Novel a new prognostic model of Thyroid Carcinoma based on Macrophage and Cuproptosis-Related lncRNA

Background Thyroid carcinoma is a type of malignant tumor with a relatively good prognosis, but some subtypes 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. Methods Single-cell sequencing analysis was conducted to identifyt genes associated with cuproptosis and differentially expressed genes within macrophages. The Wilcoxon algorithm was employed to identify tumor-related genes. The overlapping genes were utilized to discover lncRNAs that are related to both macrophages and cuproptosis. Following this, Lasso analysis was applied to develop a prognostic model. We then stratified patients into two groups based on their median risk scores and assessed their survival outcomes and responses to immune therapy. Additionally, we examined the mutational profiles of patients in both the high-risk and low-risk groups. Results Our prognostic risk model has identified 11 lncRNAs: AC107214.1, AC135050.1, AC026355.3, FOXP1-AS1, AC123768.1, MIR3945HG, AC022819.1, AC009716.1, AC120498.9, AL133444.1, and AL132709.8. It has been confirmed that our prognostic model boasts an exceptionally high accuracy, with a precision rate exceeding 0.8 in forecasting the survival outcomes of patients in the TCGA-THCA cohort. 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. Conclusion Our study highlights the potential of macrophage and cuproptosis-related lncRNAs as novel predictive biomarkers for thyroid carcinoma.