Abstract
PURPOSE: To reveal the significance of oxidative stress-related genes in the pathogenesis of thyroid eye disease (TED) and Graves' disease (GD) using a bioinformatics approach. METHODS: Utilizing datasets from the GEO database, we used the "limma" package to detect differentially expressed genes (DEGs). Oxidative stress-related DEGs related to TED and GD were identified through cross-referencing with the GeneCards database. We used a variety of methods, such as enrichment analyses, LASSO, RF techniques, PPI network analysis, and the CIBERSORT algorithm. RESULTS: We identified 22 oxidative stress-related DEGs related to TED and GD, primarily involved in miRNA transcription and regulation. Hub genes (DUSP1, EGR1, FOS, and JUNB) were linked to immune cells and were identified as potential diagnostic biomarkers. The developed nomogram model exhibited satisfactory calibration. CONCLUSION: This computational study sheds light on the molecular pathways underlying TED and GD, proposing candidate biomarkers and therapeutic targets. However, these findings are preliminary and require further experimental validation (e.g., qPCR, western blot, and IHC) in patient tissues before their clinical utility can be assessed.