Bioinformatics analysis combined with clinical sample screening reveals that leptin may be a biomarker of preeclampsia

Introduction: Preeclampsia (PE) is a gestational hypertensive disease with unclear pathogenesis. This study aimed to identify the genes that play an important role in determining the pathogenesis of PE using bioinformatics analysis and fundamental researches. Materials and methods: Datasets from the Gene Expression Omnibus (GEO) database were used to screen for differentially expressed genes (DEGs). The NCBI, SangerBox, and other databases were used to analyze the functions of the DEGs. Targetscan7, miRWalk, ENCORI, DIANA TOOLS, CircBank databases, and the Cytoscape tool were used to construct the lncRNA/circRNA-miRNA- LEP network. SRAMP, RPISeq, RBPsuite, and catRPAID were used to analyze the RNA modifications of LEP. Immune cell infiltration was analyzed using the dataset GSE75010. Placental tissues from normal pregnant women and PE patients were collected, screened for gene expression using reverse transcription quantitative polymerase chain reaction (RT-qPCR) and western blotting. The results were further verified in HTR-8/SVneo cell line hypoxia model and PE mouse model. Results: Our analyses revealed that LEP was significantly upregulated in eight datasets. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses indicated that LEP was involved in the JAK/STAT signaling pathway, angiogenesis, and placental development. Immune cell infiltration analysis showed that M1 and M2 macrophages differed between normal pregnancies and those in PE patients. A competing endogenous RNA (ceRNA) network was constructed, and proteins interacting with LEP were identified. RNA modification sites of LEP were also identified. Finally, the overexpression of LEP in PE was confirmed in clinical samples, HTR-8/SVneo cell line and PE mouse model. Conclusion: Our results indicate that LEP overexpression is associated with PE and may be a potential diagnostic marker and therapeutic target.