Abstract：
Recurrent pregnancy loss (RPL) is a condition characterized by the loss of two or more pregnancies before 20 weeks of gestation. The causes of RPL are complex and can be due to a variety of factors, including genetic, immunological, hormonal, and environmental factors. This transcriptome data mining study was done to explore the differentially expressed genes (DEGs) and related pathways responsible for pathogenesis of RPL using an Insilco approach. RNAseq datasets from the Gene Expression Omnibus (GEO) database was used to extract RNAseq datasets of RPL. Meta-analysis was done by ExpressAnalyst. The functional and pathway enrichment analysis of DEGs were performed using KEGG and BINGO plugin of Cytoscape software. Protein-protein interaction was done using STRING and hub genes were identified. A total of 91 DEGs were identified, out of which 10 were downregulated and 81 were upregulated. Pathway analysis indicated that majority of DEGs were enriched in immunological pathways (IL-17 signalling pathway, TLR-signalling pathway, autoimmune thyroid disease), angiogenic VEGF-signalling pathway and cell-cycle signalling pathways. Of these, 10 hub genes with high connectivity were selected (CXCL8, CCND1, FOS, PTGS2, CTLA4, THBS1, MMP2, KDR, and CD80). Most of these genes are involved in maintenance of immune response at maternal-fetal interface. Further, in functional enrichment analyses revealed the highest node size in regulation of biological processes followed by cellular processes, their regulation and regulation of multicellular organismal process. This in-silico transcriptomics meta-analysis findings could potentially contribute in identifying novel biomarkers and therapeutic targets for RPL, which could lead to the development of new diagnostic and therapeutic strategies for this condition.
摘要：
复发性妊娠丢失（RPL）是一种以妊娠20周前失去两次或更多次妊娠为特征的疾病。RPL的原因是复杂的，可能是由于多种因素，包括遗传,免疫学，荷尔蒙，和环境因素。使用Insilco方法进行了这项转录组数据挖掘研究，以探索负责RPL发病机理的差异表达基因（DEGs）和相关途径。来自基因表达综合(GEO)数据库的RNAseq数据集用于提取RPL的RNAseq数据集。Meta分析由ExpressAnalyst进行。使用Cytoscape软件的KEGG和BINGO插件进行DEGs的功能和途径富集分析。使用STRING进行蛋白质-蛋白质相互作用,并鉴定hub基因。总共确定了91个DEG，其中10个下调，81个上调。通路分析表明，大多数DEGs在免疫通路中富集(IL-17信号通路，TLR信号通路,自身免疫性甲状腺疾病），血管生成VEGF信号通路和细胞周期信号通路。其中,选择了10个高连通性的hub基因(CXCL8、CCND1、FOS、PTGS2,CTLA4,THBS1,MMP2,KDR,和CD80）。这些基因中的大多数参与维持母胎界面的免疫应答。Further,在功能富集分析中揭示了在调节生物过程之后是细胞过程的最高节点大小，它们对多细胞组织过程的调控。这种计算机转录组学荟萃分析结果可能有助于识别RPL的新型生物标志物和治疗靶标。这可能导致针对这种情况的新诊断和治疗策略的开发。
