Identification and validation of oxidative stress signature genes in the trabecular meshwork of glaucoma

Abstract

Oxidative stress (OS) plays an important role in trabecular meshwork (TM) dysfunction in glaucoma, but its molecular mechanism remains unclear. We integrated the GSE27276 dataset and OS-related gene sets from GeneCards to screen 61 differentially expressed OS-related genes (DEOSGs). Functional enrichment analysis revealed that these genes are primarily involved in inflammatory and OS-related signaling pathways, including IL-17, TNF, and NF-κB. A protein-protein interaction (PPI) network constructed via the STRING database identified seven hub genes (CCL3, CXCL1, NFKBIA, VCAM1, LCN2, TNFRSF1A, and HP). Validation using the GSE124114, GSE37474, and GSE65240 datasets showed that the expression of CXCL1 and VCAM1 was downregulated, while NFKBIA was upregulated. Additionally, all three genes exhibited an area under the curve (AUC) greater than 0.7. Immune infiltration analysis demonstrated significant associations between these genes and immune cells, particularly regulatory T cells and neutrophils. Regulatory network analysis suggested that transcription factors (RELA, NFKB1) and microRNAs (hsa-miR-34a-5p) may modulate these core genes. Drug-gene interaction studies identified 35 potential therapeutic agents, including Infliximab and Vitamin B6. This study systematically elucidates the molecular mechanisms of OS in glaucoma, proposing that the identified core genes and their regulatory networks not only serve as novel biomarkers for diagnosis but also provide a theoretical foundation for developing targeted therapeutic strategies.

Keywords: Bioinformatics analysis; GEO database; Glaucoma; Hub genes; Oxidative stress; Trabecular meshwork.

Fig. 1

The flow chat of this study.

Fig. 2

(A) Volcano plot of DEGs in GSE27276, with green indicating downregulated genes and red indicating upregulated genes. (B) Heatmap of the top 50 differentially expressed genes in the GSE27276 dataset, with red indicating upregulated genes and blue indicating downregulated genes. (C) Venn diagram. The intersecting genes between GSE27276-DEGs and the oxidative stress gene set, referred to as DEOSGs.

Fig. 3

Enrichment analysis. (A) Gene Ontology biological processes of the DEOSGs. (B) Gene Ontology Molecular functions of the DEOSGs. (C) Gene Ontology Cellular components of the DEOSGs. (D) KEGG enrichment analysis of the DEOSGs. The node size represents the term enrichment significance.

Fig. 4

(A) PPI network analysis of DEOSGs. The color intensity and size of the nodes are scaled according to their degree values. (B) The top 10 hub genes identified by the degree and MCC algorithms via the CytoHubba plugin in Cytoscape. (C) Expression of 7 hub genes in GSE27276, *p < 0.05, **p < 0.01, ***p < 0.001. (D) The ROC curve of hub genes in GSE27276. (E) GeneMANIA diagram showing the coexpression interactions between the 7 hub genes and their neighbouring genes.

Fig. 5

Enrichment analysis. (A) Gene Ontology biological processes of the DEOSGs. (B) KEGG enrichment analysis of the DEOSGs. The node size represents the term enrichment significance.

Fig. 6

(A) and (B) represent the correlation analysis of TNFRSF1A and NFKBIA with 28 immune cell types, respectively, with p < 0.05 considered statistically significant.

Fig. 7

(A) Expression of 7 hub genes in GSE124114, GSE37374 and GSE65240. ns, no statistical significance. (B) The ROC curve of validation dataset GSE124114, GSE37374 and GSE65240 confirmed the diagnostic importance of key genes.

Fig. 8

(A) The transcription factor regulatory network of hub genes. The pink nodes represent hub genes, the green squares represent TFs, and the connections indicate TFs regulating the hub genes. (B) The microRNAs that regulate at least 5 hub genes. The pink nodes represent hub genes, the blue squares represent microRNA, and the connections indicate microRNA regulating the hub genes.

Fig. 9

The drug prediction network of hub genes. The pink nodes represent hub genes, the yellow squares represent drug, and the connections between them indicate that the drug may act on the corresponding gene.