DataSheet2_Development and validation of preeclampsia predictive models using key genes from bioinformatics and machine learning approaches.zip

BackgroundPreeclampsia (PE) poses significant diagnostic and therapeutic challenges. This study aims to identify novel genes for potential diagnostic and therapeutic targets, illuminating the immune mechanisms involved.
MethodsThree GEO datasets were analyzed, merging two for training set, and using the third for external validation. Intersection analysis of differentially expressed genes (DEGs) and WGCNA highlighted candidate genes. These were further refined through LASSO, SVM-RFE, and RF algorithms to identify diagnostic hub genes. Diagnostic efficacy was assessed using ROC curves. A predictive nomogram and fully Connected Neural Network (FCNN) were developed for PE prediction. ssGSEA and correlation analysis were employed to investigate the immune landscape. Further validation was provided by qRT-PCR on human placental samples.
ResultFive biomarkers were identified with validation AUCs: CGB5 (0.663, 95% CI: 0.577-0.750), LEP (0.850, 95% CI: 0.792-0.908), LRRC1 (0.797, 95% CI: 0.728-0.867), PAPPA2 (0.839, 95% CI: 0.775-0.902), and SLC20A1 (0.811, 95% CI: 0.742-0.880), all of which are involved in key biological processes. The nomogram showed strong predictive power (C-index 0.873), while FCNN achieved an optimal AUC of 0.911 (95% CI: 0.732-1.000) in five-fold cross-validation. Immune infiltration analysis revealed the importance of T cell subsets, neutrophils, and NK cells in PE, linking these genes to immune mechanisms underlying PE pathogenesis.
ConclusionCGB5, LEP, LRRC1, PAPPA2, and SLC20A1 are validated as key diagnostic biomarkers for PE. Nomogram and FCNN could credibly predict PE. Their association with immune infiltration underscores the crucial role of immune responses in PE pathogenesis.

【背景】子痫前期（Preeclampsia, PE）面临显著的诊疗挑战。本研究旨在筛选可作为潜在诊疗靶点的新型基因，阐明子痫前期发生的免疫机制。
【方法】本研究分析了3个基因表达综合（GEO）数据集，将其中2个合并作为训练集，剩余1个用作外部验证集。通过差异表达基因（differentially expressed genes, DEGs）与加权基因共表达网络分析（WGCNA）的交集分析筛选候选基因，进一步借助最小绝对收缩和选择算子（LASSO）、支持向量机-递归特征消除（SVM-RFE）及随机森林（RF）算法进行筛选，以确定诊断核心基因。采用受试者工作特征（ROC）曲线评估诊断效能，构建预测列线图与全连接神经网络（FCNN）用于子痫前期预测。运用单样本基因集富集分析（ssGSEA）与相关分析探究免疫微环境特征，并通过人类胎盘样本的实时定量聚合酶链反应（qRT-PCR）进行进一步验证。
【结果】本研究共筛选得到5个生物标志物，其验证受试者工作特征曲线下面积（AUC）分别为：CGB5（0.663，95%置信区间：0.577~0.750）、LEP（0.850，95%置信区间：0.792~0.908）、LRRC1（0.797，95%置信区间：0.728~0.867）、PAPPA2（0.839，95%置信区间：0.775~0.902）及SLC20A1（0.811，95%置信区间：0.742~0.880），上述标志物均参与关键生物学过程。该预测列线图表现出优异的预测性能（C指数为0.873），全连接神经网络在五折交叉验证中获得最优AUC值0.911（95%置信区间：0.732~1.000）。免疫浸润分析揭示了T细胞亚群、中性粒细胞及自然杀伤（NK）细胞在子痫前期中的重要作用，将上述基因与子痫前期发病的免疫机制建立关联。
【结论】CGB5、LEP、LRRC1、PAPPA2及SLC20A1被验证为子痫前期的关键诊断生物标志物。预测列线图与全连接神经网络可可靠预测子痫前期发病风险，上述基因与免疫浸润的关联进一步证实了免疫应答在子痫前期发病机制中的核心作用。