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

ABSTRACT: Glaucoma poses a significant global health challenge, yet reliable biomarkers for its diagnosis and treatment remain scarce. This study employed Mendelian randomization (MR) and bioinformatics approaches to identify potential biomarkers for glaucoma. Using the GSE9944 dataset, differentially expressed genes (DEGs) were identified and analyzed through protein-protein interaction (PPI) networks and functional enrichment. MR analysis selected DEGs for further evaluation using support vector machine-recursive feature elimination (SVM-RFE), with genes exhibiting high differential expression and an area under the curve (AUC) > 0.7 considered as candidate biomarkers. Among 836 DEGs, the PPI network revealed complex interactions, and functional enrichment highlighted significant involvement of the

SUBMITTER: Lin X 

PROVIDER: S-EPMC12219105 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature