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 HMGN2, TOP2B, HNRNPH1, SF3A1, SRRM2, HIST1H1C, and HIST1H2BD 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.

Keywords: Age-related macular degeneration, bioinformatics, machine learning, hub genes, lactylation, immune infiltration.