Abstract
BACKGROUND: A potential link exists between gestational diabetes mellitus (GDM) and polyamines, yet the mechanism of polyamine metabolism-related genes (PMRGs) in GDM remains elusive. This study aims to clarify the relationship between PMRGs and GDM. METHODS: The study utilized GDM-related datasets (GSE203346, GSE154414) and PMRGs scores. Differential expression analysis on GSE203346 identified differentially expressed genes (DEGs), which were then stratified based on PMRGs scores. Then, differentially expressed PMRGs (DE-PMRGs) were screened between high and low scoring groups, intersecting with DEGs to select candidate genes. Hub genes were further identified using ML and validated. Gene set enrichment analysis (GSEA), regulatory network analysis, and drug prediction explored mechanisms and potential treatments. Finally, hub gene expression was verified by real-time reverse transcriptase-polymerase chain reaction (RT-qPCR). RESULTS: Overall 22 candidate genes were obtained through the intersection of 465 DEGs and 71 DE-PMRGs. Afterwards, 2 hub genes (NR1D1 and SLC7A5) were identified by machine learning, which had significant difference and consistent trends between GDM and control groups in GSE203346 and GSE154414 datasets. GSEA results revealed that olfactory transduction, Parkinson's disease, and oxidative phosphorylation were enriched by hub genes. Furthermore, we found MALAT1 and NEAT1 regulated NR1D1 and SLC7A5 through hsa-miR-185-5p, hsa-miR-4644 and hsa-miR-503-5p, STAT3, MITF, HNF4A and etc. regulated NR1D1 and SLC7A5 in regulatory network. RT-qRCR showed that SLC7A5 is obviously higher expressed in GDM group. CONCLUSIONS: NR1D1 and SLC7A5, hub genes linked to polyamine metabolism in GDM, offer a fresh angle to explore GDM's molecular mechanisms.