Background. Preeclampsia (PE), which has a high incidence rate worldwide, is a potentially dangerous syndrome to pregnant women and newborns. However, the exact mechanism of its pathogenesis is still unclear. In this study, we used bioinformatics analysis to identify hub genes, establish a logistic model, and study immune cell infiltration to clarify the physiopathogenesis of PE. Methods. We downloaded the GSE75010 and GSE10588 datasets from the GEO database and performed weighted gene coexpression network analysis (WGCNA) as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The online search tool for the retrieval of interacting genes and Cytoscape software were used to identify hub genes, which were then used to establish a logistic model. We also analyzed immune cell infiltration. Finally, we verified the expression of the genes included in the predictive model via RT-PCR. Results. A total of 100 and 212 differently expressed genes were identified in the GSE75010 and GSE10588 datasets, respectively, and after overlapping with WGCNA results, 17 genes were identified. KEGG and GO analyses further indicated the involvement of these genes in bioprocesses, such as gonadotropin secretion, immune cell infiltration, and the SMAD and MAPK pathways. Additionally, protein-protein interaction network analysis identified 10 hub genes, six (FLT1, FLNB, FSTL3, INHA, TREM1, and SLCO4A1) of which were used to establish a logistic model for PE. RT-PCR analysis also confirmed that, except FSTL3, these genes were upregulated in PE. Our results also indicated that macrophages played the most important role in immune cell infiltration in PE. Conclusion. This study identified 10 hub genes in PE and used 6 of them to establish a logistic model and also analyzed immune cell infiltration. These findings may enhance the understanding of PE and enable the identification of potential therapeutic targets for PE.
Background Pre-eclampsia (PE) is a common condition in pregnancy; however, methods for early diagnosis and effective treatment options are lacking. Ferroptosis is a newly identified iron-dependent cell death pathway. The aim of this study was to investigate the role of ferroptosis-related genes in PE, the underlying mechanism, and their potential diagnostic value using a bioinformatics approach. Methods We downloaded the GSE48424 and GSE98224 datasets from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between PE and healthy pregnancy samples were identified in the GSE48424 dataset and subjected to weighted gene co-expression network analysis; the most relevant modules were intersected with known ferroptosis-related genes to distinctly identify the role of ferroptosis in PE. We further searched transcription factors and microRNAs that are predicted to regulate these ferroptosis-related genes, and patients in the GSE48424 dataset were divided into two groups according to high or low expression of the key ferroptosis-related genes associated with PE. To obtain robust key ferroptosis-related genes in PE, we validated their expression levels in the external dataset GSE98224. Finally, we performed a reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assay of these genes to evaluate their expression in the placenta samples of patients with PE and normal pregnancy. Results The most relevant module of PE in the GSE48424 dataset comprising the 565 identified DEGs contained a total of 3661 genes. After overlapping, we obtained six ferroptosis-related genes involved in PE. Among these genes, patients with PE displaying lower expression levels of NOS2 and higher expression levels of PTGS2 had a higher ferroptosis potential index. The expression pattern of NOS2 was consistent in the GSE48424 and GSE98224 datasets. RT-qPCR data confirmed that NOS2 expression was more significantly elevated in patients with PE than in those with a normal pregnancy. Conclusions Our study explored the diagnostic value of ferroptosis-related genes in PE, and identified NOS2 as the key gene linking ferroptosis and PE, suggesting a new candidate biomarker for early PE diagnosis.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.