Li Suihui, Zhu Jinfeng, Zhu Tengfei, Xu Yu, Chen Wenxi, Zhou Qiaoxia, Wang Guoqiang, Li Leo, Han Yusheng, Xu Chunwei, Wang Wenxian, Cai Shangli, Xu Ruilian, Shao Yu
Department of Oncology, First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, China.
Department of Internal Medicine-Oncology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China.
J Gastrointest Oncol. 2023 Apr 29;14(2):599-616. doi: 10.21037/jgo-23-128.
Gastric cancer (GC) is an aggressive disease that requires prognostic tools to aid in clinical management. The prognostic power of clinical features is unsatisfactory, which might be improved by combining mRNA-based signatures. Inflammatory response is widely associated with cancer development and treatment response. It is worth exploring the prognostic performance of inflammatory-related genes plus clinical factors in GC.
An 11-gene signature was trained using the least absolute shrinkage and selection operator (LASSO) based on the messenger RNA (mRNA) and overall survival (OS) data of The Cancer Genome Atlas-stomach adenocarcinoma (TCGA-STAD) cohort. A nomogram was established using the signature and clinical factors with a significant linkage with OS and was validated in 3 independent cohorts (GSE15419, GSE13861, and GSE66229) via calculating the area under the receiver operator characteristic curve (AUC). The association between the signature and immunotherapy efficacy was explored in the ERP107734 cohort.
A high risk score was associated with shorter OS in both the training and the validation sets (the AUC for 1-, 3-, 5-year in TCGA-STAD cohort: 0.691, 0.644, and 0.707; GSE15459: 0.602, 0.602, and 0.650; GSE13861: 0.648, 0.611, and 0.647; GSE66229: 0.661, 0.630, and 0.610). Its prognostic power was improved by combining clinical factors including age, sex, and tumor stage (the AUC for 1-, 3-, 5-year in TCGA-STAD cohort: 0.759, 0.706, and 0.742; GSE15459: 0.773, 0.786, and 0.803; GSE13861: 0.749, 0.881, and 0.795; GSE66229: 0.773, 0.735, and 0.722). Moreover, a low-risk score was associated with a favorable response to pembrolizumab monotherapy in the advanced setting (AUC =0.755, P=0.010).
In GCs, the inflammatory response-related gene-based signature was related to immunotherapy efficacy, and its risk score plus clinical features yielded robust prognostic power. With prospective validation, this model may improve the management of GC by enabling risk stratification and the prediction of response to immunotherapy.
胃癌（GC）是一种侵袭性疾病，需要预后工具来辅助临床管理。临床特征的预后能力并不理想，通过结合基于mRNA的特征可能会有所改善。炎症反应与癌症发展和治疗反应广泛相关。探索炎症相关基因加临床因素在胃癌中的预后表现是值得的。
基于癌症基因组图谱-胃腺癌（TCGA-STAD）队列的信使核糖核酸（mRNA）和总生存期（OS）数据，使用最小绝对收缩和选择算子（LASSO）训练一个11基因特征。使用该特征和与OS有显著关联的临床因素建立列线图，并通过计算受试者工作特征曲线下面积（AUC）在3个独立队列（GSE15419、GSE13861和GSE66229）中进行验证。在ERP107734队列中探索该特征与免疫治疗疗效之间的关联。
在训练集和验证集中，高风险评分均与较短的OS相关（TCGA-STAD队列中1年、3年、5年的AUC分别为0.691、0.644和0.707；GSE15459为0.602、0.602和0.650；GSE13861为0.648、0.611和0.647；GSE66229为0.661、0.630和0.610）。通过结合年龄、性别和肿瘤分期等临床因素，其预后能力得到了提高（TCGA-STAD队列中1年、3年、5年的AUC分别为0.759、0.706和0.742；GSE15459为0.773、0.786和0.803；GSE13861为0.749、0.881和0.795；GSE66229为0.773、0.735和0.722）。此外，在晚期情况下，低风险评分与帕博利珠单抗单药治疗的良好反应相关（AUC =0.755，P=0.010）。
在胃癌中，基于炎症反应相关基因的特征与免疫治疗疗效相关，其风险评分加临床特征具有强大的预后能力。经过前瞻性验证，该模型可能通过实现风险分层和预测免疫治疗反应来改善胃癌的管理。
