基于GEO和TCGA数据库食管鳞状细胞癌预后模型构建与验证

Construction and validation of a prognostic model of esophageal squamous cell carcinoma based on GEO and TCGA databases

目的探讨食管鳞状细胞癌(esophageal squamous cell carcinoma,ESCC)组织与正常组织之间的差异表达基因,构建ESCC的预后相关模型并验证其临床应用价值。方法首先,基于GSE20347、GSE23400、GSE26886、GSE45168、GSE77861数据集确定ESCC的差异表达基因。其次,经通路富集后使用STRING和Cytoscape软件筛选关键基因。随后,Kaplan-Meier Plotter和单变量Cox回归用于生存分析,多因素Cox回归应用于构建预后模型。实时荧光定量PCR用于验证预后相关基因的差异表达情况。此外,我们通过Kaplan-Meier曲线、ROC曲线、一致性指数、灵敏度和特异度验证模型的预后价值。最后,利用基因集富集分析进一步探讨ESCC预后机制。结果本研究发现98个差异表达基因和15个关键基因,其中6个关键基因与预后相关。此外,基于VCAN、ALOX12和ACPP的预后模型经验证临床应用价值较好。结论基于VCAN、ALOX12和ACPP的预后模型可独立预测ESCC的预后。

Objective To explore the differentially expressed genes between esophageal squamous cell carcinoma(ESCC)and normal tissues then construct a risk signature with prognostic value for ESCC.Methods The differentially expressed genes of ESCC in GSE20347,GSE23400,GSE26886,GSE45168,GSE77861 were obtained using R language.Then the functional annotations analysis of differentially expressed genes was carried out and the hub genes were identified for the retrieval of interacting genes by STRING and Cytoscape software.Based on the data from The Cancer Genome Atlas(TCGA),the survival analyses was performed by Kaplan-Meier Plotter and univariate Cox regression and constructed a risk signature of 3 genes via multiple Cox regression.Furthermore,Kaplan-Meier curve,ROC curve and nomogram were performed to explore the prognostic value of the signature.Finally,the gene set enrichment analysis(GSEA)was utilized to gain insight into the pathways involved in ESCC prognosis.Results We found 98 differentially expressed genes and 15 hub genes,only 6 genes of them were found associated with survival based on TCGA database.Furthermore,a novel three-gene risk signature(including VCAN,ALOX12 and ACPP)from the differentially expressed genes was constructed and acted as a significant and independent prognostic signature for ESCC.Conclusion The three-gene(VCAN,ALOX12 and ACPP)signature might be an independent biomarker with a significant predictive value in ESCC overall survival.