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

Abstract

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 learning models. The ResNet model was established to score the hub genes. The prediction accuracy was assessed by receiver operating characteristic (ROC) curves and an external dataset. We successfully identified five key DECREGs and two pathological clusters in PE, each with distinct immune profiles and biological characteristics. Subsequently, the RF model was deemed the most optimal model for the identification of PE with a large area under the curve (AUC = 0.733). The five genes that ranked highest in the RF machine learning model were considered to be predictor genes. The calibration curve demonstrated a high level of accuracy in aligning the predicted outcomes with the actual outcomes. We validate the ResNet model using the ROC curve with the area under the curve (AUC = 0.82). Cuproptosis and immune infiltration may play an important role in the pathogenesis of PE. The present study elucidated that GSTA4, KCNK5, APLNR, IKZF2, and CAP2 may be potential markers of cuproptosis-associated PE and are considered to play a significant role in the initiation and development of cuproptosis-induced PE.