基于生物信息学方法构建肝细胞癌铁死亡相关基因预后风险模型  

Construction of a prognostic risk model for ferroptosis-related genes in hepatocellular carcinoma based on bioinformatics methods

作　　者：郝继 柳科军 牛一鸣 唐超峰[1,2] 惠永峰[1,2] 陈本栋 卜阳[1,3] HAO Ji;LIU Kejun;NIU Yiming;TANG Chaofeng;HUI Yongfei;CHEN Bendong;BO Yang(Ningxia Medical University,Yinchuan 750004,China;Department of Hepatobiliary Surgery,General Hospital of Ningxia Medical Uversity,Yinchuan 750004,China;Department of Hepatobiliary Surgery,People's Hospital of Ningxia Hui Autonomous Region,Yinchuan 750002,China)

机构地区：[1]宁夏医科大学,宁夏银川750004 [2]宁夏医科大学总医院肝胆外科,宁夏银川750004 [3]宁夏回族自治区人民医院肝胆外科,宁夏银川750002

出　　处：《宁夏医学杂志》2022年第8期683-688,F0002,F0003,共8页Ningxia Medical Journal

基　　金：宁夏回族自治区重点研发计划项目(2021BEG03067)。

摘　　要：目的基于肿瘤基因组图谱(TCGA)数据库,探索肝细胞癌中差异表达铁死亡相关基因与肿瘤预后的关系,构建肝细胞癌患者预后风险模型。方法从TCGA数据库中下载肝细胞癌转录组和患者对应的临床数据,筛选出癌组织中的差异表达基因,同时结合FerrDb数据库,筛选出与肝细胞癌患者总生存期密切相关的铁死亡基因,采用Losso回归分析和Cox回归分析确认影响预后的关键基因并筛选出与预后相关的差异表达基因。COX回归分析用于构建评估预后的风险评分模型,用诺莫列线图及校准曲线评价模型的预测能力。结果22个铁死亡相关基因在肝细胞癌组织与正常组织中表达差异有统计学意义(P<0.05);GO和KEGG分析显示,差异基因在肿瘤代谢等生物学过程显著富集(P<0.05);Lasso回归分析确认CARS1、FANCD2、SLC1A5、SLC7A11为影响预后的关键基因,成功构建基于铁死亡相关基因的预后模型,模型将所有患者分为高风险与低风险组且提示高风险组的总体生存率显著低于低分险组(P<0.05);ROC曲线显示,1年、3年和5年生存率的曲线下面积分别为0.765、0.673和0.687;多因素COX回归分析提示,风险评分是影响肝细胞癌预后的独立危险因素(HR=1.833,95%CI为1.257~2.673;P<0.05);诺莫列线图及校准曲线提示,预后模型具有良好的预测能力。结论此研究成功构建了铁死亡相关基因的肝细胞癌预后模型,该模型可以对肝细胞癌患者的个体化治疗提供理论依据,并提高肝细胞癌患者的个体化预后预测结果的准确度。Objective Based on The Cancer Genome Atlas(TCGA)database,to explore the relationship between differentially expressed ferroptosis-related genes in hepatocellular carcinoma and tumor prognosis,and to construct a prognostic risk model for hepatocellular carcinoma patients.Methods The hepatocellular carcinoma transcriptome and the clinical data corresponding to the patient were download from the TCGA database and the differentially expressed genes in the cancer tissue were screened out.At the same time,the ferroptosis-related genes closely related to the overall survival of hepatocellular carcinoma patients were screened out by combining with the FerrDb database.identify The key genes affecting the prognosis were confirmed by using Lasso Regression analysis and Cox regression analysis and the differentially expressed genes related to prognosis were screened out.COX regression analysis was used to construct a risk-scoring model for evaluating prognosis,and the predictive ability of Nomogram plot and a calibration curve evaluation model.Results There were 22 ferroptosis-related genes which were differentially expressed between the hepatocellular carcinoma tissues and the normal tissues(P<0.05).GO and KEGG analysis showed that the differential genes were significantly enriched in biological processes such as tumor metabolism(P<0.05).LASSO Regression analysis confirmed that CARS1,FANCD2,SLC1A5,and SLC7A11 were the key genes affecting the prognosis,and a prognostic model based on ferroptosis-related genes was successfully constructed.The model divided all patients into high-risk and low-risk groups and indicated that the overall survival rate of the high-risk group was significantly lower than the low-risk group(P<0.05).The ROC curve showed that the areas under the curve of 1-year,3-year,and 5-year survival rates were 0.765,0.673,and 0.687,respectively.Multivariate COX regression analysis suggested that risk score was an independent risk factor affecting the prognosis of hepatocellular carcinoma(HR=1.833,95%CI:1.257~2.67

关 键 词：肝细胞癌 铁死亡 列线图 预后 

分 类 号：R735.7[医药卫生—肿瘤]