Construction and verification of a prognostic model of neutrophil-related genes in clear cell renal cell carcinoma

Background: The tumor microenvironment of clear cell renal cell carcinoma (ccRCC) is heterogeneous, leading to diverse prognoses among patients. Neutrophils, as a key component of the tumor microenvironment, have predictive value for the prognosis of ccRCC. However, there are currently no predictive models based on neutrophil-related genes. This study aimed to construct and validate a prognostic model for ccRCC based on neutrophil-related genes to facilitate risk stratification and treatment guidance. This study aimed to construct and validate a prognostic model for ccRCC based on neutrophil-related genes to facilitate risk stratification and treatment guidance. Methods: We analyzed the RNA sequencing (RNA-seq) data and clinical information of ccRCC, screened out 10 neutrophil-related prognostic genes using R software, and constructed a risk prediction model. Single/multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used for gene screening. Results: Model validation showed that the area under the curve (AUC) values of the model for predicting 1-, 2-, and 3-year overall survival (OS) were 0.704, 0.674, and 0.656 in the test set. Its performance in the training set was better, with AUC values of 0.796, 0.784, and 0.793, respectively. The calibration curve confirmed that the model had good consistency. Kaplan-Meier (KM) survival analysis showed that the survival rate of patients in the high-risk group was significantly lower than that in the low-risk group (P