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image of Bioinformatics Analysis of Lactylation-Related Biomarkers and Potential Pathogenesis Mechanisms in Age-Related Macular Degeneration

Abstract

Background

Lactylation is increasingly recognized to play a crucial role in human health and diseases. However, its involvement in age-related macular degeneration (AMD) remains largely unclear.

Objective

The aim of this study was to identify and characterize the pivotal lactylation-related genes and explore their underlying mechanism in AMD.

Methods

Gene expression profiles of AMD patients and control individuals were obtained and integrated from the GSE29801 and GSE50195 datasets. Differentially expressed genes (DEGs) were screened and intersected with lactylation-related genes for lactylation-related DEGs. Machine learning algorithms were used to identify hub genes associated with AMD. Subsequently, the selected hub genes were subject to correlation analysis, and reverse transcription quantitative real-time PCR (RT-qPCR) was used to detect the expression of hub genes in AMD patients and healthy control individuals.

Results

A total of 68 lactylation-related DEGs in AMD were identified, and seven genes, including ,, , , , , and were selected as key genes. RT-qPCR analysis validated that all 7 key genes were down-regulated in AMD patients.

Conclusion

We identified seven lactylation-related key genes potentially associated with the progression of AMD, which might deepen our understanding of the underlying mechanisms involved in AMD and provide clues for the targeted therapy.

© 2025 Bentham Science Publishers
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2025-01-02
2025-04-04

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Bioinformatics Analysis of Lactylation-Related Biomarkers and Potential Pathogenesis Mechanisms in Age-Related Macular Degeneration
Bentham Science Publishers ; https://doi.org/10.2174/0113892029291661241114055924
/content/journals/cg/10.2174/0113892029291661241114055924
/content/journals/cg/10.2174/0113892029291661241114055924
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  • Article Type:     Research Article
Keywords: Age-related macular degeneration ; lactylation ; hub genes ; immune infiltration ; bioinformatics ; machine learning

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