AIM:To investigate the ferroptosis-related key genes in the progression of primary open angle glaucoma(POAG)through bioinformatics analysis, aiming to gain a deeper understanding of the biological mechanism of ferroptosis in POAG.

METHODS: The GSE27276 dataset, derived from the trabecular meshwork, was obtained from the GEO database. It consisted of 19 trabecular meshwork tissue samples and 17 normal trabecular meshwork tissue samples. The ferroptosis-related genes were obtained from the FerrDb database. Then the GSE27276 dataset with the ferroptosis gene set was mapped, differentially expressed ferroptosis-related genes(DE-FRGs)were identified in POAG, and the correlation analysis was performed. Additionally, the gene ontology(GO)function and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways of DE-FRGs were further analyzed. This study utilized two machine learning algorithms, namely the LASSO regression model and the SVM-RFE model, to identify the ferroptosis-related key genes of POAG. The screening results from both models were intersected to identify the most significant genes. The clinical diagnostic performance of these genes was evaluated using the receiver operating characteristic curve(ROC); the gene set enrichment analysis(GSEA)and gene set variation analysis(GSVA)were conducted on the most significant genes; the expression levels of these genes were validated using the GSE2378 and GSE9944 datasets obtained from the optic nerve head.

RESULTS: In comparison to normal trabecular meshwork tissue, a total of 396 ferroptosis genes exhibited differential expression in POAG trabecular meshwork tissue. Among these, 39 genes were up-regulated while 64 genes were down-regulated. Spearman correlation analysis revealed certain correlation between the up-regulated genes and the down-regulated genes. The GO function and KEGG pathway enrichment analysis revealed that the differential genes were primarily enriched in the oxidative stress response and ferroptosis pathways. A total of 18 DE-FRGs were identified as key genes using LASSO and SVM-RFE algorithms, which demonstrated a higher diagnostic value. GSEA and GSVA revealed a significant association between GDF15, MFN2, and OTUB1 genes with the glutathione metabolic pathway. Moreover, it was observed that MFN2 activated the glutathione metabolic pathway in the high expression group, while OTUB1 activated it in the low expression group. The cross-validation of GSE2378 and GSE9944 datasets revealed a significant increase in the expression level of CREB1 in optic nerve specimens compared to normal optic nerve specimens, and it was consistent with the expression observed in trabecular meshwork samples from the GSE27276 dataset.

CONCLUSION: Based on bioinformatics analysis, a total of 396 DE-FRGs were identified in POAG. By constructing a machine screening model and cross-validation of external datasets, CREB1 is expected to be the best characteristic gene for potential diagnostic biomarker, and provide targets for further elucidating the molecular mechanism and the diagnosis of ferroptosis in POAG. However, further in vivo and in vitro validation is required to elucidate the biological mechanism of ferroptosis in POAG.