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. 2025 Aug 20;30(1):777.
doi: 10.1186/s40001-025-03044-x.

Ferroptosis-related hub genes and immune cell dynamics as diagnostic biomarkers in age-related macular degeneration

Affiliations

Ferroptosis-related hub genes and immune cell dynamics as diagnostic biomarkers in age-related macular degeneration

Jinquan Chen et al. Eur J Med Res. .

Abstract

Background: Age-related Macular Degeneration (AMD) is widely acknowledged as a principal cause of vision loss in the elderly. Currently, the therapeutic interventions available in clinical practice fail to achieve satisfactory outcomes. Therefore, it is imperative that we approach the progress of AMD from novel perspectives in order to explore new therapeutic strategies.

Method: We obtained transcriptomic data from the macular and the peripheral retina from patients with AMD and a control group from the Gene Expression Omnibus (GEO) database. Through Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, we identified differentially expressed genes (DEGs) that were significantly enriched in functions associated with ferroptosis. Subsequent application of machine learning techniques enabled the identification of key hub genes, whose diagnostic potential was further validated. Additionally, the expression of these hub genes was corroborated in both animal and cellular models. Finally, we performed a functional enrichment analysis of these hub genes.

Results: In the macula of patients with AMD, 452 DEGs were identified, while in the peripheral retina, 222 DEGs were discovered. Within the macula, 19 genes were associated with ferroptosis, compared to 3 in the peripheral retina. Consequently, the macular was selected as the primary focus of the study. Subsequent screening of these 19 genes using LASSO regression, Support Vector Machine (SVM), and Random Forest algorithms identified four hub genes: FADS1, TFAP2A, AKR1C3, and TTPA. Consequently, we utilized cigarette smoke extract (CSE) to either stimulate retinal pigment epithelial (RPE) cells in vitro or administer it via intravitreal injection, thereby establishing in vitro and in vivo models of AMD. Results from RT-PCR and Western blot analyses revealed an upregulation of FADS1, AKR1C3, and TTPA, while TFAP2A exhibited decreased expression. Finally, we investigated the infiltration of immune cells within the macular and performed a functional enrichment analysis of the hub genes.

Conclusion: We identified four key ferroptosis-related genes (FRGs)-FADS1, AKR1C3, TFAP2A, and TTPA-that possess diagnostic relevance for AMD and correlate with immune cell infiltration. Moreover, significant changes in both mRNA and protein expression levels of these genes have been observed in in vitro experiments and mice models.

Keywords: Age-related macular degeneration; Ferroptosis-related genes; Immune cells; Machine learning.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study received ethical approval from the Ethics Committee of The First Affiliated Hospital of Chongqing Medical University (Chongqing, China), with the approval number IACUC-CQMU-2023-0355. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
DEGs in AMD patients compared to healthy. A Volcano plots illustrating DEGs in the macula of AMD compared to healthy. B Volcano plots illustrating DEGs in the peripheral retina of AMD compared to healthy. C The heatmap showed the DEGs in macula. D The heatmap showed the DEGs in the peripheral retina. E GSEA reveals functional enrichment of DEGs in the macula. F GSEA reveals functional enrichment of DEGs in the peripheral retina
Fig. 2
Fig. 2
Functional enrichment analysis of DEGs. A, B The top 6 functional enrichment in BP, CC, and MF analysis in the macula and peripheral retina. C, D The KEGG analysis of DEGs in the macula and peripheral retina. E, F Integration of KEGG and GO analyses to identify modules associated with DEGs in the macula and peripheral retina
Fig. 3
Fig. 3
Identification of FRGs in the combined microarray set of GSE29801. A The interaction of the DEGs and Ferroptosis-related genes in the macula. B The interaction of the DEGs and Ferroptosis-related genes in the the peripheral retina. C GO analysis of 19 FRGs. D KEGG analysis of 19 FRGs. E Integration of KEGG and GO analyses to identify modules associated with individual FRGs. F Integration of KEGG and GO analyses to identify modules associated with individual different expression gene
Fig. 4
Fig. 4
FRGs in the microarray set of GSE29801. A Volcano plot shows the 19 FRGs. B Heatmap shows the 19 FRGs. C The expression of 19 FRGs between AMD and healthy
Fig. 5
Fig. 5
Machine algorithms for identifying signature genes are illustrated as follows. A The penalty plot of the LASSO model, which includes error bars representing standard errors, and the LASSO plot demonstrating the reduction in coefficient sizes as the penalty parameter k increases. B The top 10 genes ranked by relative importance in the SVM model. C The top 10 genes ranked by relative importance in the Random Forest model. D The interaction among the LASSO, SVM, and Random Forest models
Fig. 6
Fig. 6
The performance of the hub genes in the GSE29801 dataset. A The expression levels of hub genes were compared between patients with AMD and healthy. B ROC analysis demonstrated the diagnostic efficacy of the hub genes. C The Protein–Protein Interaction (PPI) network of four FRGs, constructed using Genemania. D Interactions among four hub genes. E Chromosomal localization of hub genes
Fig. 7
Fig. 7
The performance of the hub genes in GSE49107. A The expression of hub genes between AMD patients and healthy. B ROC showed the diagnostic performance of the hub genes. *P < 0.05
Fig. 8
Fig. 8
The association between immune cell infiltration and FRGs is presented as follows: A Comparative analysis of immune cell infiltration between AMD patients and healthy. B The proportion of immune cell infiltration within the samples. C–F The relationship between hub genes and significantly different levels of immune cell infiltration. Statistical significance is indicated as follows: “ns” means P ≥ 0.05, *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001
Fig. 9
Fig. 9
CSE-induced modulation of hub gene expression in primary RPE cells. A Fluorescence microscopy reveals that primary RPE cells express Bestrophin and ZO-1. B Use electron microscopy to observe ferroptosis in RPE cells induced by CSE. C Western blot analysis Hub genes expression of CSE treated RPE cells and control. D The expression of FADS1, TFAP2A, AKR1C3, and TTPA in CSE treated RPE cells and control were quantified by qRT–PCR. *P < 0.05, **P < 0.01, ***P < 0.001
Fig. 10
Fig. 10
Expression of hub genes in mice model. A Utilize electron microscopy to investigate ferroptosis in the retina of mice induced by CSE. B Western blot analysis Hub genes expression of retina  from AMD mice and control. C The expression of FADS1, TFAP2A, AKR1C3, and TTPA in AMD mice and control were quantified by qRT–PCR. *P < 0.05, **P < 0.01, ***P < 0.001
Fig. 11
Fig. 11
GSEA of hub genes in AMD is presented as follows. A GSEA of FADS1 in AMD utilizing KEGG pathway analysis. B GSEA of TFAP2A in AMD employing KEGG pathway analysis. C GSEA of AKR1C3 in AMD through KEGG pathway analysis. D GSEA of TTPA in AMD using KEGG pathway analysis
Fig. 12
Fig. 12
TF-gene and Gene-miRNA interactions. A Diagram of TF-gene interactions, where rend circles represent genes. B Diagram of Gene-miRNA interactions, where rend circles represent genes

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