A new Tec family-based clinical model predicts survival in differentiated thyroid cancer patients via machine learning - Ziyu Luo; Wenhan Li; Jianhui Li; Ying Zhang (Thyroid Research)
Abstract Background 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. Methods 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. Results 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. Conclusions 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.