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Author Spotlight: Modeling an Aspect of Preeclampsia in Female Mice Using Hypoxic Human Placenta-Derived Small Extracellular Vesicles
Published on: January 26, 2024
Bioinformatics methods in biomarkers of preeclampsia and associated potential drug applications
Ying Peng1,2, Hui Hong2, Na Gao3
1Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Insights
This study identifies key genes and potential drug treatments for preeclampsia, a condition causing high blood pressure during pregnancy. Findings may aid early detection and personalized therapies for preeclampsia.
Area of Science:
- Genomics and bioinformatics
- Molecular biology
- Obstetrics and Gynecology
Background:
- Preeclampsia is a serious pregnancy complication characterized by high blood pressure and proteinuria after 20 weeks.
- It significantly increases risks for maternal mortality, organ damage, and restricted fetal development.
- Early detection biomarkers and effective treatments for preeclampsia are critically needed.
Purpose of the Study:
- To identify critical genes involved in preeclampsia pathogenesis.
- To explore potential small-molecule drug agents for preeclampsia treatment targeting identified genes.
- To investigate the relationship between gene expression, immune cell infiltration, and preeclampsia.
Main Methods:
- Utilized four Gene Expression Omnibus datasets (GSE10588, GSE25906, GSE48424, GSE60438) for analysis.
- Applied batch effect removal, differential gene expression analysis (limma), and Weighted Gene Co-expression Network Analysis (WGCNA).
- Screened potential drugs using the Connective Map (CMAP) database and evaluated immune cell infiltration via ssGSEA.
Main Results:
- Identified 681 differentially expressed genes (DEGs) between normal and preeclampsia samples.
- Screened nine potential drugs including Dexamethasone and Irinotecan via CMAP.
- Discovered two key genes, PLEK and LEP, associated with preeclampsia through WGCNA and logistic regression, with validation in an external cohort.
Conclusions:
- Identified key genes (PLEK, LEP) and pathways (chemokine signaling, B cell receptor signaling) implicated in preeclampsia.
- Screened potential therapeutic agents, offering avenues for personalized preeclampsia treatment.
- Highlights the potential of identified genes as biomarkers for early preeclampsia detection.
Background:
Preeclampsia is a pregnancy-related condition that causes high blood pressure and proteinuria after 20 weeks of pregnancy. It is linked to increased maternal mortality, organ malfunction, and foetal development limitation. In this view, there is a need critical to identify biomarkers for the early detection of preeclampsia. The objective of this study is to discover critical genes and explore medications for preeclampsia treatment that may influence these genes.
Methods:
Four datasets, including GSE10588, GSE25906, GSE48424 and GSE60438 were retrieved from the Gene Expression Omnibus database. The GSE10588, GSE25906, and GSE48424 datasets were then removed the batch effect using the "sva" R package and merged into a complete dataset. The differentially expressed genes (DEGs) were identified using the "limma" R package. The potential small-molecule agents for the treatment of PE was further screened using the Connective Map (CMAP) drug database based on the DEGs. Further, Weight gene Co-expression network (WGNCA) analysis was performed to identified gene module associated with preeclampsia, hub genes were then identified using the logistic regression analysis. Finally, the immune cell infiltration level of genes was evaluated through the single sample gene set enrichment analysis (ssGSEA).
Results:
A total of 681 DEGs (376 down-regulated and 305 up-regulated genes) were identified between normal and preeclampsia samples. Then, Dexamethasone, Prednisone, Rimexolone, Piretanide, Trazodone, Buflomedil, Scoulerin, Irinotecan, and Camptothecin drugs were screened based on these DEGs through the CMAP database. Two modules including yellow and brown modules were the most associated with disease through the WGCNA analysis. KEGG analysis revealed that the chemokine signaling pathway, Th1 and Th2 cell differentiation, B cell receptor signalling pathway and oxytocin signalling pathway were significantly enriched in these modules. Moreover, two key genes, PLEK and LEP were evaluated using the univariate and multivariate logistic regression analysis from the hub modules. These two genes were further validated in the external validation cohort GSE60438 and qRT-PCR experiment. Finally, we evaluated the relationship between immune cell and two genes.
Conclusion:
In conclusion, the present study investigated key genes associated with PE pathogenesis that may contribute to identifying potential biomarkers, therapeutic agents and developing personalized treatment for PE.
