Translational cancer research · 2025
Thyroid differentiation score-related genes and prognostic model for thyroid cancer.
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Lin Shang, Chen Di, Pan Chen-Wei, Yang Xiang-Chou
Аннотация
BACKGROUND: Thyroid differentiation score (TDS) reflects the differentiation degree of thyroid cancer (THCA). This study aimed to construct a TDS-related prognostic risk model for THCA and explore the potential biomarkers.
METHODS: Using The Cancer Genome Atlas (TCGA)-THCA dataset, overlapping differentially expressed genes (DEGs) between THCA-DEGs and TDS-DEGs were identified for functional enrichment analyses to determine their biological functions. Least absolute shrinkage and selection operator (Lasso) and Cox regression analyses were applied to construct a prognostic model. The model's predictive performance was validated through Kaplan-Meier curves, receiver operating characteristic curves, and decision curve analyses. Gene set enrichment analysis (GSEA) was performed to explore the functional pathways. Single-cell RNA sequencing analysis was performed to further explore the role of risk genes.
RESULTS: A four-gene risk model, including ATPase secretory pathway Ca2+ transporting 2 (ATP2C2), mast cell expressed membrane protein 1 (MCEMP1), FAM111 trypsin-like peptidase B (FAM111B), and uronyl 2-sulfotransferase (UST), was established, with significant predictive value for overall survival. High expression of ATP2C2 and MCEMP1 correlated with poorer prognosis, while FAM111B and UST were protective factors. GSEA revealed the involvement of apoptosis and p53 signaling pathways with four risk genes. Additionally, UST was linked to p53 signaling pathways in CD4+ memory cells, suggesting its critical role in THCA progression.
CONCLUSIONS: The TDS-related gene risk model demonstrates strong prognostic utility in THCA. UST may inhibit the p53 signaling pathway to activate CD4+ memory cells in THCA, highlighting its potential as a therapeutic target.
METHODS: Using The Cancer Genome Atlas (TCGA)-THCA dataset, overlapping differentially expressed genes (DEGs) between THCA-DEGs and TDS-DEGs were identified for functional enrichment analyses to determine their biological functions. Least absolute shrinkage and selection operator (Lasso) and Cox regression analyses were applied to construct a prognostic model. The model's predictive performance was validated through Kaplan-Meier curves, receiver operating characteristic curves, and decision curve analyses. Gene set enrichment analysis (GSEA) was performed to explore the functional pathways. Single-cell RNA sequencing analysis was performed to further explore the role of risk genes.
RESULTS: A four-gene risk model, including ATPase secretory pathway Ca2+ transporting 2 (ATP2C2), mast cell expressed membrane protein 1 (MCEMP1), FAM111 trypsin-like peptidase B (FAM111B), and uronyl 2-sulfotransferase (UST), was established, with significant predictive value for overall survival. High expression of ATP2C2 and MCEMP1 correlated with poorer prognosis, while FAM111B and UST were protective factors. GSEA revealed the involvement of apoptosis and p53 signaling pathways with four risk genes. Additionally, UST was linked to p53 signaling pathways in CD4+ memory cells, suggesting its critical role in THCA progression.
CONCLUSIONS: The TDS-related gene risk model demonstrates strong prognostic utility in THCA. UST may inhibit the p53 signaling pathway to activate CD4+ memory cells in THCA, highlighting its potential as a therapeutic target.