目的：通过生物信息学方法探究氧诱导小鼠视网膜新生血管(OIR)模型中与免疫相关的关键基因以及免疫细胞浸润水平。

方法：从GEO数据库获取基因芯片数据集，采用R语言环境中“limma”包获得差异表达基因(DEGs)，并进行GO功能富集和KEGG通路分析，基于CIBERSORT算法进行数据集免疫细胞浸润分析，通过加权基因共表达网络分析(WGCNA)筛选与免疫相关基因模块中的DEGs，利用STRING在线数据库及Cytoscape软件构建蛋白质互作(PPI)网络，利用cytoHubba模块筛选出最终的靶基因。

结果：筛选出467个表达具有显著差异性的基因，其中270个基因表达上调，197个基因表达下调。免疫浸润分析结果显示仅辅助型T细胞2(Th2 cells)高表达，且差异具有显著性(P<0.05)。WGCNA分析后，与免疫最相关模块中差异基因为66个，构建PPI网络后利用插件筛选出5个关键基因，即血管性血友病因子(vWF)、血管内皮生长因子A(VEGF-A)、Serping1、凋亡诱导因子1(AIF1)以及信号传导蛋白和转录激活物(STAT3)。

结论：采用生物信息学的方式进行OIR模型的免疫细胞浸润分析及筛选出与免疫相关的关键基因，可为视网膜新生血管性疾病的进一步研究与诊疗提供依据。

METHODS: Microarray data were obtained from the GEO database, differentially expressed genes(DEGs)were identified using the “limma” R package, GO function enrichment and KEGG pathway analysis were conducted, and immune cell infiltration based on the CIBERSORT algorithm was analyzed. Weighted gene co-expression network analysis(WGCNA)was used to screen DEGs in the immune-related gene module, constructing a protein-protein interaction(PPI)network using STRING online database and Cytoscape software, and further screening final target genes using the cytoHubba plug-in.

RESULTS: A total of 467 DEGs were screened, including 270 up-regulated and 197 down-regulated genes. Helper T cell 2(Th2 cells), an immune cell type, exhibited significantly high expression levels(P<0.05). WGCNA analysis identified 66 differential genes in modules most closely related to immunity. After constructing the PPI network, 5 key genes were screened through plug-ins: von Willebrand factor(vWF), vascular endothelial growth factor A(VEGF-A), Serping1, apoptosis inducing factor 1(AIF1)and signal transducers and activators of transcription 3(STAT3).

CONCLUSION: The findings of this study provide valuable insights into immune cell infiltration in OIR as well as key genes related to immunity through bioinformatics analysis, which can serve as a reference for further research and treatment of retinal neovascular diseases.