【摘要】 目的 通过生物信息学分析HBV感染相关免疫机制，找出定位于HBV感染相关免疫微环境的潜在治疗靶点。方法 从基因表达综合数据库下载GSE121248和GSE55092数据集。使用limma R软件包筛选HBV阳性肝组织样本与正常肝组织样本间的差异表达基因(DEGs)。从GeneCards数据库中下载免疫相关基因集，与上述两组DEGs取交集后得到免疫相关的差异表达基因(i-DEGs)。利用i-DEGs构建蛋白质-蛋白质互作(PPI)网络，以筛选可能与HBV感染相关的枢纽基因；使用多种网络工具评估枢纽基因与miRNA、转录因子、小分子药物的关联性。结果 从GSE55092和GSE121248分别获得1417和789个DEGs,二者与免疫相关基因集取交集后得到324个i-DEGs。通过构建PPI网络，鉴定出TOP2A、CDK1、AURKA、NCAPG、KIF11、CCNB1、CDC20、BUB1B、CCNB2、CCNA2共10个枢纽基因。通过探索DGIdb数据库，鉴定出可以调控TOP2A、CDK1、AURKA、CCNA2表达的36种小分子药物。进一步研究发现，CDK1对于HBV感染的诊断效能最高，其表达水平与HBV相关免疫微环境中的6种免疫细胞浸润水平显著相关。结论 本研究发现CKD1可能是调控HBV相关免疫微环境的关键靶点。更多还原

【Abstract】 Objective To explore the immune mechanisms associated with HBV infection through bioinformatics analysis and identify potential therapeutic targets located within the immune microenvironment related to HBV infection. Methods The GSE121248 and GSE55092 datasets were downloaded from Gene Expression Omnibus(GEO). The limma R package was utilized to screen for differentially expressed genes(DEGs) between HBV-positive samples and normal liver tissue samples. The immune-related gene set was downloaded from the GeneCards database, and the intersection of the above two sets of DEGs yielded immune-related differentially expressed genes(i-DEGs). A protein-protein interaction(PPI) network was constructed using i-DEGs to filter potential hub genes associated with HBV infection. The diagnostic capability of hub gens for HBV infection was assessed through receiver operating characteristic(ROC) curves. Additionally, various network tools were employed for hub gene-miRNA,-transription factor, and-small molecular drug interaction analyses. Results From GSE55092 and GSE 121248, 1417 and 789 DEGs were obtained, respectively. The intersection of these two sets with the immune-related gene set resulted in 324 i-DEGs. Through the construction of a protein-protein interaction network, we have identified 10 key genes, including TOP2A, CDK1, AURKA, NCAPG, KIF11, CCNB1, CDC20, BUB1B, CCNB2, and CCNA2. By exploring the DGIdb database, 36 small molecule drugs that can regulate the expression of TOP2A, CDK1, AURKA, and CCNA2 were identified. Further research indicates that CDK1 exhibits the highest diagnostic efficacy for HBV infection, and its expression level is significantly associated with the infiltration levels of 6 immune cells in the HBV-related immune microenvironment. Conclusion Our findings identified CDK1 as potential key regulator of the immune microenvironment associated with HBV infection.更多还原