■复发性流产(RM)在>50%的患者中仍未解决,并在没有流产特定风险因素的女性中引起身体和心理问题。为了成功怀孕,接受子宫内膜和滋养层细胞侵入子宫内膜是必要的。
■本研究旨在使用计算分析来鉴定源自RM样品的子宫内膜和滋养层细胞中的关键基因和相关途径。
■在这项生物信息学研究中,我们通过分析GSE165004和GSE76862数据集来探索子宫内膜和滋养细胞中基因的差异表达,分别与R软件中的limma包。随后,选择2个数据集之间的重叠基因,进行了基因本体论和京都基因百科全书和基因组分析。整合重叠基因以构建蛋白质-蛋白质相互作用网络和集线器基因选择。
■我们观察到子宫内膜和滋养细胞之间有41个重叠基因,并完成了重叠和非重叠基因的未来分析。京都基因百科全书和基因组分析表明,重叠基因在补体和凝血级联中显著富集,干细胞的多能性,酮体的合成和降解。基因本体论分析表明,这些基因在细胞周期中富集,凋亡,和细胞分裂。前10个基因包括:IRS1,FGF2,MAPK6,MAPK1,MAPK3,MAPK8,MAPK9,PLK1,PRKACA,和PRKCA是从PPI网络中识别出来的。
■这项研究确定了RM发展的关键基因和潜在分子途径。这可以提供新的见解,以确定与流产相关的可能机制和干预策略。
UNASSIGNED: Recurrent miscarriage (RM) remains unsolved in > 50% of patients and causes physical and psychological problems in women without specific risk factors for miscarriage. For a successful pregnancy, acceptance of the endometrium and invasion of trophoblast cells into the endometrium is necessary.
UNASSIGNED: This study aimed to use computational analysis to identify key genes and related pathways in endometrial and trophoblast cells derived from RM samples.
UNASSIGNED: In this bioinformatics study, we explored the differential expression of genes in endometrial and trophoblast cells by analyzing the GSE165004 and GSE76862 datasets, respectively with the limma package in R software. Subsequently, overlapped genes between 2 datasets were selected, gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed. The overlapped genes were integrated to construct a protein-protein interaction network and hub genes selection.
UNASSIGNED: We observed 41 overlapped genes between endometrial and trophoblast cells, and future analysis was accomplished in overlapped and nonoverlapped genes. Kyoto Encyclopedia of Genes and Genomes analysis indicated that overlapped genes were significantly enriched in the complement and coagulation cascades, pluripotency of stem cells, and synthesis and degradation of ketone bodies. Gene ontology analysis suggested that the genes were enriched in the cell cycle, apoptosis, and cell division. The top 10 genes included: IRS1, FGF2, MAPK6, MAPK1, MAPK3, MAPK8, MAPK9, PLK1, PRKACA, and PRKCA were identified from the PPI network.
UNASSIGNED: This study identified the key genes and potential molecular pathways underlying the development of RM. This could provide novel insights to determine the possible mechanisms and interventional strategies associated with miscarriage.