Data mining of esophageal squamous cell carcinoma from The Cancer Genome Atlas database
10.3760/cma.j.issn.0253-3766.2018.07.007
- VernacularTitle: 基于肿瘤基因图谱计划挖掘食管鳞癌数据
- Author:
Siyuan HE
1
;
Xiaobing WANG
1
;
Yuchen JIAO
1
Author Information
1. State Key Lab of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
- Publication Type:Clinical Trail
- Keywords:
Esophageal neoplasms;
Neoplasms, squamous cell;
Differentially expressed genes;
Prognosis
- From:
Chinese Journal of Oncology
2018;40(7):517-522
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To deeply investigate the gene expression profiles of esophageal squamous cell carcinoma (ESCC) and the relationship of gene expression levels with prognosis from The Cancer Genome Atlas (TCGA) database.
Methods:RNA-seq V2 data of 11 normal samples and 81 esophageal squamous cell carcinoma patients, and their corresponding clinical data were downloaded from The Cancer Genome Atlas database. Differentially expressed genes between normal and tumor samples were identified by using edgeR package. Gene function enrichment analyses of differentially expressed genes were conducted. A protein-protein interaction network based on differentially expressed genes was constructed by using STRING database and the hub genes were identified based on the created gene co-expression network. In addition, survival analysis was performed.
Results:Totally, 2 788 genes were identified as differential expression. Among these, 1 168 genes were up-regulated and 1 620 genes were down-regulated in tumor cases compared with normal samples. Up-regulated genes were enriched in cell cycle, DNA replication and mismatch repair pathways, while down-regulated genes were enriched in metabolic pathways. 707 genes and their 3 428 interactions were identified by protein-protein interaction analysis. Genes with copy number amplifications were considered to interact with other crucial genes. 10 co-expression modules were identified based on the gene co-expression network analysis and the ribosomal protein genes were illustrated to be correlated with tumor locations of ESCC patients (P=0.003). The 3-years survival rates of high and low expression of TNFRSF10B groups were 82.5% and 15.1%, respectively. Similarly, the 3-years survival rates of high and low expression of DDX18 groups were 82.4% and 15.2%, respectively. The survival differences stratified by these two genes were statistically significant (both P<0.1).
Conclusions:The analysis results of TCGA database showed that ribosomal protein genes are correlated with tumor locations of ESCC patients. Low expressions of TNFRSF10B and DDX18 are associated with poor prognose of ESCC patients. Consequently, TNFRSF10B and DDX18 may serve as predictive markers for ESCC patients.
