Identification of potential crucial genes associated with early-onset preeclampsia via bioinformatic analysis.

Abstract

Early-onset preeclampsia is a pregnancy complication associated with high maternal and perinatal morbidity, mortality. Intense efforts have been made to elucidate the pathogenesis, but the molecular mechanism is still elusive. This study aimed to identify potential key genes related to early-onset preeclampsia, and to obtain a better understanding of the molecular mechanisms of this disease. We performed a multi-step integrative bioinformatics analysis of microarray dataset GSE74341 downloaded from Gene Expression Omnibus (GEO) database including 7 early-onset preeclampsia and 5 gestational age matched normotensive controls. The differentially expressed genes (DEGs) were identified using the "limma" package, and their potential functions were predicted by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Furthermore, the protein-protein interaction network (PPI) was obtained from the STRING database and the PPI network was visualized by Cytoscape software. Then, hub modules and hub genes were screened out from the PPI network, and enrichment analysis was performed for them. Also, validation of hub genes expression in early-onset PE was down by using microarray dataset GSE44711. A total of 628 DEGs (256 down- and 372 up-regulated) were identified in early-onset PE compared to controls. A total of 4 significant hub modules and 26 significant hub genes were identified. In conclusion, the DEGs related to cell-cell or cell-extracellular matrix interaction (ITGA5, SPP1, LUM, VCAN, APP), placenta metabolic or oxidative stress (CCR7, NT5E, CYBB) were predicted to be newly potential crucial genes that may play significant roles in the pathogenesis of early-onset PE.