A nomogram and risk classification model predicts prognosis in Chinese esophageal squamous cell carcinoma patients

BACKGROUND: A nomogram model based on gene mutations for predicting the prognosis of patients with resected esophageal squamous cell carcinoma (ESCC) has not been established. We sought to develop a risk classification system. METHODS: In total, 312 patients with complete clinical and genome mutation landscapes in our previous study were chosen for the present study. Public International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) data of ESCC were also used as an external validation set. RESULTS: Using the least absolute shrinkage and selection operator (LASSO) method, we successfully built a 9-gene mutation-based prediction model for overall survival (OS) and a 21-gene mutation model for progression-free survival (PFS). High- and low-risk groups were stratified using the gene mutation-based classifier. Patients in the high-risk group witnessed poorer 3- and 5-year OS and PFS in both the training and validation sets (P<0.01). Moreover, calibration curves and decision curve analyses (DCAs) were used to confirm the independence and potential translational value of this predictive model. In the nomogram analysis, the risk classification model was shown to be a reliable prognostic tool. All results showed better consistency in the external ICGC and TCGA validation sets. CONCLUSIONS: We developed and validated a predictive risk model for ESCC. This practical prognostic model may help doctors make different follow-up decisions in the clinic.