scholarly journals Presence of evolutionary conserved gene expression signatures during progression of Barrett's esophagus to esophageal adenocarcinoma in human and rat

2011 ◽  
Vol 25 (S1) ◽  
Author(s):  
Soo Mi Kim ◽  
Ju‐Seog Lee
PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260353
Author(s):  
Chengjiao Yao ◽  
Yilin Li ◽  
Lihong Luo ◽  
Qin Xiong ◽  
Xiaowu Zhong ◽  
...  

Barrett’s esophagus (BE) is defined as any metaplastic columnar epithelium in the distal esophagus, which predisposes to esophageal adenocarcinoma (EAC). Yet, the mechanism through which BE develops to EAC still remain unclear. Moreover, the miRNA-mRNA regulatory network in distinguishing BE from EAC still remains poorly understood. To identify differentially expressed miRNAs (DEMs) and genes (DEGs) between EAC and BE from tissue samples, gene expression microarray datasets GSE13898, GSE26886, GSE1420 and miRNA microarray datasets GSE16456, GSE20099 were downloaded from Gene Expression Omnibus (GEO) database. GEO2R was used to screen the DEMs and DEGs. Pathway and functional enrichment analysis were performed by DAVID database. The protein–protein interaction (PPI) network was constructed by STRING and been visualized by Cytoscape software. Finnal, survival analysis was performed basing TCGA database. A total of 21 DEMs were identified. The enriched functions and pathways analysis inclued Epstein-Barr virus infection, herpesvirus infection and TRP channels. GART, TNFSF11, GTSE1, NEK2, ICAM1, PSMD12, CTNNB1, CDH1, PSEN1, IL1B, CTNND1, JAG1, CDH17, ITCH, CALM1 and ITGA6 were considered as the hub-genes. Hsa-miR-143 and hsa-miR-133b were the highest connectivity target gene. JAG1 was predicted as the largest number of target miRNAs. The expression of hsa-miR-181d, hsa-miR-185, hsa-miR-15b, hsa-miR-214 and hsa-miR-496 was significantly different between normal tissue and EAC. CDH1, GART, GTSE1, NEK2 and hsa-miR-496, hsa-miR-214, hsa-miR-15b were found to be correlated with survival.


2007 ◽  
Vol 1 ◽  
pp. BBI.S311 ◽  
Author(s):  
Florin M. Selaru ◽  
Suna Wang ◽  
Jing Yin ◽  
Karsten Schulmann ◽  
Yan Xu ◽  
...  

Background and Aims Because of the extremely low neoplastic progression rate in Barrett's esophagus, it is difficult to diagnose patients with concomitant adenocarcinoma early in their disease course. If biomarkers existed in normal squamous esophageal epithelium to identify patients with concomitant esophageal adenocarcinoma, potential applications would be far-reaching. The aim of the current study was to identify global gene expression patterns in normal esophageal epithelium capable of revealing simultaneous esophageal adenocarcinoma, even located remotely in the esophagus. Methods Tissues comprised normal esophageal epithelia from 9 patients with esophageal adenocarcinoma, 8 patients lacking esophageal adenocarcinoma or Barrett's, and 6 patients with Barrett's esophagus alone. cDNA microarrays were performed, and pattern recognition in each of these subgroups was achieved using shrunken nearest centroid predictors. Results Our method accurately discriminated normal esophageal epithelia of 8/8 patients without esophageal adenocarcinoma or Barrett's esophagus and of 6/6 patients with Barrett's esophagus alone from normal esophageal epithelia of 9/9 patients with Barrett's esophagus and concomitant esophageal adenocarcinoma. Moreover, we identified genes differentially expressed between the above subgroups. Thus, based on their corresponding normal esophageal epithelia alone, our method accurately diagnosed patients who had concomitant esophageal adenocarcinoma. Conclusions These global gene expression patterns, along with individual genes culled from them, represent potential biomarkers for the early diagnosis of esophageal adenocarcinoma from normal esophageal epithelia. Genes discovered in normal esophagus that are differentially expressed in patients with vs. without esophageal adenocarcinoma merit further pursuit in molecular genetic, functional, and therapeutic interventional studies.


2008 ◽  
Vol 134 (4) ◽  
pp. A-150
Author(s):  
Daniel S. Oh ◽  
Steven R. DeMeester ◽  
Ryutaro Mori ◽  
Hidekazu Kuramochi ◽  
Jeffrey A. Hagen ◽  
...  

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