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

Jiaying Deng; Xiaoling Weng; Weiwei Chen; Junhua Zhang; Longfei Ma; Kuaile Zhao

doi:10.21037/tcr-22-915

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

Transl Cancer Res. 2022 Sep;11(9):3128-3140. doi: 10.21037/tcr-22-915.

Authors

Jiaying Deng^#^{1

2

3}, Xiaoling Weng^#⁴, Weiwei Chen^#⁵, Junhua Zhang^{1

2

3}, Longfei Ma^{6

7}, Kuaile Zhao^{1

2

3}

Affiliations

¹ Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
² Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
³ Shanghai Key Laboratory of Radiation Oncology, Shanghai, China.
⁴ State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
⁵ Department of Radiotherapy, Yancheng Third People's Hospital, The Affiliated Yancheng Hospital of Southeast University, The Sixth Affiliated Hospital of Nantong University, Yancheng, China.
⁶ Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China.
⁷ Institute of Thoracic Oncology, Fudan University, Shanghai, China.

^# Contributed equally.

Abstract

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.

Keywords: Esophageal squamous cell carcinoma (ESCC); gene mutation; nomogram; risk.