Identification of COX7B, GLRX5, and OR52H1 as Vascular Endothelial Injury-associated Biomarkers in Gestational Diabetes Mellitus.

INTRODUCTION This study aimed to screen and validate such biomarkers to clarify the role of vascular endothelial injury (VEI) in the pathogenesis of gestational diabetes mellitus (GDM) and provide clues for future diagnostic and mechanistic studies.

Methods

VEI scores were calculated using a published VEI-related gene set. VEI-correlated gene modules were classified by weighted gene co-expression network analysis (WGCNA), with module genes subjected to GO/KEGG enrichment analysis and intersected with differentially expressed genes (DEGs) from the GSE70493 dataset. Core genes were filtered using three machine learning algorithms, including random forest, LASSO regression, and SVM-RFE, followed by analyses of diagnostic performance, hallmark pathway correlations, and immune infiltration associations.

Results

DEGs were enriched in endoplasmic reticulum protein localization, oxidative stress, and mitochondrial pathways and overlapped with the yellow gene module. Machine learning identified COX7B, GLRX5, and OR52H1 as the candidate biomarkers for GDM. These genes showed favorable diagnostic performance and distinct correlations with hallmark pathways and immune infiltration features, suggesting their potential involvement in VEI-related molecular changes in GDM.

Discussion

This study identified VEI-related hub genes in GDM but is limited by its reliance on a single dataset, a small validation sample, and the lack of experimental validation, warranting further confirmation.

Conclusion

COX7B, GLRX5, and OR52H1 were identified as VEI-associated diagnostic biomarkers in GDM. These findings support the involvement of VEI-related molecular alterations in GDM but should be regarded as hypothesis-generating, requiring further validation in larger clinical cohorts and experimental models.