肾上腺皮质癌肿瘤微环境相关基因预后模型的建立与验证

Construction and verification of tumor microenvironment-related gene prognostic model for adrenocortical carcinoma

目的对利用生物信息学方法筛选的肾上腺皮质癌(ACC)肿瘤微环境(TME)相关基因构建的预后模型进行验证, 为ACC的诊疗提供临床指导和相关生物标志物。方法从癌症基因组图谱(TCGA)数据库中收集79例ACC患者的转录组数据和临床病理数据。采用ESTIMATE算法计算免疫评分、基质评分(二者即反映TME)和ESTIMATE评分, 应用VennDiagram包对免疫评分及基质评分高、低评分组(以中位值进行分组)间差异表达基因进行选择, 采用基因本体(GO)数据库和京都基因与基因组百科全书(KEGG)数据库对选择的基因进行功能富集分析, 探索基因潜在功能与通路。采用单因素Cox回归、lasso回归和多因素Cox回归分析筛选与ACC TME相关的基因, 并建立ACC患者预后的风险评分(RS)模型, 用受试者工作特征(ROC)曲线评价构建的模型预测ACC患者预后的价值。以基因表达综合(GEO)数据库的数据集GSE33371和GSE19750作为外部验证集, 对建立的预后模型进行验证。从TCGA数据库中提取79例ACC患者资料, 将临床病理因素与所构建预后模型的RS纳入Cox回归分析, 获得ACC患者预后影响因素。结果根据免疫评分和基质评分, 用VennDiagram包筛选得到1 205个二者交集的差异表达基因, 其中上调833个, 下调372个。对差异表达基因进行各回归分析筛选后, 最终构建成功包含9个TME相关基因(GREB1、POU4F1、HIC1、HOXC9、CACNB2、RAB27B、ZIC2、C3、CYP2D6)的ACC预后模型, 即RS=GREB1×0.223 6+POU4F1×0.671 7+HIC1×0.167 5+HOXC9×0.211 3+CACNB2×0.156 0+RAB27B×0.956 5+ZIC2×0.582 7+C3×(-0.003 1)+CYP2D6×0.819 3。该模型在TCGA数据库中预测79例ACC患者1、3、5年总生存的ROC曲线下面积(AUC)分别为0.876、0.919、0.917, 在GEO验证集中预测45例ACC患者1、3、5年总生存的AUC分别为0.689、0.704、0.708, 表明模型对ACC患者生存具有较高的预测准确性。对TCGA数据库中79例ACC患者资料进行Cox回归分析显示, TME相关基因预后模型RS是ACC患者预后的独立影响因素(HR=1.011, 95%CI 1.005~1.016, P<0.01)。结论构建的ACC TME相关基因预后模型可用于预测ACC患者的预后。模型中包含的9个基因有可能作为研究ACC发病机制和免疫治疗的新靶点, 值得进一步研究。

Objective Bioinformatics method was used to screen out prognostic model constructed by the tumor microenvironment(TME)-related genes of adrenocortical carcinoma(ACC),and the prognostic model was verified to provide clinical guidances and related biomarkers for the diagnosis and treatment of ACC.Methods Transcriptome and clinicopathological data of 79 ACC patients were collected from the Cancer Genome Atlas(TCGA)database.ESTIMATE algorithm was used to calculate immune score,stromal score(both reflect TME)and ESTIMATE score;VennDiagram was used to select differentially expressed genes among immune score,high and low stromal score groups(grouped by median value);Gene Ontology(GO)database and Kyoto Encyclopedia of Genes and Genome(KEGG)database were used to perform functional enrichment analysis on selected genes and to explore the potential function and pathway of genes.Univariate Cox analysis,lasso regression analysis and multivariate Cox analysis were used to screen out genes related to ACC TME and to establish risk score(RS)model for ACC patients.The receiver operating characteristic(ROC)curve was used to evaluate the prognostic value of RS.The data sets GSE33371 and GSE19750 of Gene Expression Omnibus(GEO)were used as external validation sets to validate the prognostic model.The data of 79 ACC patients were extracted from the TCGA database,and the clinicopathological factors and the RS of the established prognostic model were included in the Cox regression analysis to obtain the prognostic factors of ACC patients.Results According to the immune score and stromal score,1205 differentially expressed genes from intersection of both scores were screened out by using VennDiagram,including 833 up-regulated genes and 372 down-regulated genes.After continuing the regression analysis and screening of differentially expressed genes,the ACC prognostic model containing 9 TME-related genes(GREB1,POU4F1,HIC1,HOXC9,CACNB2,RAB27B,ZIC2,C3,CYP2D6)was finally constructed,that was,RS=GREB1×0.2236+POU4F1×0.6717+HIC1×0.1675+HOXC9×0.2113+CACNB2×0.1560+RAB27B×0.9565+ZIC2×0.5827+C3×(-0.0031)+CYP2D6×0.8193.The area under the curve(AUC)of ROC for the 1,3,and 5-year overall survival of 79 ACC patients predicted by the model in the TCGA database was 0.876,0.919,0.917,respectively.In the GEO validation set,the AUC of the 1,3,and 5-year overall survival for 45 ACC patients predicted by the model was 0.689,0.704,and 0.708,respectively,indicating that the model had a high prediction accuracy for survival results of ACC patients.Cox regression analysis on the data of 79 ACC patients in the TCGA database showed that the TME-related gene prognostic model RS was an independent factor influencing the prognosis of ACC patients(HR=1.011,95%CI 1.005-1.016,P<0.01).Conclusions The established ACC TME-related gene prognostic model can be used to predict the prognosis of ACC patients.The model including 9 genes may become a new target for studying the pathogenesis and immunotherapy of ACC,and it is worthy of further research.