Identification of a transcription factor-microRNA network in esophageal adenocarcinoma through bioinformatics analysis and validation through qRT-PCR.

The rapidly rising incidence of esophageal adenocarcinoma (EAC), which is usually diagnosed late with a poor prognosis, has become a growing problem. This study investigated the potential transcription factor (TF)-related molecular mechanisms of EAC by using bioinformatics analysis and qRT-PCR validation. Expression profile datasets for mRNAs (GSE92396, GSE13898, GSE26886 and GSE1420) and miRNAs (GSE16456) were downloaded from the GEO database. Overlapping differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified through integrative analysis. Then, a TF-miRNA-mRNA network was constructed based on bioinformatics data from the TRRUST, TRED and miRTarBase database. Furthermore, overall survival analysis for the mRNAs and miRNAs in the TF-miRNA-mRNA network was performed with data from TCGA, and qRT-PCR was used to validate the results. A total of 294 overlapping DEGs were identified in EAC tissues compared to normal tissues, including 181 downregulated and 113 upregulated genes. Then, 16 TFs that could target the DEGs and were related to cancer were predicted based on public databases, and 41 DEGs that could be targeted were identified as key genes. Additionally, 12 DEMs were predicted through miRTarBase to be associated with the key genes, and TP53-(miR-125b)-ID2 and JUN-(miR-30a)-IL1A from the TF-miRNA-mRNA network were identified to potentially play significant roles in EAC. Furthermore, CCL20, IL1A, ABCC3, hsa-miR-23b, and hsa-miR-191, which are involved in the TF-miRNA-mRNA network, were found to be significantly associated with patient survival in EAC. Finally, the expression of a miRNA-mRNA pair (hsa-miR-30a-5p and IL1A) was revealed to be correlated with prognosis. In this study, a TF-miRNA-mRNA network was constructed to analyze the potential molecular mechanisms of EAC. Key genes and miRNAs associated with patient survival were identified, which may reveal promising approaches for EAC diagnosis and therapy.