基于多组学及机器学习方法筛选子痫前期的外周循环诊断标志物

Screening peripheral circulation diagnostic markers for preeclampsia based on multi-omics and machine learning methods

目的分析子痫患者的胎盘和外周循环转录组数据,筛选子痫的早期诊断标志物。方法从高通量基因表达数据库中获取子痫患者的临床信息和相应的芯片表达谱数据。通过差异基因表达分析、富集分析和加权基因共表达网络分析(WGCNA)等多组学方法,寻找候选的诊断标志物,并探究子痫发生的潜在机制。然后,采用组合的机器学习方法,包括随机森林树、支持向量机和最小绝对收缩和选择操作符方法,对候选标志物进一步筛选。最后,使用外周循环数据集对筛选后的诊断标志物进行验证。结果差异基因表达分析显示,在子痫中有71个基因上调,21个基因下调。WGCNA分析显示,子痫的发生与青色和绿松石色模块高度相关。候选诊断标志物的富集分析揭示,细胞周期、细胞衰老和免疫相关通路的改变可能是子痫发病的主要原因。经过机器学习的进一步筛选,确定COL17A1和DIO2基因在子痫患者的外周血中显著上调,并具有显著的诊断效能。结论COL17A1和DIO2基因可作为早期诊断子痫的外周循环诊断标志物。

Objective To identify early diagnostic biomarkers for preeclampsia by analyzing the placental and peripheral circulatory transcriptomic data of patients.Methods Clinical information and microarray expression profiles of preeclampsia patients were sourced from high-throughput gene expression databases.Multi-omics approaches,including differential gene expression analysis,enrichment analysis,and weighted gene co-expression network analysis(WGCNA),were utilized to identify candidate diagnostic markers and explore potential mechanisms of preeclampsia.Subsequently,a combination of machine learning techniques,including random forest,support vector machine,and least absolute shrinkage and selection operator(LASSO),were employed for further screening of these candidates.Finally,the selected diagnostic markers were validated using a peripheral circulation dataset.Results Differential gene expression analysis revealed 71 upregulated and 21 downregulated genes in preeclampsia.WGCNA linked the onset of preeclampsia with blue and teal modules.Enrichment analysis of candidate biomarkers suggested changes in cell cycle,cellular senescence,and immune-related pathways as primary drivers of preeclampsia.Further refinement through machine learning identified significant upregulation of COL17A1 and DIO2 genes in the peripheral blood of patients,demonstrating robust diagnostic potential.Conclusions COL17A1 and DIO2 genes can be used as peripheral circulating diagnostic markers for the early diagnosis of eclampsia.