Identification and Exploration of Novel Predictive Markers, Tumor-Related Biomarkers, in Preeclampsia

Abstract

Abstract Purpose: To explore whether significant differentially expressed genes (DEGs) in the blood circulation of preeclampsia patients can truly reflect placental function, predict the development of disease, and explain the pathogenesis of preeclampsia. Methods: The main method of our study is biological information technology.The microarray dataset GSE48424 was downloaded from the Gene Expression Omnibus (GEO) database. The DEGs were identified by GEO2R, and functional enrichment analyses were performed by the R package/a free online platform (https://www.bioinformatics.com.cn). The protein‒protein interaction network (PPI) was constructed, and module analysis was performed using STRING and Cytoscape. R language was used for the visualization of the results. GraphPad Prism was used to generate graphs. logFC (fold change) >0.58 and adj. P values<0.05 were considered statistically significant. Results: A total of 178 DEGs were obtained, consisting of 121 downregulated genes and 57 upregulated genes. Five hub genes were identified and considered to be significant according to scores calculated by Cytoscape. The enriched functions and pathways analysis revealed that these genes were mainly enriched in regulation of the ERK1 and ERK2 cascade. Differential expression of TLR4, SMAD4, POU5F1, MAP2K1, and RAB1A was significant in blood circulation and placental tissues of preeclampsia. Conclusion: The TLR4, SMAD4, POU5F1, MAP2K1, and RAB1A genes may be predicted targets to prevent preeclampsia development. Moreover, these genes could truly reflect placental function and nicely explain the pathogenesis of preeclampsia from a molecular biology perspective.