ArticleBiomedicines2023
Single-Cell RNA Sequencing and Microarray Analysis Reveal the Role of Lipid-Metabolism-Related Genes and Cellular Immune Infiltration in Pre-Eclampsia and Identify Novel Biomarkers for Pre-Eclampsia.
Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 9 citations in OpenAlex.
- Single-cell mapping of maternal-fetal cross-talk in preeclampsia.Research square · 2025Article
- A signature serum biomarker for non-invasive diagnosis of preeclampsia patients.Journal of assisted reproduction and genetics · 2025Article
- Integration of genetic, proteomic, and transcriptomic data identifies therapeutic targets and prognostic biomarkers in bladder cancer.Translational andrology and urology · 2025Article
- Analysis of Transcriptome Differences Between Subcutaneous and Intramuscular Adipose Tissue of Tibetan Pigs.Genes · 2025Article
- Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms.Scientific reports · 2025Article
- Identification and Immunological Characterization of Cuproptosis Related Genes in Preeclampsia Using Bioinformatics Analysis and Machine Learning.Journal of clinical hypertension (Greenwich, Conn.) · 2025Article
- Identification of Coagulation and Fibrinolysis-Associated Biomarkers With Implications for Preeclampsia.Genetics research · 2025Article
- Identification of hub glutamine metabolism-associated genes and immune characteristics in pre-eclampsia.PloS one · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pre-eclampsia (PE) is a gestational hypertensive disorder that is characterized by hypertension and proteinuria, typically occurring after 20 weeks of gestation. Despite its global impact on pregnant women, the precise pathogenic mechanisms of PE remain unclear. Dysregulated lipid metabolism and immune cell infiltration contribute to PE development. Our study aimed to identify lipid-metabolism-related genes (LMRG-PEs) and investigate their association with immune infiltration. We utilized the "Seurat" R package for data quality control, cell clustering, and marker gene identification. The "SingleR" package enabled the matching of marker genes to specific cell types. Pseudotemporal ordering analysis was conducted using the "Monocle" package. Weighted correlation network analysis (WGCNA), gene set variation analysis (GSVA), and gene set enrichment analysis (GSEA) approaches were employed to explore lipid-metabolism-related genes, while potential targeted drugs were predicted using the drug-gene interaction database (DGIdb). Hub gene expression was validated through RT-qPCR. By analyzing single-cell RNA sequencing data, we identified and classified 20 cell clusters into 5 distinct types. Differential gene expression analysis revealed 186 DEGs. WGCNA identified 9 critical modules and 265 genes significantly associated with PE diagnosis, emphasizing the importance of the core genes
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Registered trials
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