Hyperuricemia (HUA) and obesity (OB) are complex metabolic disorders with significant health consequences. This study investigated the role of mitochondrial autophagy-related differentially expressed genes (MRDEGs) in HUA and OB through gene expression and immune cell infiltration analyses.

Datasets GSE142421 and GSE69306 were retrieved from the Gene Expression Omnibus (GEO) database, preprocessed, and annotated. Differential gene expression was assessed using the limma package, identifying differentially expressed genes (DEGs) with |logFC| > 0 and p-value < 0.05. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted using clusterProfiler. MRDEGs were identified by intersecting DEGs with mitochondrial autophagy-related genes. Least absolute shrinkage and selection operator (LASSO) regression models identified key genes, followed by immune cell infiltration analysis using CIBERSORT.

In GSE142421, 3600 DEGs were identified, with 1651 upregulated and 1949 downregulated in HUA. In GSE69306, 1752 DEGs were identified, with 775 upregulated and 977 downregulated genes in OB. Venn diagram analysis revealed 1096 MRDEGs in HUA and 412 in OB. LASSO models identified four key genes (EMD, EEFSEC, ZFP36L2, MTFR1L) in HUA and seven (CTCF, LUC7L, POLA1, SUGP2, RB1CC1, UXS1 and METTL17) in OB. Immune infiltration analysis demonstrated that 15 types of immune cells were enriched in HUA samples, with the gene ZFP36L2 and monocytes showing the strongest significant negative correlation. In OB samples, 20 types of immune cells were enriched, with the gene METTL17 and naive B cells exhibiting the strongest negative correlation. 

We successfully identified MRDEGs in HUA and OB by analyzing gene expression and immune cell infiltration. The development of a LASSO regression model and risk scoring system highlighted the potential of these genes for diagnostic purposes. In this study, we also explored the involvement of immune cells in the disease process and their relationship with the identified genes. These findings enhance our understanding of the molecular mechanisms behind HUA and OB, providing a foundation for future research. With further validation, these results may lead to new diagnostic and treatment approaches.