Identification of key ubiquitination‐related genes in gestational diabetes mellitus: A bioinformatics‐driven study

Abstract Background and Aims Gestational diabetes mellitus (GDM) is characterized by glucose intolerance that occurs during pregnancy. This study aimed to identify key ubiquitination‐related genes associated with GDM pathogenesis. Methods Microarray data from GSE154377 was analyzed to identify diffe...

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Bibliographic Details
Published in:Health Science Reports
Main Authors: Yuheng Dai, Sha Lu, Wensheng Hu
Format: Article
Language:English
Published: Wiley 2024-10-01
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Online Access:https://doi.org/10.1002/hsr2.70115
Description
Summary:Abstract Background and Aims Gestational diabetes mellitus (GDM) is characterized by glucose intolerance that occurs during pregnancy. This study aimed to identify key ubiquitination‐related genes associated with GDM pathogenesis. Methods Microarray data from GSE154377 was analyzed to identify differentially expressed genes (DEGs) in GDM vs normal pregnancy samples. Weighted gene co‐expression network analysis was performed on ubiquitination‐related genes. Functional enrichment, protein‐protein interaction network, and TF‐mRNA‐miRNA interaction network analyses were conducted on differentially expressed ubiquitination‐related genes (DE‐URGs). Results We identified 2337 DEGs and 65 DE‐URGs in GDM. Functional enrichment analysis of the 65 DE‐URGs revealed involvement in protein ubiquitination and ubiquitin‐dependent catabolic processes. Protein‐protein interaction network analysis identified 8 hub genes, including MAP1LC3C, USP26, USP6, UBE2U, USP2, USP43, UCHL1, and USP44. ROC curve analysis showed these hub genes have high diagnostic accuracy for GDM (AUC > 0.6). The TF‐mRNA‐miRNA interaction network suggested USP2 and UCHL1 may be key ubiquitination genes in GDM. Conclusion In conclusion, this study contributes to our understanding of the molecular landscape of GDM by uncovering key ubiquitination‐related genes. These findings may serve as a foundation for further investigations, offering potential biomarkers and therapeutic targets for clinical applications in GDM management.
ISSN:2398-8835