Identification of F13A1 and SCCPDH as Potential Diagnostic Markers for Preeclampsia

Abstract

Abstract Preeclampsia (PE) is a serious condition that affects pregnant women worldwide. This study aimed to identify diagnostic candidate genes for PE. Gene expression from a PE cohort and controls was analyzed using Limma and weighted gene coexpression network analysis (WGCNA) to identify differentially expressed genes (DEGs) and module genes. Machine learning techniques were then used to identify immune-associated hub genes for PE diagnosis, resulting in the selection of two significant genes (F13A1 and SCCPDH). A nomogram and receiver operating characteristic (ROC) curve were developed to evaluate the diagnostic value, which was high for both hub genes (AUC: 0.9 and 0.88, respectively). A deconvolution study also identified dysregulated immune cells in PE. Overall, this study provides potential diagnostic candidate genes for PE patients and highlights the importance of early diagnosis and treatment for this condition.