Deng Jiaying, Weng Xiaoling, Chen Weiwei, Zhang Junhua, Ma Longfei, Zhao Kuaile
Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Transl Cancer Res. 2022 Sep;11(9):3128-3140. doi: 10.21037/tcr-22-915.
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.
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.
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.
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.
基于基因突变预测食管鳞状细胞癌（ESCC）患者预后的列线图模型尚未建立。我们试图开发一种风险分类系统。
本研究选取了我们之前研究中312例具有完整临床和基因组突变情况的患者。国际癌症基因组联盟（ICGC）和癌症基因组图谱（TCGA）的ESCC公共数据也用作外部验证集。
使用最小绝对收缩和选择算子（LASSO）方法，我们成功构建了基于9个基因突变的总生存（OS）预测模型和基于21个基因突变的无进展生存（PFS）模型。使用基于基因突变的分类器对高风险和低风险组进行分层。在训练集和验证集中，高风险组患者的3年和5年总生存率及无进展生存率均较差（P<0.01）。此外，校准曲线和决策曲线分析（DCA）用于确认该预测模型的独立性和潜在转化价值。在列线图分析中，风险分类模型被证明是一种可靠的预后工具。所有结果在外部ICGC和TCGA验证集中显示出更好的一致性。
我们开发并验证了ESCC的预测风险模型。这种实用的预后模型可能有助于医生在临床中做出不同的随访决策。
