
This study aims to explore the immunological characteristics of lung squamous cell carcinoma (LUSC) patients who may benefit from immunotherapy. The research utilizes a machine-learning approach to analyze programmed cell death genes (PCDGs) and construct a risk model for LUSC. Key genes associated with LUSC prognosis are identified, and a risk function is developed to classify high- and low-risk groups. The study evaluates the intrinsic immune microenvironment, predicts immunotherapy efficacy, and builds a prognostic model for LUSC patients based on risk scores.
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