Abstract

The GSE20685 dataset served as the training set, and genes related to prognosis were selected using univariable Cox regression and a p-value of 0.01 as the filtering condition. The correlation coefficients of prognosis-related DEGs between primary and metastatic samples were then identified with the help of the LASSO–Cox regression model. On the basis of these genes and coefficients, a risk prognosis model was developed. Each patient’s risk score was estimated using the model, the median score was used as the cut-off value, and the sample was divided into two groups: the high-risk group (HRG) and the low-risk group (LRG). The HRG and LRG survival curves were analyzed, and the 3-, 5-, and 10-year survival rates were predicted using a time-dependent ROC curve. The prognostic risk model was validated with the help of the GSE45255 dataset. The independent predictive ability of the prognostic model was evaluated by comparing the correlations between risk scores and other clinical prognostic factors using both univariable and multivariable Cox regression analyses.