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Yu Xia,1– 3 Yu-Dong Zhao,4 Gui-Xiang Sun,1,2 Shuai-Shuai Xia,1 Zheng-Wang Yang3 1Provincial Key Laboratory of TCM Diagnostics, Hunan University of Chinese Medicine, Changsha, Hunan Province, 410208, People’s Republic of China; 2Institute of Chinese Medicine Diagnosis, Hunan University of Chinese Medicine, Changsha, Hunan Province, 410208, People’s Republic of China; 3Department of Obstetrics and Gynecology, The First Hospital of Hunan University of Chinese Medicine, Changsha, Hunan Province, 410007, People’s Republic of China; 4School of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, People’s Republic of ChinaCorrespondence: Gui-Xiang SunProvincial Key Laboratory of TCM Diagnostics, Hunan University of Chinese Medicine, No. 300, Xueshi Road, Yuelu District, Changsha, Hunan Province, 410208, People’s Republic of China, Tel +86-13787272837, Email 84663423@qq.comObjective: Preeclampsia (PE) is a pregnancy-specific multisystem disease as well as an important cause of maternal and perinatal death. This study aimed to analyze the placental transcriptional data and clinical information of PE patients available in the published database and predict the target genes for prevention of PE.Methods: The clinical information and corresponding RNA data of PE patients were downloaded from the GEO database. Cluster analysis was performed to examine the correlation between different genotyping genes and clinical manifestations. Then, bioinformatic approaches including GO, KEGG, WGCNA, and GSEA were employed to functionally characterize candidate target genes involved in pathogenesis of PE.Results: Two PE datasets GSE60438 and GSE75010 were obtained and combined, thereby providing the data of 205 samples in total (100 non-PE and 105 PE samples). After eliminating the batch effect, we grouped and analyzed the integrated data, and further performed GSEA analysis. It was found that the genes in group 1 and group 2 were different from t