免疫检查点相关基因预测结肠癌患者预后及其免疫状态  

Immune Checkpoint⁃Related Genes Predict Prognosis and Immune Status in Patients With Colon Cancer

作　　者：梁骏[1] 魏学标 侯晓东 廖小龙 温剑艺 郭伟新[1] 李汉彪 王首红[1] LIANG Jun;WEI Xue-biao;HOU Xiao-dong;LIAO Xiao-long;WEN Jian-yi;GUO Wei-xin;LI Han-biao;WANG Shou-hong(Geriatric Critical Care Medicine,Guangdong Provincial Geriatrics Institute,Southern Medical University,Guangzhou 510080,China;Guangdong Provincial Cadre Health Management Center,Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences),Southern Medical University,Guangzhou 510080,China)

机构地区：[1]老年重症医学科,广东省老年医学研究所,南方医科大学附属广东省人民医院(广东省医学科学院),广州510080 [2]广东省干部健康管理中心,南方医科大学附属广东省人民医院(广东省医学科学院),广州510080

出　　处：《循证医学》2022年第6期375-384,共10页The Journal of Evidence-Based Medicine

摘　　要：目的免疫检查点基因是调控结肠癌患者免疫应答的关键机制,并对结肠癌患者的预后、治疗具有潜在的临床应用价值,然而目前尚无基于免疫检查点基因的预后模型来预测结肠癌患者的预后和免疫状态。方法从肿瘤基因组图谱(The Cancer Genome Atlas,TCGA)和高通量基因表达(Gene Expression Omnibus,GEO)公共数据库获取并整理结肠癌患者的基因表达谱数据及其对应的临床信息,鉴定差异表达的免疫检查点基因。采用最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)Cox回归分析构建免疫检查点基因相关的TCGA结肠癌患者预后模型。应用GEO结肠癌患者队列(GSE143985)进行验证。Kaplan⁃Meier曲线和受试者操作特征(receiver operating characteristic,ROC)曲线用于评估模型预测准确性。Spearman相关性分析观察预后模型与免疫细胞浸润情况。单因素和多因素回归分析免疫检查点风险基因及临床指标,并基于多因素回归结果构建临床列线图。结果在正常和结肠癌组织中共筛选出29个差异表达的免疫检查点相关基因,LASSO回归分析后构建了基于14个免疫检查点相关基因的预后模型。根据中位风险评分将TCGA结肠癌患者分为高、低危险组,Kaplan⁃Meier生存分析显示,相较于低危组,高危组患者的生存较差。ROC分析则显示TCGA训练队列的1年、3年预测曲线下面积(area under the curve,AUC)值均大于0.7,而GEO验证队列的1年、3年预测AUC值均大于0.8,显示了模型较高的准确性和稳定性。免疫细胞浸润分析显示风险评分与CD4+T细胞和CD8+T细胞有显著相关性。此外,基于免疫检查点风险基因及临床指标构建的列线图C⁃指数为0.767,显示了较高的临床应用价值。结论我们构建了一种新的免疫检查点基因相关的结肠癌预后模型,可用于结肠癌的预后及免疫状态预测,为结肠癌患者的临床预后提供一定的指导依据Objective Immune checkpoint gene was a key mechanism regulating the immune response in patients with colon cancer and had potential clinical application value for the prognosis and treatment of patients with colon cancer.However,there was no prognostic model based on the immune checkpoint genes to predict colon cancer patients􀆳prognosis and immune status.Methods The gene expression profile data and corresponding clinical information of colon cancer patients were collected from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO)public databases and the differentially expressed immune checkpoint genes were identified.The least absolute contraction and selection operator(LASSO)Cox regression analysis was used to construct an immune checkpoint gene⁃related prognostic model of TCGA patients with colon cancer.The GEO colon cancer patient cohort(GSE143985)was used for validation.Kaplan⁃Meier curve and receiver operating characteristic(ROC)curve were used to evaluate the prediction accuracy of the model.Spearman correlation analysis was used to observe the prognosis model and immune cell infiltration.The immune checkpoint risk genes and clinical indicators were analyzed by univariate and multifactorial regression,and a clinical histogram was constructed based on the multivariate regression results.Results A total of 29 differentially expressed immune checkpoint⁃related genes were screened in normal and colon cancer tissues.After LASSO regression analysis,a prognostic model based on 14 immune checkpoint⁃related genes was constructed.The TCGA colon cancer patients were divided into high⁃risk and low⁃risk groups based on the median risk score.Kaplan⁃Meier survival analysis showed that patients in the high⁃risk group had poorer survival than those in the low⁃risk group.ROC analysis showed that the 1⁃year forecast area under the curve(AUC)values of the TCGA training cohort and the 3⁃year forecast AUC values were all greater than 0.7,while the 1⁃year forecast AUC values of the GEO verificat

关 键 词：免疫检查点 结肠癌 预后 免疫 列线图 

分 类 号：R735.3[医药卫生—肿瘤]