Abstract
Gestational diabetes mellitus (GDM) is a common pregnancy-related disorder with potential impacts on the fetoplacental unit. To uncover the underlying molecular mechanisms, we conducted a comprehensive bioinformatics analysis using a dataset from Gene Expression Omnibus, which included 37 primary human fetoplacental vascular endothelial cells (FPVEs) from healthy and GDM-complicated pregnancies. We identified 613 differentially expressed genes (DEGs) through the limma package, with 260 up-regulated and 353 down-regulated. Weighted gene co-expression network analysis was then performed, clustering genes into 11 modules. The MEdarkgreen module, containing 1,391 co-expression genes, showed the highest correlation with FPVE programming. After intersecting with DEGs, 192 co-expression hub genes were obtained. Gene Ontology enrichment analysis of these hub genes revealed enrichment in biological processes such as ribonucleoprotein complex biogenesis and ncRNA processing. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed significant enrichment in pathways related to ribosome function, neurodegenerative diseases, and oxidative phosphorylation. Protein-protein interaction network analysis led to the identification of five signature genes (RPS13, MRPS5, MRPL22, MRPL21, and NDUFS3). These genes exhibited significantly lower expression in FPVEs from GDM pregnancies and demonstrated excellent diagnostic performance, with high area under the curve values in receiver operating characteristic analysis. Further KEGG signaling pathway analysis elucidated the multiple signaling pathways in which these signature genes are involved under GDM conditions. We also constructed LncRNA-miRNA-target genes interaction networks for the signature genes. The networks showed that the expression of these genes is regulated by multiple miRNAs and LncRNAs, highlighting the complex post-transcriptional regulatory mechanisms at play. Overall, our study provides novel insights into the molecular basis of FPVE programming in GDM and potential biomarkers for its diagnosis and understanding.