Wang Zhichao, Cheng Long, Li Guanghui, Cheng Huiyan
Department of Pediatric Surgery, First Hospital of Jilin University, Changchun, 130021, Jilin, China.
Department of Intensive Care Unit, First Hospital of Jilin University, Changchun, 130031, Jilin, China.
Sci Rep. 2025 Jan 13;15(1):1767. doi: 10.1038/s41598-025-86442-9.
Preeclampsia (PE) is a major pregnancy-specific cardiovascular complication posing latent life-threatening risks to mothers and neonates. The contribution of immune dysregulation to PE is not fully understood, highlighting the need to explore molecular markers and their relationship with immune infiltration to potentially inform therapeutic strategies. We used bioinformatics tools to analyze gene expression data from the Gene Expression Omnibus (GEO) database using the GEOquery package in R. Differential expression analysis was performed using the DESeq2 and limma packages, followed by analysis of variance to identify immune-related differentially expressed genes (DEGs). Several machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), bagged trees, and random forest (RF), were used to select immune-related signaling genes closely associated with the occurrence of PE. Our analysis identified 34 immune source-related DEGs. Using the identified PE- and immune source-related genes, we constructed a diagnostic forecasting model employing several ML algorithms. We identified six types of statistically significant immune cells in patients with PE and discovered a strong relationship between biomarkers and immune cells. Moreover, the immune-derived hub genes for PE exhibited strong binding capabilities with drugs, such as alitretinoin, tretinoin, and acitretin. This study presents a robust prediction model for PE that integrates multiple machine learning-derived immune-related biomarkers. Our results indicate that these biomarkers may outperform previously reported molecular signatures in predicting PE and provide insights into the mechanisms underlying immune dysregulation in PE. Further validation in larger cohorts could lead to their clinical application in PE prediction and treatment.
子痫前期（PE）是一种主要的妊娠特异性心血管并发症，对母亲和新生儿构成潜在的生命威胁风险。免疫失调对PE的作用尚未完全了解，这凸显了探索分子标志物及其与免疫浸润的关系以指导治疗策略的必要性。我们使用生物信息学工具，通过R中的GEOquery软件包分析来自基因表达综合数据库（GEO）的基因表达数据。使用DESeq2和limma软件包进行差异表达分析，随后进行方差分析以鉴定免疫相关差异表达基因（DEG）。几种机器学习算法，包括最小绝对收缩和选择算子（LASSO）、袋装树和随机森林（RF），被用于选择与PE发生密切相关的免疫相关信号基因。我们的分析确定了34个免疫源相关的DEG。利用鉴定出的与PE和免疫源相关的基因，我们构建了一个采用多种机器学习算法的诊断预测模型。我们在PE患者中鉴定出六种具有统计学意义的免疫细胞，并发现生物标志物与免疫细胞之间存在密切关系。此外，PE的免疫源性枢纽基因与阿利维A酸、维甲酸和阿维A等药物表现出很强的结合能力。本研究提出了一个强大的PE预测模型，该模型整合了多种机器学习衍生的免疫相关生物标志物。我们的结果表明，这些生物标志物在预测PE方面可能优于先前报道的分子特征，并为PE免疫失调的潜在机制提供了见解。在更大的队列中进一步验证可能会使其在PE预测和治疗中得到临床应用。
