Gene model-related m6A expression levels predict the risk of preeclampsia

Abstract

Abstract Background This is the first study to explore the potential functions and expression patterns of RNA N6-methyladenosine (m6A) and potential related genes in preeclampsia. Methods We 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. Results Two 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. Conclusions This 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.

Keywords

• Preeclampsia
• N6-methyladenosine
• Hub gene
• Prognosis
• Bioinformatic gene analysis
• Biomarkers