Luo Ziyu, Li Wenhan, Li Jianhui, Zhang Ying
Department of Surgical Oncology, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, 710068, China.
Thyroid Res. 2025 May 1;18(1):18. doi: 10.1186/s13044-025-00234-x.
The Tec family of proteins has been identified as a key player in numerous diseases. However, no studies on the associations of Tec family proteins with overall survival (OS) in differentiated thyroid cancer (DTC) patients have been conducted.
RNA sequencing (RNA-Seq) and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. LASSO-Cox, random forest, and eXtreme Gradient Boosting (XGBoost) analysis methods were used to screen for the genes encoding Tec family proteins that were most closely associated with DTC. A predictive model was developed to estimate the OS of DTC patients. The validity of the prediction model was evaluated via receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and fivefold and 200-fold cross-validation. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the biological functions of the most significant genes.
The AC007494.3 and AC019226.2 genes were most strongly associated with the OS of DTC patients. Therefore, the model can be used to predict the OS of DTC patients. Functional annotation analysis revealed characteristics similar to those of other Tec kinases.
We found that the TEC gene has significant predictive value for the prognosis of DTC patients. The TEC gene has potential value as a target for future drug development. In addition, we recommend more comprehensive treatment and closer monitoring of high-risk populations.
Tec 家族蛋白已被确定为多种疾病的关键因素。然而，尚未有关于 Tec 家族蛋白与分化型甲状腺癌（DTC）患者总生存期（OS）相关性的研究。
从癌症基因组图谱（TCGA）数据库下载 RNA 测序（RNA-Seq）数据和临床数据。使用 LASSO-Cox、随机森林和极端梯度提升（XGBoost）分析方法筛选与 DTC 最密切相关的编码 Tec 家族蛋白的基因。建立预测模型以估计 DTC 患者的 OS。通过受试者工作特征（ROC）曲线、决策曲线分析（DCA）以及五倍和 200 倍交叉验证评估预测模型的有效性。此外，进行基因本体（GO）和京都基因与基因组百科全书（KEGG）分析以研究最显著基因的生物学功能。
AC007494.3 和 AC019226.2 基因与 DTC 患者的 OS 关联最为密切。因此，该模型可用于预测 DTC 患者的 OS。功能注释分析揭示了与其他 Tec 激酶相似的特征。
我们发现 TEC 基因对 DTC 患者的预后具有显著预测价值。TEC 基因作为未来药物开发的靶点具有潜在价值。此外，我们建议对高危人群进行更全面的治疗和更密切的监测。
