Lin Shang, Chen Di, Pan Chen-Wei, Yang Xiang-Chou
Department of Nuclear Medicine, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China.
The Second School of Clinical Medicine, Wenzhou Medical University, Wenzhou, China.
Transl Cancer Res. 2025 Aug 31;14(8):4662-4678. doi: 10.21037/tcr-2025-460. Epub 2025 Aug 27.
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.
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.
A four-gene risk model, including ATPase secretory pathway Ca transporting 2 (), mast cell expressed membrane protein 1 (), FAM111 trypsin-like peptidase B (FAM111B), and uronyl 2-sulfotransferase (), was established, with significant predictive value for overall survival. High expression of and correlated with poorer prognosis, while and were protective factors. GSEA revealed the involvement of apoptosis and p53 signaling pathways with four risk genes. Additionally, was linked to p53 signaling pathways in CD4 memory cells, suggesting its critical role in THCA progression.
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.
甲状腺分化评分（TDS）反映甲状腺癌（THCA）的分化程度。本研究旨在构建THCA的TDS相关预后风险模型并探索潜在生物标志物。
利用癌症基因组图谱（TCGA）-THCA数据集，鉴定THCA差异表达基因（DEGs）与TDS-DEGs之间的重叠差异表达基因进行功能富集分析，以确定其生物学功能。应用最小绝对收缩和选择算子（Lasso）及Cox回归分析构建预后模型。通过Kaplan-Meier曲线、受试者工作特征曲线和决策曲线分析验证模型的预测性能。进行基因集富集分析（GSEA）以探索功能通路。进行单细胞RNA测序分析以进一步探究风险基因的作用。
建立了一个包含ATPase分泌途径钙转运2（）、肥大细胞表达膜蛋白1（）、FAM111胰蛋白酶样肽酶B（FAM111B）和糖醛酸2-磺基转移酶（）的四基因风险模型，对总生存期具有显著预测价值。和的高表达与较差预后相关，而和是保护因素。GSEA显示四个风险基因参与凋亡和p53信号通路。此外，在CD4记忆细胞中与p53信号通路相关，表明其在THCA进展中的关键作用。
TDS相关基因风险模型在THCA中显示出强大的预后效用。UST可能抑制THCA中的p53信号通路以激活CD4记忆细胞，突出了其作为治疗靶点的潜力。
