Uncovering Placental Endocrine Dysregulation in Gestational Diabetes Mellitus Using Bioinformatics and Machine Learning

BACKGROUND: Gestational diabetes mellitus (GDM) is a common metabolic disorder, posing serious health risks to both mother and fetus. This study aims to explore the placental endocrine mechanisms involved in GDM. METHODS: The training dataset GSE203346 and endocrine-related gene sets from the Molecular Signatures Database were used to identify endocrine-related differentially expressed genes (ER-DEGs). Support vector machine recursive feature elimination (SVM-RFE) and Boruta algorithms were employed to identify hub genes. Diagnostic performance was analyzed using the receiver operating characteristic (ROC) curves. Immune cell infiltration was evaluated using the CIBERSORT algorithm, followed by single-gene gene set enrichment analysis (GSEA). RESULTS: A total of 57 ER-DEGs were identified in the GDM placental samples. Machine learning identified nine important genes, of which CACNA1C, CCDC102B, and KIF21A exhibited high diagnostic performance (AUC > 0.80) in two external validation datasets (GSE154414 and GSE255075). Immune infiltration analysis revealed potential differences in several immune cell proportions between high- and low-expression groups of the hub genes. High expression of CACNA1C, CCDC102B, and KIF21A was associated with the Notch pathway, mTOR pathway, and autophagy. CONCLUSIONS: CACNA1C, CCDC102B, and KIF21A may be the potential biomarkers and therapeutic targets for GDM.