摘要
目的 探讨基于与免疫检查点基因相关的基底膜基因构建预测肝癌患者预后模型的可行性,并进行相关性分析及药物敏感性差异分析.方法 从癌症基因组图谱数据库中获取大规模的肝癌患者数据,包括转录组数据、临床特征数据和突变数据.通过整合这些数据,构建一个预后模型,并选择与免疫检查点基因相关的基底膜基因作为独立预后蛋白.通过独立预后分析验证模型的独立性.另外行免疫相关性分析,评估高风险组和低风险组患者之间的差异基因和免疫相关功能.最后进行药物敏感性分析,评估不同风险组患者对常用药物的敏感性差异.结果 根据多变量Cox回归分析得出预后模型公式:风险评分=(1.25968690844101×ADAMTS5表达)+(0.332985729856301×CTSA表达)+(-0.31683170952125×FBLN5表达)+(0.346521502281786×P3H1表达).免疫相关性分析揭示了高风险组和低风险组患者之间的差异基因和免疫相关功能,差异基因包括Cytolytic_activity、Type_Ⅰ_IFN_Reponse、Type_Ⅱ_IFN_Reponse和MHC_class_Ⅰ.单因素预后分析,风险评分的HR和95%CI分别为1.201和1.132,1.274(P＜0.01);多因素预后分析,风险评分的HR和95%CI分别为1.174和1.102,1.251(P＜0.01).受试者工作特征曲线显示,1、3和5年曲线下面积均大于0.5.一致性指数(C-index)曲线显示所构建的模型C-index值最大.药物敏感性分析发现高风险组和低风险组患者在Cyclopamine、Epothilone B、Foretinib、Imatinib、Midostaurin、Oba-toclax Mesylate、Paclitaxel、Pyrimethamine、Sorafenib、Thapsigargin、Tipifarnib和Vinorelbine等的敏感性上存在显著差异.结论 基于与免疫检查点基因相关的基底膜基因的预后模型对肝癌预后有潜在预测价值,其免疫相关性分析和药物敏感性分析可提供肝癌免疫状态和个体化治疗信息,可为肝癌的预后评估和治疗决策提供新的工具和指导,有助于改善患者的生存率和生活质量.
Abstract
Objective To explore the feasibility of constructing a prognostic model of liver cancer patients based on the basal membrane genes associated with immune checkpoint genes and to analyze their correlation and differences in drug sensitivity.Methods Large-scale data of liver cancer patients were obtained from the Cancer Genome Atlas(TCGA)database,including transcriptome data,clinical characteristic data and mutation data.By integrating these data,a prognostic model was constructed and basement membrane genes associated with immune checkpoint genes were selected as independent prog-nostic proteins.The independence of the model was verified by independent prognostic analysis.Immunocorrelation analyses were also performed to assess differential gene and immune-related functions between high-risk and low-risk patients.Finally,drug sensitivity analysis was performed to evaluate the difference in sensitivity of patients in different risk groups to common-ly used drugs.Results According to multivariate Cox regression analysis,the prognostic model formula was obtained:Risk score=(1.25968690844101×ADAMTS5 expression)+(0.332985729856301×CTSA expression)+(-0.31683170952125×FBLN5 expression)+(0.346521502281786×P3H1 expression).Immunocorrelation analysis revealed differential genes and immune-related functions between high-risk and low-risk groups,and differential gens included Cytolytic_activity,Type_Ⅰ_IFN_Reponse,Type_Ⅱ_IFN_Reponse and MHC_class_Ⅰ.In univariate prognostic analysis,HR of risk score and 95％CI were 1.201 and 1.132,1.274,respectively(P<0.01).In multivariate prognostic analysis,HR of risk score and 95％CI were 1.174 and 1.102,1.251,respectively(P<0.01).The receiver operating characteristic(ROC)curve showed that the area un-der the ROC curve(AUC)of 1,3 and 5 years was greater than 0.5.The consistency index(C-index)curve showed that the con-structed model had the largest C-index value.Drug sensitivity analysis found that there were significant differences in the sensi-tivity of Cyclopa-mine,Epothilone B,Foretinib,Imatinib,Midostaurin and Obatoclax,Mesylate,Paclitaxel,Pyrimethamine,Sorafenib,Thapsi-gargin,Tipifarnib and Vinorelbine in high-risk and low-risk groups.Conclusion The prognostic model based on the basal membrane genes associated with immune checkpoint genes has potential predictive value for the prognosis of liver cancer.The immunocorrelation analysis and drug sensitivity analysis can provide information on the immune status and individualized treatment of liver cancer,which can provide new tools and guidance for prognostic assessment and treatment de-cision-making of liver cancer,and help to improve the survival rate and quality of life of patients.
基金项目
国家自然科学基金委员会资助计划项目(32001373)
国家科技期刊平台
NSTL
万方数据