Identification of Key Genes and Metabolic Dysregulation in Thyroid Eye Disease Through Integrated Transcriptomic and Metabolomic Analysis
Thyroid eye disease (TED) markedly compromises ocular function and significantly reduces patients' quality of life, thereby imposing considerable healthcare and economic strains. Present therapeutic strategies, which mainly include glucocorticoids, teprotumumab, immunosuppressive drugs, and surgical treatments, exhibit limited effectiveness and are associated with high rates of relapse. Consequently, unraveling the molecular mechanisms of TED and discovering new diagnostic and therapeutic targets hold immense clinical importance. This study performed an in-depth analysis of the transcriptomic and metabolomic landscapes of orbital adipose tissue in individuals with TED, aiming to fill the gap in comprehensive molecular characterizations and metabolic perturbations in this disease. By adopting a multi-omics integration strategy, we leveraged transcriptome sequencing, broad-spectrum metabolomics, weighted gene co-expression network analysis (WGCNA), machine learning for biomarker identification, immune cell infiltration profiling, molecular docking, QPCR, and immunohistochemistry. These techniques were systematically employed to pinpoint key genes and metabolites associated with TED, leading to the development of an accurate disease prediction model. Remarkably, our analysis of the dataset revealed 4,250 differentially expressed genes (DEGs), with BPIFA1 and SERPINB3 identified as crucial genes through machine learning algorithms. These genes demonstrated outstanding predictive performance in both internal (AUC=0.953) and external dataset validations (AUC=0.717). Furthermore, metabolomic profiling detected 2,159 metabolites, pinpointing three key metabolites: glucuronic acid, phenylglycine, and isoleucine-methionine. The integration of transcriptomic and metabolomic data underscored the significant enrichment of the tyrosine metabolism pathway, establishing functional links between the identified genes and metabolites. These insights offer fresh perspectives on the molecular foundations of TED and propose that BPIFA1 and SERPINB3 could serve as promising biomarkers, while glucuronic acid, phenylglycine, and isoleucine-methionine may hold potential as metabolic indicators. In summary, our research provides a solid framework for comprehending the pathophysiology of TED.