Abstract
PURPOSE: An increasing number of patients with lung squamous cell carcinoma (LUSC) are benefiting from immunotherapy. However, the individual immune profile of patients who respond to treatment is unclear. Multiple programmed cell death (PCD) patterns play an important role in the proliferation and differentiation of tumor cells, predicting the efficacy of immunotherapy using a risk model for programmed cell death gene combinations LUSC risk model. METHODS: Genes associated with 12 types of PCD were analyzed to establish a prognostic model. Risk scores were calculated using PCDG-based expression profiles, and LUSC patients were classified into two groups. Tumor immune microenvironment (TIME) characteristics and immunotherapy responses were compared between the two groups. Finally, staging was predicted using the extreme gradient boosting tree algorithm (eXtreme Gradient Boosting, XGBoost), and an algorithmic model was constructed to predict the prognosis of LUSC patients based on the PCDG risk score. RESULTS: A stepwise downscaling of 1256 PCDGs was performed to screen out 16 genes associated with LUSC prognosis to construct a risk model. Immune cell infiltration levels, the immunotherapy response, and prognostic differences were different between these two groups of patients. The classification prediction model based on the XGBoost algorithm and the prognostic model based on the risk score were able to distinguish the risk subtypes and individual prognosis of LUSC patients, respectively. CONCLUSIONS: PCD patterns exert a crucial effect on the development of LUSC. An evaluation of different PCD patterns in LUSC improves the understanding of the characteristics of infiltrating immune cells and mutational features of the TIME, distinguishes LUSC patients who might benefit from immunotherapy, and predicts their future survival.