基于免疫相关基因标记的胃癌预后预测模型的构建和验证  

Construction and Validation of a Prognostic Prediction Model for Gastric Cancer Based on Immune-Related Gene Signatures

作　　者：胡震[1,2] 赵绍基 祁玉忠 王光熙 孙开宇 吴文辉[1] HU Zhen;ZHAO Shao-ji;QI Yu-zhong;WANG Guang-xi;SUN Kai-yu;WU Wen-hui(Gastroenterology Center,the Seventh Affiliated Hospital of Sun Yat-sen University,Shenzhen 518107,China;Department of Gastrointestinal Surgery,the First Affiliated Hospital of Sun Yat-sen University,Guangzhou 510080,China)

机构地区：[1]中山大学附属第七医院消化医学中心,深圳518107 [2]中山大学附属第一医院胃肠外科,广州510080

出　　处：《南昌大学学报（医学版）》2024年第2期1-10,17,共11页Journal of Nanchang University：Medical Sciences

基　　金：国家自然科学基金(82203642);广州市科技计划项目(2023A04J2212);深圳市医疗卫生三名工程(SZSM201911010)。

摘　　要：目的开发一种基于免疫特征的预后预测模型,用于精准识别胃癌免疫敏感人群,以期改进联合免疫疗法并揭示肿瘤免疫交互作用的潜在分子特征。方法使用单样本基因集富集分析(ssGSEA)对胃癌免疫景观进行定量评估,并将其划分为具有不同免疫浸润丰度的集群。利用最小绝对收缩和选择算法(LASSO)及Cox回归模型对候选基因进行逐步回归,以构建胃癌预后预测模型并进行外部验证。采用Kaplan-Meier分析、受试者工作特征(ROC)曲线、列线图、校准曲线等评估模型的预测能力。采用IMvigor210队列以及“oncoPredict”包预测免疫治疗应答及药物敏感性。结果基于28个免疫相关基因集的免疫状态定量分析确定并验证了2个具有不同预后和免疫浸润模式的功能簇。识别和构建由6个免疫相关基因组成的特征风险模型,并将其划分为具有不同临床病理特征、预后和免疫背景的风险亚群。高风险亚群具有较差的预后,且伴有免疫抑制、高水平的静止细胞亚群浸润、低频基因突变和较低的免疫检查点分子表达,这些特征与免疫逃逸和预后不良密切相关。不同风险组之间的半最大抑制浓度(IC 50)存在显著差异,这能够有效识别“热肿瘤”及免疫治疗敏感药物的筛选。结论建立并验证基于免疫特征的新型风险模型,其在预测胃癌预后和指导临床肿瘤治疗方面显示出潜在的运用价值。Objective To develop a prognostic risk model based on immune features for precise stratification of gastric cancer(GC),with the aim of improving combined immunotherapy and revealing molecular features of tumor-immune interactions.Methods The immune landscape of GC was quantitatively evaluated using single-sample gene set enrichment analysis(ssGSEA),and patients were classified into clusters with different immune infiltration abundances.The least absolute shrinkage and selection operator(LASSO)Cox regression analysis was then utilized to perform stepwise regression on candidate genes to develop a risk model and conduct external validation.Kaplan Meier analysis,receiver operating characteristic(ROC)curve,nomogram,calibration curve,etc.were performed to evaluate the predictive ability of the model.In addition,the IMvigor210 cohorts and“oncoPredict”packet were used to predict immunotherapy responses and drug sensitivity.Results The study identified and validated two functional clusters with different prognosis and immune infiltration patterns based on 28 immune gene sets,and verified their heterogeneous immune landscapes.Subsequently,a signature composed of six immune-related genes was determined and constructed,and further divided into risk subgroups with different clinical and pathological features,prognosis,and immune landscape.The high-risk subgroup was characterized by immunosuppression,high levels of static cell subsets infiltration,low-frequency gene mutations,and lower immune checkpoint molecule expression levels,which is closely related to immune escape and poor prognosis.There are significant differences in half maximal inhibitory concentration(IC 50)in risk groups,which can effectively distinguish“hot tumors”from“cold tumors”,which is of great significance for guiding the selection of immunotherapy drugs.Conclusion The study established and validated a new classification based on immune characteristics that shows unique value in predicting GC prognosis and guiding the screening of potential dru

关 键 词：胃癌 肿瘤微环境 列线图 免疫治疗 免疫检查点 

分 类 号：R735.2[医药卫生—肿瘤]