Identifying diagnostic biomarkers for glaucoma based on transcriptome combined with Mendelian randomization.

Area of Science:

• Ophthalmology
• Genetics
• Bioinformatics

Background:

• Glaucoma is a leading cause of irreversible blindness worldwide.
• Current diagnostic and therapeutic biomarkers for glaucoma are limited, necessitating novel discoveries.

Purpose of the Study:

• To identify and validate novel diagnostic and therapeutic biomarkers for glaucoma using integrated bioinformatics and Mendelian randomization (MR) approaches.
• To elucidate the molecular mechanisms underlying glaucoma pathogenesis and explore potential therapeutic targets.

Main Methods:

• Differential gene expression analysis of the GSE9944 dataset to identify DEGs.
• Protein-protein interaction (PPI) network construction and functional enrichment analysis (e.g., PI3K-AKT, MAPK pathways).
• MR analysis combined with support vector machine-recursive feature elimination (SVM-RFE) to select robust candidate biomarkers based on differential expression and predictive accuracy (AUC > 0.7).

Main Results:

• Identified 836 DEGs, with significant enrichment in PI3K-AKT and MAPK signaling pathways.
• MR analysis linked 113 DEGs to glaucoma, and SVM-RFE identified six signature genes.
• ATP6V0D1 and FAM89B were validated as robust biomarkers with AUC > 0.7.
• Experimental validation confirmed downregulation of ATP6V0D1 and FAM89B under stress, associated with PI3K/AKT pathway suppression.

Conclusions:

• ATP6V0D1 and FAM89B are promising novel biomarkers for glaucoma diagnosis and treatment.
• These biomarkers offer potential for developing targeted therapies and advancing the understanding of glaucoma.
• The findings highlight the utility of integrated bioinformatics and MR approaches in identifying disease biomarkers.