Department of Obstetrics, Jinan, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Endocr Metab Immune Disord Drug Targets. 2024;24(14):1611-1621. doi: 10.2174/0118715303275367240103102801.
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.
This study aims to identify ferroptosis-related genes in GDM by bioinformatics methods and to explore their clinical diagnostic value.
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.
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.
The genes associated with ferroptosis in GDM were CCNB2 and CDK1, which can be used as valid indicators for the diagnosis of GDM.
妊娠糖尿病（GDM）被认为是未来母亲和后代心脏代谢紊乱的一个危险因素。铁死亡是一种新型的程序性细胞死亡，可能参与 GDM 的发生和发展。
本研究旨在通过生物信息学方法鉴定 GDM 中的铁死亡相关基因，并探讨其临床诊断价值。
利用基因表达综合数据库（GEO）分析数据集 GSE103552，筛选 GDM 中的差异表达基因（DEGs）。进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路富集分析以及蛋白质-蛋白质相互作用（PPI）网络分析。从 MSigDB 中检索铁死亡基因集，并对数据库进行 GSVA 基因集分析。最后，通过逻辑回归对 GDM 患者和对照组进行区分，以筛选诊断标志物。
在 GDM 的表达谱中鉴定出 179 个 DEGs。GO 和 KEGG 富集分析显示，TGF-β、p53 信号通路、血小板激活、谷胱甘肽代谢、味觉感觉感知、白细胞和血管内皮细胞迁移调节等通路显著富集。通过 GSVA 分析筛选出与铁死亡基因集相关的 DEGs（n=107）。对疾病的筛选 DEGs 和铁死亡评分的 DEGs 进行交集，共鉴定出 35 个交集基因。PPI 鉴定出与 GDM 相关的两个关键基因 CCNB2 和 CDK1。Wilcox 检验显示，GDM 中 CCNB2 和 CDK1 的表达水平较低。CCNB2 和 CDK1 预后模型的 ROC 曲线下面积（AUC）为 0.822。
GDM 中与铁死亡相关的基因是 CCNB2 和 CDK1，可作为 GDM 诊断的有效指标。
