ArticleBMC medical genomics2022
Gene model-related m6A expression levels predict the risk of preeclampsia.
Article in BMC medical genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- METTL14 promotes trophoblast dysfunction by elevating SZRD1 expression in an m6A-dependent manner.Human cell · 2026Article
- Epigenetic mechanisms in preeclampsia: translational therapeutic strategies and precision-medicine perspectives.Journal of medicine and life · 2026Review
- Hippo pathway in endometriosis pathogenesis: from cellular dysregulation to therapeutic opportunities.Frontiers in pharmacology · 2026Review
- Worldwide research trends of pregnancy hypertension in epigenetics field.Frontiers in public health · 2025Article
- N6-methyladenosine modifications in maternal-fetal crosstalk and gestational diseases.Frontiers in cell and developmental biology · 2023Review
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis is the first study to explore the potential functions and expression patterns of RNA N6-methyladenosine (m6A) and potential related genes in preeclampsia.
methodsWe identified two m6A modification patterns through unsupervised cluster analysis and validated them by principal component analysis. We quantified the relative abundance of specific infiltrating immunocytes using single-sample gene set enrichment analysis (ssGSEA) and the Wilcoxon test. To screen hub genes related to m6A regulators, we performed weighted gene coexpression network analysis. Functional enrichment analysis was conducted for differential signalling pathways and cellular processes. Preeclampsia patients were grouped by consensus clustering based on differentially expressed hub genes and the relationship between different gene-mediated classifications and clinical features.
resultsTwo m6A clusters in preeclampsia, cluster A and cluster B, were determined based on the expression of 17 m6A modification regulators; ssGSEA revealed seven significantly different immune cell subtypes between the two clusters. A total of 1393 DEGs and nine potential m6A-modified hub genes were screened. We divided the patients into two groups based on the expression of these nine genes. We found that almost all the patients in m6A cluster A were classified into hub gene cluster 1 and that a lower gestational age may be associated with more m6A-associated events.
conclusionsThis study revealed that hub gene-mediated classification is consistent with m6A modification clusters for predicting the clinical characteristics of patients with preeclampsia. Our results provide new insights into the molecular mechanisms of preeclampsia.
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