Exploring molecular mechanisms of radioactive iodine therapy in thyroid cancer using single-cell RNA sequencing data

To investigate the role of ubiquitination-related differentially expressed genes (URDEGs) in thyroid carcinoma (THCA) and their implications for predicting responses to radioactive iodine (RAI) therapy. We analyzed data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases using TCGA biolinks and GEO query tools for data acquisition and normalization. Differential expression analysis was performed with DESeq2 to identify URDEGs between RAI-sensitive and RAI- refractory groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using ClusterProfiler. A prognostic risk model was built using Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Single-cell RNA sequencing data were analyzed with Seurat for clustering and annotation. Gene Set Variation Analysis (GSVA) was used to assess pathway enrichment across various cell subpopulations. A total of 25 URDEGs were identified, which were enriched in processes related to cytoskeleton organization and pathways including the peroxisome proliferator-activated receptor (PPAR) signaling pathway. A prognostic model based on four URDEGs—INSM2, TAGLN3, MDGA2, and SIK1—demonstrated strong predictive capability. Immune infiltration analysis revealed significant differences in immune cell composition between high- and low-risk groups. Single-cell analysis identified 74,497 cells across 22 clusters and nine cell types, revealing gene expression patterns that highlight cellular heterogeneity. URDEGs show potential as biomarkers for predicting RAI therapy response and prognosis in THCA. These findings provide insights for personalized therapeutic strategies targeting specific molecular pathways in THCA patients.