Wang Ying, Li Zhen, Song Guiyu, Wang Jun
Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
Front Genet. 2020 Dec 1;11:579709. doi: 10.3389/fgene.2020.579709. eCollection 2020.
Preeclampsia is the main cause of maternal mortality due to a lack of diagnostic biomarkers and effective prevention and treatment. The immune system plays an important role in the occurrence and development of preeclampsia. This research aimed to identify significant immune-related genes to predict preeclampsia and possible prevention and control methods.
Differential expression analysis between normotensive and PE pregnancies was performed to identify significantly changed immune-related genes. Generalized linear model (GLM), random forest (RF), and support vector machine (SVM) models were established separately to screen the most suitable biomarkers for the diagnosis of PE among these significantly changed immune-related genes. The consensus clustering method was used to divide the PE cases into several subgroups to explore the function of the significantly changed immune-related genes in PE.
Thirteen significantly changed immune-related genes were obtained by the differential expression analysis. RF was the best model and was used to select the four most important explanatory variables (CRH, PI3, CCL18, and CCL2) to diagnose PE. A nomogram model was constructed to predict PE based on these four variables. The decision curve analysis (DCA) and clinical impact curves revealed that PE patients could significantly benefit from this nomogram. Consensus clustering analysis of the 13 differentially expressed immune-related genes (DIRGs) was used to identify 3 subgroups of PE pregnancies with different clinical outcomes and immune cell infiltration.
Our study identified four immune-related genes to predict PE and three subgroups of PE with different clinical outcomes and immune cell infiltration. Future studies on the three subgroups may provide direction for individualized treatment of PE patients.
由于缺乏诊断生物标志物以及有效的预防和治疗方法，子痫前期是孕产妇死亡的主要原因。免疫系统在子痫前期的发生和发展中起重要作用。本研究旨在识别重要的免疫相关基因以预测子痫前期及可能的预防和控制方法。
对血压正常和子痫前期妊娠进行差异表达分析，以识别显著变化的免疫相关基因。分别建立广义线性模型（GLM）、随机森林（RF）和支持向量机（SVM）模型，在这些显著变化的免疫相关基因中筛选用于诊断子痫前期的最合适生物标志物。采用一致性聚类方法将子痫前期病例分为几个亚组，以探讨显著变化的免疫相关基因在子痫前期中的功能。
通过差异表达分析获得了13个显著变化的免疫相关基因。RF是最佳模型，用于选择四个最重要的解释变量（CRH、PI3、CCL18和CCL2）来诊断子痫前期。基于这四个变量构建了列线图模型以预测子痫前期。决策曲线分析（DCA）和临床影响曲线显示子痫前期患者可从该列线图中显著获益。对13个差异表达的免疫相关基因（DIRGs）进行一致性聚类分析，以识别具有不同临床结局和免疫细胞浸润的子痫前期妊娠的3个亚组。
我们的研究识别了四个免疫相关基因来预测子痫前期以及具有不同临床结局和免疫细胞浸润的子痫前期的3个亚组。未来对这三个亚组的研究可能为子痫前期患者的个体化治疗提供方向。
