Identification and Immunological Characterization of Cuproptosis Related Genes in Preeclampsia Using Bioinformatics Analysis and Machine Learning

作者：Tiantian Yu, Guiying Wang, Xia Xu, Jian­ying Yan · 发表于：Journal of Clinical Hypertension · 年份：2025 · DOI：10.1111/jch.14982 · 被引用次数：3 · 研究领域：Pregnancy and preeclampsia studies、MicroRNA in disease regulation、Birth, Development, and Health

Preeclampsia (PE) is a pregnancy-specific disorder characterized by an unclearly understood pathogenesis and poses a great threat to maternal and fetal safety. Cuproptosis, a novel form of cellular death, has been implicated in the advancement of various diseases. However, the role of cuproptosis and immune-related genes in PE is unclear. The current study aims to elucidate the gene expression matrix and immune infiltration patterns of cuproptosis-related genes (CRGs) in the context of PE. The GSE98224 dataset was obtained from the Gene Expression Omnibus (GEO) database and utilized as the internal training set. Based on the GSE98224 dataset, we explored the differentially expressed cuproptosis related genes (DECRGs) and immunological composition. We identified 10 DECRGs conducted Gene Ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and a protein-protein interaction (PPI) network. Furthermore, patients with PE were categorized into two distinct clusters, and an investigation was conducted to examine the status of immune cell infiltration. Additionally, the application of Weighted Gene Co-expression Network Analysis (WGCNA) was utilized to differentiate modules consisting of co-expressed genes and conduct clustering analysis. The intersecting genes were obtained by intersecting differently expressed genes in PE and PE clusters. The most precise forecasting model was chosen by evaluating the effectiveness of four machine learn...