Novel a new prognostic model of thyroid carcinoma based on macrophage and cuproptosis-related lncRNA

Thyroid carcinoma is a type of malignant tumor with a relatively good prognosis, but some types have a poorer prognosis. Therefore, it is necessary to establish a new prognostic model to predict the survival outcomes and immune therapy responses of thyroid cancer patients. ScRNA analysis was conducted to identify cuproptosis-related genes and macrophages-related genes. The Wilcoxon algorithm was employed to identify tumor-related genes. The overlapping genes were utilized to identify lncRNAs that are related to both macrophages and cuproptosis. Lasso was used to construct a prognostic model. Based on the model, we conducted survival analysis, mutational profile analysis, and drug sensitivity analysis. Our prognostic risk model has identified 11 macrophage and cuproptosis-related lncRNAs. It has been confirmed that our prognostic model showed favorable performance in predicting the survival outcomes of patients in the TCGA-THCA cohort, with an AUC value exceeding 0.8, supporting its potential value for prognostic assessment of thyroid carcinoma. In this study, we observed no significant disparity in gene mutation rates between the high-risk and low-risk groups. Patients categorized in the low-risk group exhibit heightened sensitivity to immunotherapy and demonstrate responsiveness to a variety of immunotherapeutic agents, such as dasatinib. Our study highlights the potential of macrophage and cuproptosis-related lncRNAs as novel predictive biomarkers for thyroid carcinoma.