Genetic Metabolic Unit, Department of Paediatrics, Advanced Paediatrics Centre, Post Graduate Institute of Medical Education & Research, Sector-12, Chandigarh, 160012, India.
Genetic Metabolic Unit, Department of Paediatrics, Advanced Paediatrics Centre, Post Graduate Institute of Medical Education & Research, Sector-12, Chandigarh, 160012, India.
Taiwan J Obstet Gynecol. 2024 May;63(3):297-306. doi: 10.1016/j.tjog.2024.01.035.
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.
复发性流产（RPL）是一种以妊娠 20 周前连续丧失两次或两次以上妊娠为特征的疾病。RPL 的病因复杂，可能与遗传、免疫、激素和环境因素等多种因素有关。本转录组数据挖掘研究采用计算方法，旨在探索差异表达基因（DEGs）和与 RPL 发病机制相关的途径。使用基因表达综合数据库（GEO）数据库中的 RNAseq 数据集提取 RPL 的 RNAseq 数据集。通过 ExpressAnalyst 进行荟萃分析。使用 Cytoscape 软件的 KEGG 和 BINGO 插件对 DEGs 进行功能和通路富集分析。使用 STRING 进行蛋白质-蛋白质相互作用分析，并鉴定枢纽基因。共鉴定出 91 个 DEGs，其中 10 个下调，81 个上调。通路分析表明，大多数 DEGs 富集在免疫途径（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 个具有高连通性的枢纽基因（CXCL8、CCND1、FOS、PTGS2、CTLA4、THBS1、MMP2、KDR 和 CD80）。这些基因大多参与维持母体-胎儿界面的免疫反应。此外，在功能富集分析中，调控生物过程的节点大小最高，其次是细胞过程、其调控和多细胞生物体过程的调控。这项基于转录组的荟萃分析研究结果可能有助于识别 RPL 的新型生物标志物和治疗靶点，从而为该疾病的新诊断和治疗策略的发展提供帮助。
