Chen JL, Dai HF, Kan XC, Wu J, Chen HW. The integrated bioinformatic analysis identifies immune microenvironment-related potential biomarkers for patients with gestational diabetes mellitus.
Front Immunol 2024;
15:1296855. [PMID:
38449866 PMCID:
PMC10917066 DOI:
10.3389/fimmu.2024.1296855]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 01/22/2024] [Indexed: 03/08/2024] Open
Abstract
Background
Gestational diabetes mellitus (GDM), a transient disease, may lead to short- or long-term adverse influences on maternal and fetal health. Therefore, its potential functions, mechanisms and related molecular biomarkers must be comprehended for the control, diagnosis and treatment of GDM.
Methods
The differentially expressed genes (DEGs) were identified using GSE49524 and GSE87295 associated with GDM from the Gene Expression Omnibus database, followed by function enrichment analysis, protein-protein interactions network construction, hub DEGs mining, diagnostic value evaluation and immune infiltration analysis. Finally, hub DEGs, the strongest related to immune infiltration, were screened as immune-related biomarkers.
Results
A hundred and seven DEGs were identified between patients with GDM and healthy individuals. Six hub genes with high diagnostic values, including ALDH1A1, BMP4, EFNB2, MME, PLAUR and SLIT2, were identified. Among these, two immune-related genes (PLAUR and SLIT2) with the highest absolute correlation coefficient were considered immune-related biomarkers in GDM.
Conclusion
Our study provides a comprehensive analysis of GDM, which would provide a foundation for the development of diagnosis and treatment of GDM.
Collapse