Study on key pathogenic mechanisms and potential intervention targets of age-related macular degeneration based on bioinformatics methods

To identify biomarkers associated with age-related macular degeneration (AMD) using bioinformatics methods, and to explore the potential functions and mechanistic pathways of the associated hub genes.

Gene expression profile data from GSE1719 and GSE50195 were downloaded from the GEO database. Differentially expressed genes (DEGs) associated with AMD were identified using the GEO2R tool, with |logFC| > 0.5 and p < 0.05 as the threshold. The common DEGs between the two datasets were identified through Venn diagram analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to investigate the functions and pathways of the DEGs. A protein–protein interaction (PPI) network and a TF-DEGs-miRNA network were constructed using the STRING tool, and key genes were identified using the maximal clique centrality (MCC) algorithm.

Venn diagram analysis revealed that 36 common DEGs were shared between AMD patients and normal retinal pigment epithelium (RPE)/choroid samples in both GSE1719 and GSE50195 datasets. KEGG pathway enrichment analysis showed that these DEGs were mainly enriched in synaptic vesicle cycling, alpha-linolenic acid metabolism, and lipid digestion and absorption pathways. PPI network construction and TF-DEGs-miRNA network analysis identified PXDN as a key gene.

The key gene PXDN and related signaling pathways identified through bioinformatics analysis provide theoretical support for further understanding the potential mechanisms underlying AMD pathogenesis and offer a reference for clinical diagnosis and treatment research.