ArticleFrontiers in molecular biosciences2024
Analyzing molecular signatures in preeclampsia and fetal growth restriction: Identifying key genes, pathways, and therapeutic targets for preterm birth.
Article in Frontiers in molecular biosciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- IGFBP3 repression driven by inflammation links air pollution to placental and developmental defects.EMBO molecular medicine · 2026Article
- Hydrogen sulfide modulates gene networks in hypoxia/reoxygenation-stressed trophoblasts: insights from transcriptome profiling.Frontiers in bioinformatics · 2026Article
- Lipidomic and transcriptomic profiling reveal alterations in the coexistence of gestational diabetes mellitus and preeclampsia impacting maternal and neonatal outcomes.Scientific reports · 2025Article
- Effect of THBS1 and P21 Gene Polymorphisms and Their Interaction On Risk of Pre-eclampsia.Reproductive sciences (Thousand Oaks, Calif.) · 2025Article
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Authors and funding
6 authors.
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Abstract
Background: Intrauterine growth restriction (IUGR) and preeclampsia (PE) are intricately linked with specific maternal health conditions, exhibit shared placental abnormalities, and play pivotal roles in precipitating preterm birth (PTB) incidences. However, the molecular mechanism underlying the association between PE and IUGR has not been determined. Therefore, we aimed to analyze the data of females with PE and those with PE + IUGR to identify the key gene(s), their molecular pathways, and potential therapeutic interactions. Methods: In this study, a comprehensive relationship analysis of both PE and PE + IUGR was conducted using RNA sequence datasets. Using two datasets (GSE148241 and GSE114691), differential gene expression analysis via DESeq2 through R-programming was performed. Gene set enrichment analysis was performed using ClusterProfiler, protein‒protein interaction (PPI) networks were constructed, and cluster analyses were conducted using String and MCODE in Cytoscape. Functional enrichment analyses of the resulting subnetworks were performed using ClueGO software. The hub genes were identified under both conditions using the CytoHubba method. Finally, the most common hub protein was docked against a library of bioactive flavonoids and PTB drugs using the PyRx AutoDock tool, followed by molecular dynamic (MD) simulation analysis. Pharmacokinetic analysis was performed to determine the ADMET properties of the compounds using pkCSM. Results: We identified eight hub genes highly expressed in the case of PE, namely, PTGS2, ENG, KIT, MME, CGA, GAPDH, GPX3, and P4HA1, and the network of the PE + IUGR gene set demonstrated that nine hub genes were overexpressed, namely, PTGS2, FGF7, FGF10, IL10, SPP1, MPO, THBS1, CYBB, and PF4. PTGS2 was the most common hub gene found under both conditions (PE and PEIUGR). Moreover, the greater (-9.1 kcal/mol) molecular binding of flavoxate to PTGS2 was found to have satisfactory pharmacokinetic properties compared with those of other compounds. The flavoxate-bound PTGS2 protein complex remained stable throughout the simulation; with a ligand fit to protein, Conclusion: The findings of this study may be useful for treating PE and IUGR in the management of PTB.
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