Identification of key genes and metabolites in thyroid eye disease through integrated transcriptomic and metabolomic analysis

Introduction Thyroid eye disease (TED) markedly compromises ocular function and quality of life, imposing significant healthcare and economic burdens. 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. Methods In this study, we 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, immune cell infiltration profiling, molecular docking, QPCR, and immunohistochemistry for biomarker identification. These techniques were systematically employed to pinpoint key genes and metabolites associated with TED, leading to the development of an accurate disease prediction model. Results Remarkably, our analysis of the PRJNA1314138 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 [Area under the curve (AUC)=0.953] and external dataset validations (AUC = 0.717). Furthermore, metabolomic profiling detected 2, 158 metabolites, pinpointing three key metabolites: gluconic acid, colneleic acid, 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. Conclusion These insights offer novel perspectives on the molecular foundations of TED and propose that BPIFA1 and SERPINB3 could serve as promising biomarkers, while gluconic acid, colneleic acid and isoleucine-methionine may hold potential as metabolic indicators. Our research provides a solid framework for comprehending the pathophysiology of TED.