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Cuproptosis-related signature and immune infiltration in age-related macular degeneration

Chen Li1, Yi-Cheng Lu1, Ming-Xuan Chen2

  • 1Department of Ophthalmology, the First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu Province, China.

International Journal of Ophthalmology
|August 29, 2025
PubMed
Abstract

Insights

This study explores cuproptosis-related genes and immune infiltration in age-related macular degeneration (AMD). A predictive model using hub genes SLC31A1 and VEGFA was developed for AMD risk assessment.

Area of Science:

  • Ophthalmology
  • Molecular Biology
  • Immunology

Background:

  • Age-related macular degeneration (AMD) is a leading cause of vision loss.
  • Cuproptosis, a newly identified cell death pathway, is implicated in various diseases.
  • The role of cuproptosis in AMD pathogenesis remains unclear.

Purpose of the Study:

  • To investigate cuproptosis-related molecular mechanisms and immune infiltration in AMD.
  • To establish a predictive model for AMD risk based on cuproptosis-related genes.
  • To explore the clinical prognostic value of these genes in AMD.

Main Methods:

  • Analysis of microarray datasets (GSE29801, GSE160011) for cuproptosis-related differentially expressed genes (Cu-DEGs) and immune cell infiltration in AMD.
  • Application of three machine learning techniques to identify diagnostic genes.
  • Validation using an external dataset and reverse transcription polymerase chain reaction (RT-PCR) for hub gene expression.

Main Results:

  • Six cuproptosis-signature genes were identified in AMD.
  • SLC31A1 and VEGFA were selected as hub genes, associated with immune signaling pathways.
  • Higher mRNA expression of SLC31A1 and VEGFA was observed in laser-induced choroidal neovascularization (CNV) models.

Conclusions:

  • Cuproptosis is systematically linked to AMD development.
  • A predictive model incorporating cuproptosis-related genes (SLC31A1, VEGFA) offers prognostic value for AMD patients.
  • These findings highlight potential therapeutic targets for AMD.
Keywords:
age-related macular degenerationcuproptosisimmune infiltrationmachine learning

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