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

Authors Chen D, Lu T, Tan J, Zhao K, Li Y, Zhao W, Li H, Wang Q, Wang Y, Wei L

Received 11 January 2019

Accepted for publication 25 February 2019

Published 18 April 2019 Volume 2019:11 Pages 3315—3326

DOI https://doi.org/10.2147/CMAR.S201274

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Cristina Weinberg

Peer reviewer comments 2

Editor who approved publication: Dr Chien-Feng Li


Di Chen,1 Tong Lu,2 Junying Tan,1 Kun Zhao,1 Yuli Li,1 Wenjie Zhao,1 Hao Li,1 Qiuyue Wang,1 Yuanyong Wang,2 Liangzhou Wei1

1Department of Gastroenterology, Affiliated Hospital of Qingdao University, Qingdao 266003, People’s Republic of China; 2Department of Thoracic Surgery, Affiliated Hospital of Qingdao University, Qingdao 266003, People’s Republic of China

Purpose: 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.
Methods: 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.
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.
Conclusion: 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.

Keywords: esophageal adenocarcinoma, prognosis, differentially expressed genes, microRNA, transcription factor

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