Constructing a thyroid cancer prognostic risk model based on CD8(+) T cell associated genes

Thyroid cancer (TC) is a common and curable endocrine tumor occurring in the head and neck characterized by a low mortality rate compared to other malignancies. In this study, the immune microenvironment of TC was investigated to identify biomarkers. The mRNA and clinical data available in this study were accessed from The Cancer Genome Atlas-Thyroid Cancer (TCGA-THCA) dataset. Differences in immune infiltration levels of TC and normal samples were assessed by CIBERSORT. Thyroid cancer samples were classified into high- and low-abundance groups according to the median abundance of immune cell infiltration, and CD8(+) T cells were notably correlated with the survival status. Differential expression analysis was conducted on CD8(+) T cells to obtain immune-related differentially expressed genes (DEGs). Subsequently, a prognostic risk model was established through Cox regression analysis. According to the median risk score, samples in the training set and validation set were assigned to high- and low-risk groups. The survival and ROC curves demonstrated that the model possesses favorable prognostic prediction ability. Furthermore, the results of gene set enrichment analysis (GSEA) indicated differences between the high- and low-risk groups in terms of ECM receptor interaction and transforming growth factor β (TGF-β) signaling pathways. The tumor microenvironment of TC samples was evaluated by ESTIMATE, which showed that stromal scores were higher in the high-risk group. Finally, simple-sample GSEA (ssGSEA) was performed on TC samples. The results indicated a higher infiltration level of NK cells in the low-risk group, as well as a lower level in the high-risk group. In terms of immune function-related gene sets, genes related to APC co-inhibition, cytolytic activity, HLA and T cell co-inhibition were observed to present higher expression levels in the low-risk group. In general, this study built a 6-gene prognostic risk assessment model based on CD8(+) T cells through bioinformatics analysis, which is expected to be a reference for clinicians to judge the prognosis of TC patients.