Department of Clinical Laboratory, The Fourth Hospital of Shijiazhuang, Shijiazhuang, China.
Department of Anesthesiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Medicine (Baltimore). 2024 May 24;103(21):e38260. doi: 10.1097/MD.0000000000038260.
Preeclampsia (PE) is a pregnancy complication characterized by placental dysfunction. However, the relationship between maternal blood markers and PE is unclear. It is helpful to improve the diagnosis and treatment of PE using new biomarkers related to PE in the blood. Three PE-related microarray datasets were obtained from the Gene Expression Synthesis database. The limma software package was used to identify differentially expressed genes (DEGs) between PE and control groups. Least absolute shrinkage and selection operator regression, support vector machine, random forest, and multivariate logistic regression analyses were used to determine key diagnostic biomarkers, which were verified using clinical samples. Subsequently, functional enrichment analysis was performed. In addition, the datasets were combined for immune cell infiltration analysis and to determine their relationships with core diagnostic biomarkers. The diagnostic performance of key genes was evaluated using the receiver operating characteristic (ROC) curve, C-index, and GiViTi calibration band. Genes with potential clinical applications were evaluated using decision curve analysis (DCA). Seventeen DEGs were identified, and 6 key genes (FN1, MYADM, CA6, PADI4, SLC4A10, and PPP4R1L) were obtained using 3 types of machine learning methods and logistic regression. High diagnostic performance was found for PE through evaluation of the ROC, C-index, GiViti calibration band, and DCA. The 2 types of immune cells (M0 macrophages and activated mast cells) were significantly different between patients with PE and controls. All of these genes except SLC4A10 showed significant differences in expression levels between the 2 groups using quantitative reverse transcription-polymerase chain reaction. This model used 6 maternal blood markers to predict the occurrence of PE. The findings may stimulate ideas for the treatment and prevention of PE.
子痫前期(PE)是一种以胎盘功能障碍为特征的妊娠并发症。然而,母体血液标志物与 PE 之间的关系尚不清楚。使用与血液中 PE 相关的新生物标志物来改善 PE 的诊断和治疗是有帮助的。从基因表达综合数据库中获得了 3 个与 PE 相关的微阵列数据集。使用 limma 软件包来识别 PE 组和对照组之间的差异表达基因(DEGs)。使用最小绝对收缩和选择算子回归、支持向量机、随机森林和多元逻辑回归分析来确定关键诊断生物标志物,并使用临床样本进行验证。随后进行功能富集分析。此外,对数据集进行了合并,以进行免疫细胞浸润分析,并确定它们与核心诊断生物标志物的关系。使用接收器工作特征(ROC)曲线、C 指数和 GiViTi 校准带评估关键基因的诊断性能。使用决策曲线分析(DCA)评估具有潜在临床应用的基因。鉴定出 17 个 DEGs,并使用 3 种机器学习方法和逻辑回归获得了 6 个关键基因(FN1、MYADM、CA6、PADI4、SLC4A10 和 PPP4R1L)。通过 ROC、C 指数、GiViti 校准带和 DCA 的评估,发现该模型对 PE 具有较高的诊断性能。PE 患者和对照组之间的 2 种免疫细胞(M0 巨噬细胞和活化的肥大细胞)存在显著差异。除 SLC4A10 外,所有这些基因在 2 组之间的表达水平均存在显著差异。该模型使用 6 个母体血液标志物来预测 PE 的发生。这些发现可能为 PE 的治疗和预防提供思路。