Abstract
Objective: This study aimed to construct a prognostic ferroptosis-related signature for thyroid cancer and probe into the association with tumor immune microenvironment.Methods: Based on the expression profiles of ferroptosis-related genes, a LASSO cox regression model was established for thyroid cancer. Kaplan-Meier survival analysis was presented between high and low risk groups. The predictive performance was assessed by ROC. The predictive independency was validated via multivariate cox regression analysis and stratified analysis. A nomogram was established and verified by calibration curves. The enriched signaling pathways were predicted via GSEA. The association between the signature and immune cell infiltration was analyzed by CIBERSORT. The ferroptosis-related genes were validated in thyroid cancer tissues by immunohistochemistry and RT-qPCR.Results: A ferroptosis-related eight gene model was established for predicting the prognosis of thyroid cancer. Patients with high risk score indicated a poorer prognosis than those with low risk score (p = 1.186e-03). The AUCs for 1-, 2-, and 3-year survival were 0.887, 0.890, and 0.840, respectively. Following adjusting other prognostic factors, the model could independently predict the prognosis (p = 0.015, HR: 1.870, 95%CI: 1.132–3.090). A nomogram combining the signature and age was constructed. The nomogram-predicted probability of 1-, 3-, and 5-year survival approached the actual survival time. Several ferroptosis-related pathways were enriched in the high-risk group. The signature was distinctly associated with the immune cell infiltration. After validation, the eight genes were abnormally expressed between thyroid cancer and control tissues.Conclusion: Our findings established a prognostic ferroptosis-related signature that was associated with the immune microenvironment for thyroid cancer.
Highlights
Thyroid cancer is the most often diagnosed endocrine malignancy, accounting for 1% of all newly diagnosed cancers (1)
Based on 46 differentially expressed ferroptosis-related genes, prognosis-related genes were selected for least absolute shrinkage and selection operator (LASSO) Cox regression analysis
Univariate cox regression analysis demonstrated that DPP4 [hazard ratio (HR): 0.756, 95% confidence interval (CI): 0.623–0.918, p = 0.004], GPX4 (HR: 0.381, 95% CI: 0.147–0.990, p = 0.048) and GSS (HR: 0.361, 95% CI: 0.139–0.935, p = 0.036) were protective factors for thyroid cancer prognosis (Figure 1G)
Summary
Thyroid cancer is the most often diagnosed endocrine malignancy, accounting for 1% of all newly diagnosed cancers (1). In the past 30 years, the global incidence of thyroid cancer has markedly increased (2). The disease is expected to become the fourth major cancer worldwide (3). Surgery followed by radioactive iodine or observation is the main therapy for most of patients (2). The application of high-throughput technology is increasing, which deepens the understanding about the molecular characteristics of thyroid cancer. Molecular markers have become effective tools for predicting prognosis and identifying new therapeutic targets in thyroid cancer management
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