Purine metabolism-related genes and immunization in thyroid eye disease were validated using bioinformatics and machine learning

Abstract

Thyroid eye disease (TED), an autoimmune inflammatory disorder affecting the orbit, exhibits a range of clinical manifestations. While the disease presentation can vary, cases that adhere to a prototypical pattern typically commence with mild symptoms that subsequently escalate in severity before entering a phase of stabilization. Notably, the metabolic activity of cells implicated in the disease substantially deviates from that of healthy cells, with purine metabolism representing a critical facet of cellular material metabolism by supplying components essential for DNA and RNA synthesis. Nevertheless, the precise involvement of Purine Metabolism Genes (PMGs) in the defensive mechanism against TED remains largely unexplored. The present study employed a bioinformatics approach to identify and validate potential PMGs associated with TED. A curated set of 65 candidate PMGs was utilized to uncover novel PMGs through a combination of differential expression analysis and a PMG dataset. Furthermore, GSEA and GSVA were employed to explore the biological functions and pathways associated with the newly identified PMGs. Subsequently, the Lasso regression and SVM-RFE algorithms were applied to identify hub genes and assess the diagnostic efficacy of the top 10 PMGs in distinguishing TED. Additionally, the relationship between hub PMGs and clinical characteristics was investigated. Finally, the expression levels of the identified ten PMGs were validated using the GSE58331 and GSE105149 datasets. This study revealed ten PMGs related with TED. PRPS2, PFAS, ATIC, NT5C1A, POLR2E, POLR2F, POLR3B, PDE3A, ADSS, and NTPCR are among the PMGs. The biological function investigation revealed their participation in processes such as RNA splicing, purine-containing chemical metabolism, and purine nucleotide metabolism. Furthermore, the diagnostic performance of the 10 PMGs in differentiating TED was encouraging. This study was effective in identifying ten PMGs linked to TED. These findings provide light on potential new biomarkers for TED and open up possibilities for tracking disease development.