Preeclampsia (PE) is a multisystem disease that affects women during the pregnancy. Its pathogenicity remains unclear, and no definitive screening test can predict its occurrence so far. The aim of this study is to identify the critical genes that are involved in the pathogenicity of PE by applying integrated bioinformatic methods and to investigate the genes' diagnostic capability.Datasets that investigated PE have been downloaded from Gene Expression Omnibus (GEO) datasets. Differential gene expression, weighted gene co-expression analysis (WGCNA), protein-protein interaction (PPI) network construction, and finally, the calculation of area under the curve and Receiver operating characteristic curve (ROC) analysis were done for the potential hub genes. The results generated from the GSE186257 dataset (discovery cohort) were validated in the GSE75010 dataset (validation cohort). Following validation of the hub-genes, a multilayer regulatory network was constructed to include the up-stream regulatory elements (transcription factors and miRNAs) of the validated hub-genes.WGCNA revealed six modules that were significantly correlated with PE. A total of 231 differentially expressed genes (DEGs) were identified. DEGs were intersected with the WGCNA modules' genes, totalling 55 genes. These shared genes were used to construct the PPI network; subsequently, four genes, namely FLT1, HTRA4, LEP and PAPPA2, were identified as hub-genes for PE in the discovery cohort. The expressional of these four hub genes were validated in the validation cohort and found to be highly expressed. ROC analysis in both datasets revealed that all these genes had a significant PE diagnostic ability. The regulatory network showed that FLT1 gene is the most connected and regulated gene among the validated hub-genes.This integrated analysis revealed that FLT1, LEP, HTRA4 and PAPPA2 may be strongly involved in the pathogenicity of PE and act as promising biomarkers and potential therapeutic targets for PE.