bioinformatics method, and 10 important mole-cules were screened, which may be the potential key genes and biological markers of AS.