National Natural Science Foundation of China(No.81973909)
方法：从GEO数据库中获取小梁网来源的GSE27276数据集，其中包括19个POAG小梁网组织样本和17个正常小梁网组织样本； 下载FerrDb数据库整理的铁死亡相关基因，将GSE27276数据集与铁死亡基因集进行映射，筛选POAG中铁死亡相关的预后差异表达基因(DE-FRGs)并进行相关性分析，进一步了解DE-FRGs的GO和KEGG通路富集。应用LASSO回归模型与SVM-RFE模型两种机器学习的算法筛选铁死亡相关POAG的关键基因，将两种模型的筛选结果取交集，获得最佳特征基因，使用受试者特征曲线(ROC)评估临床诊断能力； 对最佳特征基因进行单基因基因组富集分析(GSEA)和变异分析(GSVA)； 借助视乳头来源的GSE2378与GSE9944数据集验证最佳特征基因的表达水平。
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.