Abstract
Cuproptosis is a recently discovered type of programmed cell death that shows significant potential in the diagnosis and treatment of cancer. It has important significance in the prognosis of HSNC. This study aims to construct a cuproptosis-related prognostic model and risk score through new data analysis methods such as machine learning algorithms for the prognosis analysis of HSNC. Protein-protein interaction network and machine learning methods were employed to identify hub genes that were used to construct a TreeGradientBoosting model for predicting overall survival. The relationship between the risk scores obtained from the model and features such as tumor microenvironment (TME) and tumor immunity was explored. The C-indexes of the TreeGradientBoosting model in the training and validation cohorts were 0.776 and 0.848, respectively. The nomogram based on risk scores and clinical features showed good performance, and distinguished the TME and immunity between high-risk and low-risk groups. The cuproptosis-associated risk score can be used to predict prognoses, TME, and tumor immunity of HNSC patients.