首页|基于机器学习的反复种植失败相关泛素化基因与免疫浸润分析

基于机器学习的反复种植失败相关泛素化基因与免疫浸润分析

Ubiquitination-related gene signature based on machine learning for recurrent implantation failure and its associations with immune infiltration

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目的 基于生物信息学方法探究与反复种植失败(recurrent implantation failure,RIF)密切相关的泛素化相关基因,并探索其与免疫浸润之间的相互作用.方法 从基因表达综合数据库(GEO)中选取GSE111974、GSE26787和GSE223672数据集,其中GSE111974作为训练集进行差异分析,对差异基因与泛素化基因取交集得到RIF相关的差异泛素化基因,随后利用机器学习筛选出关键基因.利用GSE223672单细胞数据集分析关键基因在内膜细胞亚群中的表达.ssGSEA算法用于确定样本间免疫细胞比例以及免疫细胞与关键基因之间的相关性.结果 KLHL13、UCHL1、TOP2A和USP33为RIF的特征基因.TOP2A在RIF子宫内膜间充质干细胞中表达量最高,USP33主要在间充质干细胞和内皮细胞中表达.CD8+T细胞、中性粒细胞、Th1细胞等在RIF受到抑制,而辅助性T细胞明显活跃.KLHL13的表达与抗原呈递细胞(APC)共刺激等呈正相关,而USP33表达与APC共刺激呈负相关.TOP2A和UCHL1表达与T淋巴辅助细胞浸润呈负相关.结论 KLHL13、UCHL1、TOP2A和USP33是RIF的诊断标志物,它们通过调节免疫细胞浸润影响RIF.
Objective To investigate ubiquitination-related genes closely associated with recurrent implantation failure(RIF)based on bioinformatics and explore their interaction with immune infiltration.Methods GSE111974,GSE26787,and GSE223672 data sets were selected from the Gene Expression Omnibus(GEO),with GSE111974 serving as the training set for differential analysis.Differentially expressed ubiquitination-related genes(DEUGs)related to RIF were obtained by intersecting differentially expressed genes with ubiquitination genes,and then hub genes were screened using machine learning.The expression of hub genes in endometrial cell clusters was analyzed using GSE223672.The ss GSEA algorithm was used to determine the proportion of immune cells between samples and the correlation between immune cells and hub genes.Results KLHL13,UCHL1,TOP2A,and USP33 were identified as characteristic genes in RIF.Among these,TOP2A exhibited the highest expression in endometrial mesenchymal stem cells,whereas USP33 was predominantly expressed in mesenchymal stem cells and endothelial cells.In RIF,CD8+T cells,neutrophils,and Th l cells were suppressed,while helper T cells were significantly active.KLHL13 expression was positively correlated with APC co-stimulation,whereas USP33 expression was negatively correlated.Additionally,TOP2A and UCHL1 expression showed negative correlations with T helper cell infiltration.Conclusion KLHL13,UCHL1,TOP2A,and USP33 are diagnostic markers of RIF and affect RIF by regulating immune cell infiltration.

recurrent implantation failureubiquitinationTOP2Aimmune cell infiltrationmachine learning

梁湘萍、王兆亿、邱锡坚、刘风华

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广州医科大学,广东广州 511495

广东医科大学附属医院妇科,广东湛江 524000

广东省妇幼保健院生殖健康与不孕专科,广东广州 511442

反复种植失败 泛素化 TOP2A 免疫细胞浸润 机器学习

广东省卫济医学发展基金

K-202104-2

2024

中国实用妇科与产科杂志
中国医师协会 中国实用医学杂志社

中国实用妇科与产科杂志

CSTPCD北大核心
影响因子:1.97
ISSN:1005-2216
年,卷(期):2024.40(8)