Signature Based on Six Autophagy-related Genes to Predict Prognosis of Head and Neck Squamous Cell Carcinoma

Objective The present study aimed to develop an autophagy-related gene prognostic prediction model to provide survival risk prediction for head and neck squamous cell carcinoma (HNSCC) patients. Methods The K-mean cluster analysis was performed on HNSCC samples based on the expression values of 210 autophagy-related genes for candidate signature gene selection. LASSO Cox regression analysis was generated using the potential genes and the risk score was calculated from the prognosis model. The risk score was processed as an independent prognostic indicator to construct the nomogram model. The immune status including immune cell infiltration ratio and checkpoints of patients with HNSCC in high- and low-risk groups was also explored. Results LASSO Cox regression analysis was performed on the selected autophagy-related genes. According to the lambda value corresponding to the number of different genes in the LASSO Cox analysis, six genes (GABARAPL2, SAR1A, ST13, GAPDH, FADD and LAMP1) were finally chosen. The risk score based on the genes was generated, which was an independent prognostic marker for HNSCC. The prognostic prediction model (nomogram) was further optimized by the independent prognostic factors (risk score), which can better predict the prognosis and survival of patients. With the risk score and prognosis model, eight types of immune cells and six key immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, TIGIT) displayed expression specificity. Conclusion This study identified several potential prognostic biomarkers and established an autophagy-related prognostic prediction model for HNSCC, which provides a valuable reference for future clinical research.