原因不明的复发性自然流产(URSA)是全世界生殖女性面临的严峻挑战,其病因和发病机制尚未完全阐明。巨噬细胞(Mφ)与蜕膜基质细胞(DSC)或滋养细胞之间的异常细胞间通讯被认为是URSA的关键。然而,串扰中的确切分子机制尚未得到很好的理解。本研究旨在通过生物信息学分析的综合研究策略,探索Mφ与DSC或滋养细胞之间可能参与通讯的潜在分子机制,并确定其诊断特征。机器学习和实验。首先,URSA患者的蜕膜组织(GSE26787,GSE165004)和胎盘组织(GSE22490)的微阵列,以及涉及诱导蜕膜化(GSE94644)和体外巨噬细胞极化(GSE30595)的微阵列来自基因表达综合(GEO)数据库。和721个蜕膜差异表达基因(DEGs),613胎盘-DEGs,通过差异表达分析在URSA中获得510个Mφ极化DEGs。然后,构建了蛋白质-蛋白质相互作用(PPI)网络,hub基因在Cytoscape软件中通过CytoHubba进行鉴定,并通过实时PCR检测进行验证。随后,通过ClueGO对蜕膜-DEGs和胎盘-DEGs的免疫富集分析验证了它们对Mφ的调节作用。此外,对Mφ极化DEGs和加权基因共表达网络分析(WGCNA)衍生的必需模块基因进行了功能富集分析,以揭示与Mφ异常极化相关的生物学功能。此外,我们从DSC-DEGs中筛选出29、43和22个分泌蛋白编码基因,胎盘-DEGs和Mφ极化DEGs,分别。此外,通过CytoHubba筛选hub分泌蛋白编码基因。此外,我们对这些基因进行了功能富集分析。并对供体细胞的hub分泌蛋白编码基因和受体细胞的hub基因进行了Spearman相关性分析,以进一步了解细胞间通讯的分子机制。此外,通过机器学习从分泌的蛋白质编码基因中筛选出具有诊断价值的特征基因,并通过与临床样本的免疫荧光共定位分析进行验证.最后,DSC的三种生物标志物(FGF9,IL1R2,NID2)和Mφ的三种生物标志物(CFB,获得NID2、CXCL11)。总之,本项目为理解URSA母胎界面巨噬细胞参与细胞间通讯的机制调控网络提供了新思路。此外,它为URSA的诊断和治疗提供了创新的见解。
Unexplained recurrent spontaneous abortion (URSA) is a severe challenge to reproductive females worldwide, and its etiology and pathogenesis have not yet been fully clarified. Abnormal intercellular communication between macrophages (Mφ) and decidual stromal cells (DSCs) or trophoblasts has been supposed to be the key to URSA. However, the exact molecular mechanisms in the crosstalk are not yet well understood. This study aimed to explore the potential molecule mechanism that may be involved in the communication between Mφ and DSC or trophoblast cells and determine their diagnostic characteristics by using the integrated research strategy of bioinformatics analysis, machine learning and experiments. First, microarrays of decidual tissue (GSE26787, GSE165004) and placenta tissue (GSE22490) in patients with URSA, as well as microarrays involving induced decidualization (GSE94644) and macrophage polarization in vitro (GSE30595) were derived from the gene expression omnibus (GEO) database. And 721 decidua-differentially expressed genes (DEGs), 613 placenta-DEGs, 510 Mφ polarization DEGs were obtained in URSA by differential expression analysis. Then, the protein-protein interaction (PPI) network was constructed, and the hub genes were identified by CytoHubba in Cytoscape software and validated by real-time PCR assay. Subsequently, immune enrichment analysis on decidua-DEGs and placenta-DEGs by ClueGO verified their regulation effects on Mφ. Besides, functional enrichment analysis was performed on Mφ polarization DEGs and the essential module genes derived from the weighted gene co-expression network analysis (WGCNA) to uncover the biological function that were related to abnormal polarization of Mφ. Furthermore, we screened out 29, 43 and 22 secreted protein-encoding genes from DSC-DEGs, placenta-DEGs and Mφ polarization DEGs, respectively. Besides, the hub secreted-protein-encoding genes were screened by CytoHubba. Moreover, we conducted functional enrichment analysis on these genes. And spearman correlation analysis between hub secreted-protein-encoding genes from donor cells and hub genes in recipient cells was performed to further understand the molecular mechanism of intercellular communication further. Moreover, signature genes with diagnostic value were screened from secreted protein-encoding genes by machine learning and validated by immunofluorescence co-localization analysis with clinical samples. Finally, three biomarkers of DSCs (FGF9, IL1R2, NID2) and three biomarkers of Mφ (CFB, NID2, CXCL11) were obtained. In conclusion, this project provides new ideas for understanding the mechanism regulatory network of intercellular communication involving macrophages at the maternal-fetal interface of URSA. Also, it provides innovative insights for the diagnosis and treatment of URSA.