Identifying Diagnostic Biomarkers for Glaucoma Based on Transcriptome Combined with Mendelian Randomization

Glaucoma poses a major health challenge, yet reliable biomarkers for diagnosis and treatment are scarce. This study employed Mendelian randomization and bioinformatics to uncover potential biomarkers. The GSE9944 dataset was used for training and validation in glaucoma research. Differentially expressed genes (DEGs) were identified through differential expression analysis. The protein-protein interaction network (PPI) and functional enrichment were conducted. MR analysis selected DEGs for support vector machine-recursive feature elimination (SVM-RFE), and genes with high differential expression and an area under the curve (AUC) > 0.7 were deemed biomarkers. Biomarker-based analysis, network design, and drug prediction followe. Using 836 DEGs, the PPI network showed diverse interactions, including ATG14-UVRAG. DEGs were enriched in PI3K-Akt and MAPK pathways. MR analysis linked 113 DEGs to glaucoma, with 57 genes matching expression trends. SVM-RFE identified six signature genes, with ATP6V0D1 and FAM89B as biomarkers (AUC > 0.7). Finally, the molecular regulatory networks revealed that biomarkers might involve several regulatory pathways, including ATP6V0D1-hsa-let-7b-5p-HCG18 and ATP6V0D1 or FAM89B-CREB1. The ATP6V0D1 and FAM89B recognized as glaucoma biomarkers, aiding diagnosis, treatment and deepening glaucoma mechanisms understanding