Comprehensive Analysis and Identification of Key Driver Genes for Distinguishing Between Esophageal Adenocarcinoma and Squamous Cell Carcinoma - Wang, Feng; Zhang, Lan; Xu, Yue; Xie, Yilin; Li, Shenglei (Frontiers in Cell and Developmental Biology, 2021)
Background Esophageal cancer (EC) is one of the deadliest cancers in the world. However, the mechanism that drives the evolution of EC is still unclear. On this basis, we identified the key genes and molecular pathways that may be related to the progression of esophageal adenocarcinoma and Squamous Cell Carcinoma to find potential markers or therapeutic targets. Methods GSE26886 were obtained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) among normal samples, EA and squamous cell carcinoma were determined using R software. Then, potential functions of DEGs using DAVID database. The String software was used to identify the most important modules in PPI network. The expression levels of hub genes were confirmed using UALCAN database. Kaplan Meier plotters was used to confirm the correlation between hub genes and outcomes in EC. Results In this study, we identified 1098 genes induced in esophageal adenocarcinoma (EA) and esophageal squamous cell carcinoma (ESCC) and 669 genes were reduced in EA and ESCC, suggesting that these genes may play an important role in the occurrence and development of EC tumors. Bioinformatics analysis showed that these genes were involved in cell cycle regulation, p53 and PI3K/Akt signaling pathway. In addition, we identified 147 induced genes and 130 reduced genes differentially expressed in EA and ESCC. The expression of ESCC in EA group was different from that in control group. By protein-protein interaction (PPI) network analysis, we identified 10 Hub genes, including GNAQ, RGS5, MAPK1, ATP1B1, HADHA, HSDL2, SLC25A20, ACOX1, SCP2 and NLN.TCGA validation showed that these genes were present in the dysfunctional samples between EC and normal samples and between EA and ESCC. Kaplan Meier analysis showed that MAPK1, ACOX1, SCP2 and NLN were associated with overall survival in patients with ESCC and EA. Conclusions In this study, we identified a series of DEGs between EC and normal samples and between EA and ESCC samples. We also identified 10 key genes involved in the EC process. We believe that this study may provide a new biomarker for the prognosis of EA and ESCC.