Construction of thyroid cancer classification and iodine metabolism related diagnostic model using thyroid differentiation score and bioinformation

Abstract

To more accurately diagnose and treat patients with different subtypes of thyroid cancer, we constructed a diagnostic model related to the iodine metabolism of THCA subtypes. THCA expression profiles, corresponding clinicopathological information, and single-cell RNA-seq were downloaded from TCGA and GEO databases. Genes related to thyroid differentiation score were obtained by GSVA. Through logistic analyses, the diagnostic model was finally constructed. DCA curve, ROC curve, machine learning, and K-M analysis were used to verify the accuracy of the model. qRT-PCR was used to verify the expression of hub genes in vitro. There were 104 crossover genes between different TDS and THCA subtypes. Finally, 5 genes (ABAT, CHEK1, GPX3, NME5, and PRKCQ) that could independently predict the TDS subpopulation were obtained, and a diagnostic model was constructed. ROC, DCA, and RCS curves exhibited that the model has accurate prediction ability. K-M and subgroup analysis results showed that low model scores were strongly associated with poor PFI in THCA patients. The model score was significantly negatively correlated with T cell follicular helper. In addition, the diagnostic model was significantly negatively correlated with immune scores. Finally, the results of qRT-PCR corresponded with bioinformatics results. This diagnostic model has good diagnostic and prognostic value for THCA patients, and can be used as an independent prognostic indicator for THCA patients.