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Increasing evidence has indicated a close association between immune infiltration in cancer and clinical outcomes. However, related research in thyroid cancer is still deficient. Our research comprehensively investigated the immune infiltration of thyroid cancer. Data derived from TCGA and GEO databases were analyzed by the CIBERSORT, ESTIMATE, and EPIC algorithms. The CIBERSORT algorithm calculates the proportions of 22 types of immune cells. ESTIMATE algorithm calculates a stromal score to represent all stromal cells in cancer. The EPIC algorithm calculates the proportions of cancer-associated fibroblasts (CAFs) and endothelial cells (ECs), which are the main components of stromal cells. We analyzed the correlation of immune infiltration with clinical characteristics and outcomes of patients. We determined that the infiltration of CD8 +  T cells improved the survival of thyroid cancer patients. Overexpression of immune checkpoints was closely related to the development of thyroid cancer. In general, stromal cells were associated with the progression of thyroid cancer. Interestingly, CAFs and ECs had opposite roles in this process. In addition, the BRAF V600E  mutation was related to the upregulation of immune checkpoints and CAFs and the downregulation of CD8 +  T cells and ECs. Finally, we constructed an immune risk score model to predict the prognosis and development of thyroid cancer. Our research demonstrated a comprehensive panorama of immune infiltration in thyroid cancer, which may provide potential value for immunotherapy.

A new risk factor indicator for papillary thyroid cancer based on immune infiltration