Exploring Biomarkers and Mechanisms of Action of Adaptive Immune Response in Age-Related Macular Degeneration Based on Transcriptomics.

Background: Age-related macular degeneration (AMD) is a common retinal degenerative disease linked to adaptive immune response dysregulation. This study aimed to identify shared immune-related biomarkers and explore their underlying mechanisms. Methods: GSE29801 and GSE135092 served as training and validation sets. Adaptive immune response-related genes (AIR-RGs) from MSigDB were intersected with AMD-related differentially expressed genes (DEGs) to identify candidate genes. Machine learning algorithms were applied to screen biomarkers, validated in datasets and a mouse model of choroidal neovascularization by qPCR. A nomogram was constructed and assessed. GSEA and immune infiltration analyses explored mechanisms and immune microenvironment associations. Results: A total of 148 DEGs were identified, yielding 15 candidate genes after intersection with AIR-RGs. Machine learning identified C3 and HLA-DOA as potential biomarkers, with their differential expression validated across datasets. A nomogram based on these biomarkers demonstrated good predictive performance for AMD pathology (AUC = 0.795). Biomarkers were associated with some immune-inflammatory pathways. Significant differences in immune cell infiltration were observed between AMD and control groups, with biomarkers positively correlated with differentially infiltrated immune cells, such as natural killer cells. Conclusions: The identification of the established biomarker C3 serves as a proof-of-principle for the analytical approach, rather than a novel discovery, thereby validating the model's capacity to uncover other critical immune targets. Consequently, C3 and HLA-DOA serve as potential biomarkers for AMD, significantly correlated with disease progression via immune pathways and offering insights for immune-based therapeutic strategies.