Objective To mine valid information in public databases through bioinformatics analysis and machine learning models and to identify candidate genes related to preeclampsia,so as to improve the accuracy of early diagnosis and provide targets for pathogenesis,diagnosis and treatment research.Methods The RNA-seq datasets of placental tissue samples of preeclampsia patients and healthy pregnant women were retrieved from the Gene Expression Omnibus,and the gene expression matrix was obtained after data download,quality control,comparison and quantification through bioinformation analysis.The differentially expressed genes were screened by DESeq2 1.38.3,the enrichment pathway was determined using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes,the co-expression network was constructed using weighted gene co-expression network analysis(WGCNA),and the machine learning prediction model was established by random forest algorithm.Results A total of 49 common differentially expressed genes were screened from placental tissue samples of 156 pregnant women(70 preeclampsia patients and 86 healthy pregnant women)in 4 datasets and they were significantly enriched in extracellular regions,positive regulation pathway of follicle-stimulating hormone secretion,hormone activity pathway,and cytokine-cytokine receptor interaction pathway,etc.The 49 differentially expressed genes were categorized into 7 co-expression modules by WGCNA,and key modules highly related to preeclampsia were identified.Six candidate key genes(fms related receptor tyrosine kinase 1[FLT1],pappalysin 2[PAPPA2],protein phosphatase 1 regulatory inhibitor subunit 1C[PPP1R1C],myosin ⅦB[MYO7B],long intergenic non-protein coding RNA 2009[LINC02009],and inhibin subunit α[INHA])were screened.The random forest model based on these 6 key genes had good predictive value for preeclampsia(area under curve was 0.978).Conclusion Preeclampsia may be associated with genes for hormone secretion,immune response,angiogenic factors,pregnancy-associated plasma proteins,and inhibin,and these genes may be candidate diagnostic markers of preeclampsia.