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

AIM: To investigate cuproptosis-related molecular and immune infiltration in age-related macular degeneration (AMD) development and establish a predictive model. METHODS: The expression profiles of cuproptosis-related genes and immune signature in AMD based on the microarray dataset GSE29801 were an...

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Main Authors: Chen Li, Yi-Cheng Lu, Ming-Xuan Chen
Format: Article
Language:English
Published: Press of International Journal of Ophthalmology (IJO PRESS) 2025-09-01
Series:International Journal of Ophthalmology
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Online Access:http://ies.ijo.cn/en_publish/2025/9/20250904.pdf
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author Chen Li
Yi-Cheng Lu
Ming-Xuan Chen
author_facet Chen Li
Yi-Cheng Lu
Ming-Xuan Chen
author_sort Chen Li
collection DOAJ
description AIM: To investigate cuproptosis-related molecular and immune infiltration in age-related macular degeneration (AMD) development and establish a predictive model. METHODS: The expression profiles of cuproptosis-related genes and immune signature in AMD based on the microarray dataset GSE29801 were analyzed. A total of 142 AMD samples were used to identify the cuproptosis-related differentially expressed genes (Cu-DEGs), together with the immune cell infiltration. To further refine the list of potential genes for AMD diagnosis, three machine learning techniques were used, and an external dataset were applied for confirming the accuracy of the predictive performance. Reverse transcription polymerase chain reaction (RT-PCR) were also performed to examine the level of mRNA of hub genes. The activated immune responses and Cu-DEGs were assessed between AMD and controls. RESULTS: Six genes, including ATP7A, DBT, VEGFA, UBE2D3, CP, SLC31A1, were screened as cuproptosis-signature in AMD via three machine learning methods. Next, SLC31A1 and VEGFA was selected as hub genes by performance evaluation in an external dataset GSE160011, further analysis showed that SLC31A1 and VEGFA were associated with pathways related to immune signaling and immune function, which were then observed in relation to infiltrating immune cells. Finally, the mRNA expression levels of SLC31A1 and VEGFA were significantly higher in laser induced choroidal neovascularization (CNV) group than in control group detected by RT-PCR. CONCLUSION: In this study, the possible relationship between cuproptosis and AMD is expounded systematically. A predictive model is developed to assess the risk of cuproptosis-related genes and their clinical prognostic value in AMD patients.
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2227-4898
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publishDate 2025-09-01
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spelling doaj-art-71a50f2f5e144f298f13a60b0d18b4cf2025-08-22T06:07:08ZengPress of International Journal of Ophthalmology (IJO PRESS)International Journal of Ophthalmology2222-39592227-48982025-09-011891640164910.18240/ijo.2025.09.0420250904Cuproptosis-related signature and immune infiltration in age-related macular degenerationChen Li0Yi-Cheng Lu1Ming-Xuan Chen2Chen Li. Department of Ophthalmology, the First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu Province, China. lceye0902@126.comDepartment of Ophthalmology, the First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu Province, ChinaSchool of Clinical Medicine, Medical College of Soochow University, Suzhou 215006, Jiangsu Province, ChinaAIM: To investigate cuproptosis-related molecular and immune infiltration in age-related macular degeneration (AMD) development and establish a predictive model. METHODS: The expression profiles of cuproptosis-related genes and immune signature in AMD based on the microarray dataset GSE29801 were analyzed. A total of 142 AMD samples were used to identify the cuproptosis-related differentially expressed genes (Cu-DEGs), together with the immune cell infiltration. To further refine the list of potential genes for AMD diagnosis, three machine learning techniques were used, and an external dataset were applied for confirming the accuracy of the predictive performance. Reverse transcription polymerase chain reaction (RT-PCR) were also performed to examine the level of mRNA of hub genes. The activated immune responses and Cu-DEGs were assessed between AMD and controls. RESULTS: Six genes, including ATP7A, DBT, VEGFA, UBE2D3, CP, SLC31A1, were screened as cuproptosis-signature in AMD via three machine learning methods. Next, SLC31A1 and VEGFA was selected as hub genes by performance evaluation in an external dataset GSE160011, further analysis showed that SLC31A1 and VEGFA were associated with pathways related to immune signaling and immune function, which were then observed in relation to infiltrating immune cells. Finally, the mRNA expression levels of SLC31A1 and VEGFA were significantly higher in laser induced choroidal neovascularization (CNV) group than in control group detected by RT-PCR. CONCLUSION: In this study, the possible relationship between cuproptosis and AMD is expounded systematically. A predictive model is developed to assess the risk of cuproptosis-related genes and their clinical prognostic value in AMD patients.http://ies.ijo.cn/en_publish/2025/9/20250904.pdfage-related macular degenerationcuproptosisimmune infiltrationmachine learning
spellingShingle Chen Li
Yi-Cheng Lu
Ming-Xuan Chen
Cuproptosis-related signature and immune infiltration in age-related macular degeneration
International Journal of Ophthalmology
age-related macular degeneration
cuproptosis
immune infiltration
machine learning
title Cuproptosis-related signature and immune infiltration in age-related macular degeneration
title_full Cuproptosis-related signature and immune infiltration in age-related macular degeneration
title_fullStr Cuproptosis-related signature and immune infiltration in age-related macular degeneration
title_full_unstemmed Cuproptosis-related signature and immune infiltration in age-related macular degeneration
title_short Cuproptosis-related signature and immune infiltration in age-related macular degeneration
title_sort cuproptosis related signature and immune infiltration in age related macular degeneration
topic age-related macular degeneration
cuproptosis
immune infiltration
machine learning
url http://ies.ijo.cn/en_publish/2025/9/20250904.pdf
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AT yichenglu cuproptosisrelatedsignatureandimmuneinfiltrationinagerelatedmaculardegeneration
AT mingxuanchen cuproptosisrelatedsignatureandimmuneinfiltrationinagerelatedmaculardegeneration