Background: Thyroid eye disease (TED) is a common autoimmune inflammatory disease that significantly impairs quality of life. Mitochondrial metabolism is a key driver for the pathogenesis of inflammatory diseases, yet its role in TED remains poorly understood. Our study aimed to investigate the mechanisms by which mitochondrial metabolism contributes to TED and identify potential treatment targets using a multi-methodological approach.

Methods: We performed a series of bioinformatics analyses, including the identification of differentially expressed genes (DEGs), Kyoto Encyclopaedia of Genes and Genomes (KEGG) enrichment analysis, immune infiltration assessments, genome-wide association studies (GWAS), and machine learning.

Results: By integrating the datasets GSE58331, GSE105149, and GSE185952, we ultimately screened 14 mitochondria-related (DEGs) from TED samples and applied machine learning to select diagnostic markers. 3 disease-associated diagnostic biomarkers (Ifi27, Cyba, and Cmpk2) were identified. Immune cell infiltration analysis revealed 4 immune cell types significantly associated with TED: follicular helper T (Tfh) cells, monocytes, M1 macrophages, and neutrophils. Multiple specific correlations were observed between these immune cells and the diagnostic biomarkers.

Conclusion: We identified a unique mitochondrial-related genetic signature essential for TED progression, which provides valuable insights into immune infiltration. These findings may advance molecular research on TED and offer novel strategies and therapeutic approaches for this condition.