Predictions for high and expression resulting in a poor prognosis in esophageal squamous cell carcinoma by bioinformatics analyses.

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is one of the most common malignant neoplasms of the digestive tract worldwide. The lack of key molecular biomarkers is associated with the poor prognosis in ESCC patients. The present study was aimed to identify candidate genes for diagnostic, prognostic, and therapeutic applications in ESCC by bioinformatics.

METHODS

Two datasets of ESCC (GSE20347 and GSE70409) from gene expression omnibus (GEO) were analyzed using GEO2R online tool to identify the differentially expressed genes (DEGs). Subsequently, functions and pathways enrichment analyses of DEGs and their protein-protein interaction (PPI) network analyses were performed. When key DEGs were identified, their relationship with ESCC prognosis was further validated.

RESULTS

There were 134 commonly changed DEGs (33 up-regulated and 101 down-regulated) from GSE20347 and GSE70409 datasets were identified using integrated bioinformatical analysis. Gene ontology (GO) and pathway enrichment analysis was performed to annotate genes and gene products, highlight biological processes (BPs) and systemic functional information. Through the PPI network and cluster analysis, two clusters containing 21 key DEGs were detected and 14 of them were validated based on TCGA and GTEx data. Among these key DEGs, and were significantly associated with the prognosis in ESCC cases.

CONCLUSIONS

In conclusion, a total of 14 key DEGs and outcome in ESCC were identified by integrated bioinformatics analyses. and might be novel potential diagnostic and prognostic biomarkers in ESCC.