Abstract：
This study aimed to investigate immune score and stromal score-related signatures associated with preeclampsia (PE) and identify key genes for diagnosing PE using bioinformatics analysis. Four microarray datasets, GSE75010, GSE25906, GSE44711, and GSE10588 were obtained from the Gene Expression Omnibus database. GSE75010 was utilized for differential expressed gene (DEGs) analysis. Subsequently, bioinformatic tools such as gene ontology, Kyoto Encyclopedia of Genes and Genomes, weighted gene correlation network analysis, and gene set enrichment analysis were employed to functionally characterize candidate target genes involved in the pathogenesis of PE. The least absolute shrinkage and selection operator regression approach was employed to identify crucial genes and develop a predictive model. This method also facilitated the creation of receiver operating characteristic (ROC) curves, enabling the evaluation of the model\'s precision. Furthermore, the model underwent external validation through the other three datasets. A total of 3286 DEGs were identified between normal and PE tissues. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed enrichments in functions related to cell chemotaxis, cytokine binding, and cytokine-cytokine receptor interaction. weighted gene correlation network analysis identified 2 color modules strongly correlated with immune and stromal scores. After intersecting DEGs with immune and stromal-related genes, 13 genes were selected and added to the least absolute shrinkage and selection operator regression. Ultimately, 7 genes were screened out to establish the risk model for discriminating preeclampsia from controls, with each gene having an area under the ROC curve >0.70. The constructed risk model demonstrated that the area under the ROC curves in internal and the other three external datasets were all greater than 0.80. A 7-gene risk signature was identified to build a potential diagnostic model and performed well in the external validation group for PE patients. These findings illustrated that immune and stromal cells played essential roles in PE during its progression.
摘要：
本研究旨在研究与先兆子痫（PE）相关的免疫评分和基质评分相关的特征，并使用生物信息学分析确定诊断PE的关键基因。四个微阵列数据集，GSE75010、GSE25906、GSE44711和GSE10588从基因表达综合数据库获得。GSE75010用于差异表达基因(DEGs)分析。随后，生物信息学工具，如基因本体论，京都基因和基因组百科全书，加权基因相关网络分析，和基因集富集分析用于功能表征参与PE发病机理的候选靶基因。采用最小绝对收缩和选择算子回归方法来识别关键基因并建立预测模型。该方法还促进了接收器工作特性（ROC）曲线的创建，启用模型精度的评估。此外，该模型通过其他三个数据集进行了外部验证.在正常和PE组织之间总共鉴定了3286个DEGs。基因本体论和京都百科全书的基因和基因组分析揭示了与细胞趋化性相关的功能的丰富，细胞因子结合，和细胞因子-细胞因子受体相互作用。加权基因相关网络分析确定了2个颜色模块与免疫和基质评分密切相关。在将DEGs与免疫和基质相关基因相交后，选择13个基因并添加到最小绝对收缩和选择算子回归。最终,筛选出7个基因,建立区分子痫前期与对照组的风险模型,每个基因的ROC曲线下面积>0.70。构建的风险模型表明，内部和其他三个外部数据集的ROC曲线下面积均大于0.80。鉴定了7基因风险特征以建立潜在的诊断模型，并在PE患者的外部验证组中表现良好。这些发现表明，免疫和基质细胞在PE的发展过程中发挥了重要作用。
