PDF(Pubmed)
Abstract：
Antiphospholipid syndrome (APS) is a group of clinical syndromes of thrombosis or adverse pregnancy outcomes caused by antiphospholipid antibodies, which increase the incidence of in vitro fertilization failure in patients with infertility. However, the common mechanism of repeated implantation failure (RIF) with APS is unclear. This study aimed to search for potential diagnostic genes and potential therapeutic targets for RIF with APS.
To obtain differentially expressed genes (DEGs), we downloaded the APS and RIF datasets separately from the public Gene Expression Omnibus database and performed differential expression analysis. We then identified the common DEGs of APS and RIF. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed, and we then generated protein-protein interaction. Furthermore, immune infiltration was investigated by using the CIBERSORT algorithm on the APS and RIF datasets. LASSO regression analysis was used to screen for candidate diagnostic genes. To evaluate the diagnostic value, we developed a nomogram and validated it with receiver operating characteristic curves, then analyzed these genes in the Comparative Toxicogenomics Database. Finally, the Drug Gene Interaction Database was searched for potential therapeutic drugs, and the interactions between drugs, genes, and immune cells were depicted with a Sankey diagram.
There were 11 common DEGs identified: four downregulated and seven upregulated. The common DEG analysis suggested that an imbalance of immune system-related cells and molecules may be a common feature in the pathophysiology of APS and RIF. Following validation, MARK2, CCDC71, GATA2, and KLRC3 were identified as candidate diagnostic genes. Finally, Acetaminophen and Fasudil were predicted as two candidate drugs.
Four immune-associated candidate diagnostic genes (MARK2, CCDC71, GATA2, and KLRC3) were identified, and a nomogram for RIF with APS diagnosis was developed. Our findings may aid in the investigation of potential biological mechanisms linking APS and RIF, as well as potential targets for diagnosis and treatment.
To obtain differentially expressed genes (DEGs), we downloaded the APS and RIF datasets separately from the public Gene Expression Omnibus database and performed differential expression analysis. We then identified the common DEGs of APS and RIF. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed, and we then generated protein-protein interaction. Furthermore, immune infiltration was investigated by using the CIBERSORT algorithm on the APS and RIF datasets. LASSO regression analysis was used to screen for candidate diagnostic genes. To evaluate the diagnostic value, we developed a nomogram and validated it with receiver operating characteristic curves, then analyzed these genes in the Comparative Toxicogenomics Database. Finally, the Drug Gene Interaction Database was searched for potential therapeutic drugs, and the interactions between drugs, genes, and immune cells were depicted with a Sankey diagram.
There were 11 common DEGs identified: four downregulated and seven upregulated. The common DEG analysis suggested that an imbalance of immune system-related cells and molecules may be a common feature in the pathophysiology of APS and RIF. Following validation, MARK2, CCDC71, GATA2, and KLRC3 were identified as candidate diagnostic genes. Finally, Acetaminophen and Fasudil were predicted as two candidate drugs.
Four immune-associated candidate diagnostic genes (MARK2, CCDC71, GATA2, and KLRC3) were identified, and a nomogram for RIF with APS diagnosis was developed. Our findings may aid in the investigation of potential biological mechanisms linking APS and RIF, as well as potential targets for diagnosis and treatment.
摘要：
■抗磷脂综合征（APS）是一组由抗磷脂抗体引起的血栓形成或不良妊娠结局的临床综合征，这增加了不孕患者体外受精失败的发生率。然而,APS反复植入失败(RIF)的常见机制尚不清楚.本研究旨在寻找APS联合RIF的潜在诊断基因和潜在治疗靶点。
■为了获得差异表达基因（DEGs），我们分别从公共基因表达综合数据库下载APS和RIF数据集,并进行差异表达分析.然后，我们确定了APS和RIF的常见DEG。进行了基因本体论和京都基因和基因组途径富集分析，然后我们产生了蛋白质-蛋白质相互作用。此外，通过使用CIBERSORT算法对APS和RIF数据集进行免疫浸润研究。LASSO回归分析用于筛选候选诊断基因。为了评估诊断价值，我们开发了一个列线图，并用接收器工作特性曲线对其进行了验证，然后在比较毒性基因组学数据库中分析了这些基因。最后,在药物基因相互作用数据库中搜索潜在的治疗药物，以及药物之间的相互作用，基因，用桑基图描绘了免疫细胞。
■确定了11个常见的DEG：4个下调，7个上调。常见的DEG分析表明，免疫系统相关细胞和分子的失衡可能是APS和RIF病理生理学的共同特征。验证后，MARK2、CCDC71、GATA2和KLRC3被鉴定为候选诊断基因。最后,对乙酰氨基酚和法舒地尔被预测为两种候选药物。
■确定了四个免疫相关的候选诊断基因（MARK2，CCDC71，GATA2和KLRC3），并绘制了带有APS诊断的RIF的列线图。我们的发现可能有助于研究APS和RIF之间潜在的生物学机制，以及潜在的诊断和治疗目标。
■为了获得差异表达基因（DEGs），我们分别从公共基因表达综合数据库下载APS和RIF数据集,并进行差异表达分析.然后，我们确定了APS和RIF的常见DEG。进行了基因本体论和京都基因和基因组途径富集分析，然后我们产生了蛋白质-蛋白质相互作用。此外，通过使用CIBERSORT算法对APS和RIF数据集进行免疫浸润研究。LASSO回归分析用于筛选候选诊断基因。为了评估诊断价值，我们开发了一个列线图，并用接收器工作特性曲线对其进行了验证，然后在比较毒性基因组学数据库中分析了这些基因。最后,在药物基因相互作用数据库中搜索潜在的治疗药物，以及药物之间的相互作用，基因，用桑基图描绘了免疫细胞。
■确定了11个常见的DEG：4个下调，7个上调。常见的DEG分析表明，免疫系统相关细胞和分子的失衡可能是APS和RIF病理生理学的共同特征。验证后，MARK2、CCDC71、GATA2和KLRC3被鉴定为候选诊断基因。最后,对乙酰氨基酚和法舒地尔被预测为两种候选药物。
■确定了四个免疫相关的候选诊断基因（MARK2，CCDC71，GATA2和KLRC3），并绘制了带有APS诊断的RIF的列线图。我们的发现可能有助于研究APS和RIF之间潜在的生物学机制，以及潜在的诊断和治疗目标。
