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Iron metabolism and preeclampsia: new insights from bioinformatics analysis
Xijiao Guo1, Sha Li1, Guoping Xiong1
1Department of Obstetrics, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Insights
Iron metabolism and ferroptosis are key in preeclampsia (PE). Five hub genes (LTF, PLOD2, CP, NR1D2, P3H2) show diagnostic potential for PE, linking iron regulation to disease occurrence.
Area of Science:
- Genomics and Bioinformatics
- Reproductive Medicine
- Molecular Biology
Background:
- Preeclampsia (PE) is a complex pregnancy disorder with significant implications for maternal and fetal health.
- Iron metabolism and ferroptosis are increasingly recognized as critical factors in PE pathogenesis.
- Accurate diagnosis and effective prevention strategies for PE remain significant clinical challenges.
Purpose of the Study:
- To identify novel molecular diagnostic targets for preeclampsia using bioinformatics and machine learning.
- To investigate the role of iron metabolism and ferroptosis in the development of PE.
- To establish potential biomarkers for early diagnosis and improved management of PE.
Main Methods:
- Differential gene expression analysis and Weighted Gene Correlation Network Analysis (WGCNA) were performed on PE datasets.
- Machine learning algorithms (Lasso-Cox, SVM, XGboost) were employed to identify key hub genes.
- Gene Set Enrichment Analysis (GSEA), ROC curve analysis, and assessment of immune cell infiltration were conducted.
Main Results:
- 355 differentially expressed genes were identified, associated with extracellular matrix, inflammation, and iron homeostasis.
- Five hub genes (LTF, PLOD2, CP, NR1D2, P3H2) were identified as potential PE biomarkers.
- These hub genes demonstrated significant diagnostic value and correlation with ferroptosis and immune cell infiltration (T cells).
Conclusions:
- The identified hub genes (LTF, PLOD2, CP, NR1D2, P3H2) show promise as diagnostic biomarkers for preeclampsia.
- The study highlights a strong link between iron metabolism dysregulation and the occurrence of PE.
- Further research into these biomarkers could lead to improved diagnostic tools and therapeutic strategies for PE.
Objective:
Preeclampsia (PE) is a multifactorial systemic pregnancy disease, in which iron metabolism and ferroptosis play significant roles during its pathogenesis. The diagnosis and prevention of PE remain urgent clinical issues that need to be addressed. Therefore, finding molecular diagnostic targets for PE through bioinformatics and machine learning methods is crucial for the diagnosis and prevention of patients with PE.
Methods:
Data sets for PE were obtained from the GEO database, and gene differential expression analysis was conducted along with enrichment analysis annotations. Subsequently, WGCNA was used to screen for genes associated with PE. Functional annotations were performed for the intersection of differentially expressed genes (DEGs), two key modules, and iron metabolism-related genes. Lasso-Cox, SVM, and XGboost machine learning methods were utilized to identify hub genes related to iron metabolism in PE, followed by GSEA analysis. The diagnostic value of the hub genes was assessed using ROC curves, and the correlations of hub genes with ferroptosis were evaluated based on ssgsea scores. Finally, the immune cell infiltration in PE was assessed, along with the relationship between hub genes and infiltrating immune cells.
Results:
A total of 355 differentially expressed genes in PE were identified. The functional enrichment analysis indicated that the genes were primarily associated with extracellular matrix, inflammatory response, immune response, iron ion binding, transport, and homeostasis, endoplasmic reticulum lumen, and hypoxic response. Pathway enrichment analysis revealed associations primarily with metabolic pathways, PI3K-Akt signaling pathway, cAMP signaling pathway, JAK-STAT signaling pathway, oxidative phosphorylation, HIF-1 signaling pathway, and pathways related to iron absorption and transport. Through WGCNA analysis and machine learning, five hub genes associated with PE were finally identified: LTF, PLOD2, CP, NR1D2, and P3H2. LTF, PLOD2, and CP were highly expressed in the PE group, while NR1D2 and P3H2 were lowly expressed. ROC curve analysis demonstrated that all hub genes had good diagnostic value. The ssgsea scores indicated that hub genes were significantly associated with ferroptosis. The immune infiltration results revealed that resting CD4+ memory T cells and regulatory T cells participated in the pathogenesis of PE.
Conclusion:
LTF, PLOD2, CP, NR1D2, and P3H2 may serve as diagnostic biomarkers for PE, and the occurrence of PE is related to iron metabolism responses.
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