抗磷脂综合征复发性流产中枢基因的综合分析：综合生物信息学分析、机器学习和实验验证

摘要(中文译文)

复发性流产（RPL）与抗磷脂综合征（APS）显著相关，但其共同致病机制尚不清楚。本研究旨在识别APS相关RPL的潜在枢纽基因，为其发病机制提供见解并探索潜在的诊断策略。我们从GEO数据库获取APS和RPL数据集，进行差异表达分析以识别差异表达基因。随后，开展基因本体（GO）分析、通路富集分析、加权基因共表达网络分析（WGCNA）以及蛋白质-蛋白质相互作用（PPI）分析。在枢纽基因筛选中，应用最小绝对收缩和选择算子（LASSO）回归、高斯混合模型（GMM）和随机森林（RF）算法分析RPL的基因表达谱。利用CIBERSORT工具评估RPL和APS患者样本中的免疫细胞浸润水平，同时使用“clusterProfiler”R包对两个队列中每个枢纽基因进行单基因基因集富集分析（GSEA）。通过受试者工作特征（ROC）曲线验证三个枢纽基因的诊断性能，随后构建列线图模型并使用ROC曲线和校准曲线评估其效能。此外，利用比较毒理基因组学数据库探索这些枢纽基因与妊娠相关疾病之间的关联。最后，进行定量聚合酶链反应（qPCR）和细胞功能实验以验证枢纽基因的表达和功能特征。本研究从基因表达综合数据库（GEO）获取RPL和APS数据集，共识别出10个共同的差异表达基因（DEGs），包括8个下调和2个上调基因。对这些共享DEGs的分析表明，免疫系统相关细胞和分子的失衡可能是APS和RPL病理生理过程中的共同特征。通过机器学习和列线图构建，我们识别出NAA30、ARHGAP44和SUGT1作为枢纽基因。进一步实验揭示SUGT1在伴APS的RPL中下调，并可影响滋养层细胞的生物学行为。本研究为伴APS的RPL的分子机制提供了有价值的见解，同时识别了该疾病的潜在生物标志物和治疗靶点。

Abstract

Recurrent Pregnancy Loss (RPL) is significantly associated with Antiphospholipid Syndrome (APS), yet the shared pathogenic mechanism remains unclear. This study identifies potential hub genes for APS-related RPL, providing insights into its pathogenesis and potential diagnostic strategies. We retrieved APS and RPL datasets from the GEO database and performed differential expression analysis to identify differentially expressed genes. Thereafter, Gene Ontology (GO) analysis, pathway enrichment analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) analysis were carried out. For hub gene screening, Least Absolute Shrinkage and Selection Operator (LASSO) regression, Gaussian mixture model (GMM), and RandomForest (RF) algorithms were applied to analyze the gene expression profiles of RPL. The CIBERSORT tool was utilized to assess the immune cell infiltration levels in samples from RPL and APS patients, while the "clusterProfiler" R package was employed to perform single-gene Gene Set Enrichment Analysis (GSEA) for each hub gene in both cohorts. Three hub genes were validated for diagnostic performance via receiver operating characteristic (ROC) curves, with a nomogram model subsequently constructed and its efficacy assessed using ROC and calibration curves. Additionally, the Comparative Toxicogenomics Database was used to explore the associations between these hub genes and pregnancy-related diseases. Finally, quantitative polymerase chain reaction (qPCR) and cell function experiments were conducted to validate the expression and functional characteristics of the hub genes. In this study, RPL and APS datasets were obtained from Gene Expression Omnibus (GEO) database, 10 common differentially expressed genes(DEGs)were identified, including eight downregulated and two upregulated genes. The analysis of these shared DEGs indicated that the imbalance of immune system-associated cells and molecules might represent a common characteristic in the pathophysiological processes of both APS and RPL. Through machine learning and the construction of a nomogram, we identified NAA30, ARHGAP44, and SUGT1 as hub genes. Further experiments revealed that SUGT1 was downregulated in RPL with APS and could influence the biological behavior of trophoblast cells. The present study provides valuable insights into the molecular mechanisms underlying RPL with APS, while also identifying potential biomarkers and therapeutic targets for this disease.

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