基于基因组学数据分析构建肾上腺皮质癌预后模型

Construction of Prognosis Model of Adrenocortical Carcinoma Based on Genomic Data Analysis

目的通过对基因组学数据进行分析,筛选肾上腺皮质癌(ACC)预后相关独立危险因素,并构建ACC预后相关的列线图模型。方法从GEO数据库下载GSE10927数据集数据,利用一系列生物信息学方法,对GSE10927数据集进行处理、分析,并筛选差异基因。通过单因素与多因素的Cox回归分析,筛选与ACC患者预后相关的独立危险因素。使用GEPIA在线分析工具及TCGA数据库数据对上述发现的独立危险因素进行验证。最后,通过R软件构建和验证ACC预后相关列线图模型。结果从GSE10927数据集中获得12个仅在ACC患者中明显差异表达的基因。经过单因素与多因素的Cox回归分析表明,CDK1、PBK、KIAA0101和PRC1可作为ACC患者的预后相关独立危险因素。经过GEPIA在线分析工具及TCGA数据库数据的验证,再次证实CDK1、PBK、KIAA0101和PRC1可作为ACC患者的预后相关独立危险因素。利用上述独立危险因素构建列线图模型,该模型对ACC患者1、3、5年生存率预测性能的AUC值分别为[0.83,95%CI(0.68~0.99)]、[0.89,95%CI(0.82~0.96)]、[0.81,95%CI(0.69~0.92)]。结论CDK1、PBK、KIAA0101和PRC1基因是ACC患者预后相关的独立危险因素,同时有望成为ACC靶向治疗的新靶标。本研究基于基因组学数据分析所构建的列线图模型,可有效预测ACC患者1、3、5年的生存状况。

Objective By analyzing the genomic data,the independent risk factors related to the prognosis of adrenocortical carcinoma(ACC)were screened,and a nomogram model related to ACC prognosis was constructed.Methods GSE10927 data set was downloaded from GEO database.A series of bioinformatics methods were used to process and analyze GSE10927 data set,and screen differential genes.Independent risk factors related to the prognosis of ACC patients were screened by univariate and multivariate Cox regression analysis.GEPIA online analysis tool and data from TCGA database were used to verify the independent risk factors screened.Finally,the nomogram model related to ACC prognosis was constructed and verified by R software.Results Twelve genes that were significantly differentially expressed only in ACC patients were obtained from GSE10927 data set.Univariate and multivariate Cox regression analysis showed that CDK1,PBK,KIAA0101 and PRC1 could be independent risk factors related to the prognosis of ACC patients.Through verification of GEPIA online analysis tool and data from TCGA database,it was confirmed that CDK1,PBK,KIAA0101 and PRC1 could be used as independent risk factors related to the prognosis of ACC patients.A nomogram model was constructed by using the above independent risk factors.The AUC values of the model for predicting the 1-year,3-year and 5-year survival rates of ACC patients were[0.83,95%CI(0.68~0.99)],[0.89,95%CI(0.82~0.96)],[0.81,95%CI(0.69~0.92)],respectively.Conclusion CDK1,PBK,KIAA0101 and PRC1 genes are independent risk factors related to the prognosis of ACC patients,and are expected to be new targets of ACC targeted therapy.In this study,the nomogram model constructed based on genomic data analysis can effectively predict the 1-year,3-year and 5-year survival rates of ACC patients.