Development of a Risk Predictive Model for Evaluating Immune Infiltration Status in Invasive Thyroid Carcinoma.

Aims: This study aimed to reveal the molecular characteristics and potential biomarker of immune-activated and immunosuppressive invasive thyroid carcinoma. Methods: Expression and clinical data for invasive thyroid carcinoma were obtained from the TCGA database. Tumor samples were divided into immune-activated or immunosuppressive groups based on the immune enrichment score calculated by ssGSEA. Differentially expressed genes (DEGs) between tumor vs. normal groups or between immune-activated vs. immunosuppressive groups were screened, followed by functional enrichment. Immune infiltration was evaluated using the ESTIMATE, CIBERSORTx, and EPIC algorithms, respectively. A random forest algorithm and Lasso cox analysis were used to identify gene signatures for risk model construction. Results: Totally 1171 DEGs were screened between tumor vs. normal groups, and multiple tumorigenesis-associated pathways were significantly activated in invasive thyroid carcinoma. Compared to immune-activated samples, immunosuppressive samples showed higher tumor purity, lower immune/stromal scores, and lower expression of immune markers, as well as lower infiltration abundance of CD4+ T cells and CD8+ T cells. A risk model based on a 12-immune signature (CCR7, CD1B, CD86, CSF2RB, HCK, HLA-DQA1, LTA, LTB, LYZ, NOD2, TNFRSF9, and TNFSF11) was developed to evaluate the immune infiltration status (AUC = 0.998; AUC of 0.958 and 0.979 in the two external validation datasets), which showed a higher clinical benefit and high accuracy. Immune-activated samples presented lower IC50 value for bortezomib, MG.132, staurosporine, and AZD8055, indicating sensitivity to these drugs. Conclusion: A 12-gene-based immune signature was developed to predict the immune infiltration status for invasive thyroid carcinoma patients and then to identify the subsets of invasive thyroid carcinoma patients who might benefit from immunotherapy.