Title:Bioinformatics-based Identification of Ferroptosis-related Genes and their Diagnostic Value in Gestational Diabetes Mellitus
Volume: 24
Issue: 14
Author(s): Xiaomei Lv and Yujun An*
Affiliation:
- Department of Obstetrics, Jinan, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
Keywords:
Gestational diabetes mellitus, metabolic disorder, ferroptosis, diagnostic, differentially expressed genes, bioinformatics.
Abstract:
Background: Gestational diabetes mellitus (GDM) is considered a risk factor for
heart metabolic disorder in future mothers and offspring. Ferroptosis is a new type of programmed
cell death, which may participate in the occurrence and development of GDM.
Objective: This study aims to identify ferroptosis-related genes in GDM by bioinformatics
methods and to explore their clinical diagnostic value.
Methods: The dataset GSE103552 was analyzed using the Gene Expression Omnibus (GEO)
database to screen for differentially expressed genes (DEGs) in GDM. Gene Ontology (GO) and
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and proteinprotein
interaction (PPI) network were performed. Gene sets for ferroptosis were retrieved in
MSigDB and GSVA gene set analysis was performed on the database. Finally, logistic regression
was performed to differentiate between GDM patients and controls to screen for diagnostic
markers.
Results: A total of 179 DEGs were identified in the expression profile of GDM. GO and KEGG
enrichment analysis revealed significant enrichment in the TGF-β, p53 signaling pathway, platelet
activation, glutathione metabolism, sensory perception of taste, and leukocyte and vascular
endothelial cell migration regulation. DEGs (n = 107) associated with the ferroptosis gene set
were screened by GSVA analysis. The screened DEGs for disease and DEGs for ferroptosis
scores were intersected and 35 intersected genes were identified. PPI identified two key genes
associated with GDM as CCNB2 and CDK1. Wilcox-test showed low expression of CCNB2
and CDK1 in GDM. The area under the ROC curve (AUC) of the CCNB2 and CDK1 prognostic
model was 0.822.
Conclusion: The genes associated with ferroptosis in GDM were CCNB2 and CDK1, which
can be used as valid indicators for the diagnosis of GDM.