基于生物信息学方法筛选复发性流产中巨噬细胞相关的免疫特征基因

Identification of macrophage-related immune characteristic genes in recurrent miscarriage through bioinformatics approaches

目的通过生物信息学分析方法,筛选可能导致复发性流产(recurrent miscarriage,RM)的母胎免疫微稳态失衡相关基因,寻找潜在的RM分子标志物。方法从Gene Expression Omnibus(GEO)数据库下载由24例RM患者和24例正常对照妇女子宫内膜组织数据所组成的数据集GSE165004,采用R语言的Limma包及CIBERSOR免疫浸润和加权基因共表达网络分析(weighted gene co-expression network analysis,WGCNA)方法,筛选了差异表达基因(differentially expressed genes,DEGs)和免疫相关模块;通过基因集富集分析(gene set enrichment analysis,GSEA)和基因集变异分析(gene set variation analysis,GSVA),评价了这些核心基因的功能关联性。最后我们使用蜕膜组织的数据集GSE161969进一步验证了关键基因的诊断价值。结果通过差异分析,识别出580个差异表达基因,并通过WGCNA筛选得到3271个与免疫相关的模块基因;利用机器学习技术,鉴定出FGF2、ANO1和LAPTM5作为关键基因,并通过GSVA分析,发现这些基因在免疫浸润和巨噬细胞途径中发挥重要作用。结论FGF2、ANO1和LAPTM5可能参与RM的免疫致病途径,是潜在的RM生物标志分子。

Objectives To screen out genes potentially involved in the dysregulation of immune microhomeostasis at the maternal-fetal interface of recurrent miscarriage(RM)patients,and to identify novel biomarkers of RM by bioinformatic analysis.Methods The dataset GSE165004 of endometrial tissues from RM patients(n=24)and normal women as the control(n=24)was downloaded from the GEO database,and differentially expressed genes(DEGs)and immune-related modules were analyzed by using the R language's Limma package,along with CIBERSORT immune infiltration and Weighted Gene Co-expression Network Analysis(WGCNA).The functional associations of these core genes were evaluated through Gene Set Enrichment Analysis(GSEA)and Gene Set Variation Analysis(GSVA).Finally,we used the decidual tissue dataset GSE161969 to further validate the diagnostic value of these key genes.Results Differential analysis identified 580 DEGs,and 3271 immune-related modular genes were selected by WGCNA analysis.FGF2,ANO1,and LAPTM5 were subsequently identified as key genes through machine learning techniques.GSVA analysis further revealed critical roles of FGF2,ANO1 and LAPTM5 in immune infiltration and macrophage pathways.Conclusion FGF2,ANO1 and LAPTM5 might participate in the immuno-related pathogenesis of RM,and present potential biomarkers for the early diagnosis and treatment of RM.