Published online Jan 14, 2019. doi: 10.3748/wjg.v25.i2.233
Peer-review started: October 14, 2018
First decision: November 29, 2018
Revised: December 10, 2018
Accepted: December 15, 2018
Article in press: December 15, 2018
Published online: January 14, 2019
Barrett’s esophagus (BE) is the only known precursor of esophageal adenocarcinoma (EAC), and patients with BE have a persistent and excessive risk of EAC over time. As an aggressive disease with high mortality, the overall 5-year survival rate of EAC is less than 20%. Therefore, early detection of BE and EAC could significantly improve the 5-year survival rate of EAC. Due to the limitations of endoscopic surveillance and the lack of clinical risk stratification strategies, more attention has been paid to improving endoscopic techniques and to the discovery of molecular biomarkers for EAC and BE.
The exact molecular mechanisms of EAC and BE are controversial, so a top priority is to explore their basic molecular mechanisms and pathways. The discovery of molecular changes during their progression may identify biomarkers that can be used for early diagnosis and prognostic prediction. Advances in biological ‘omics’-based techniques have been used to explore the changes in gene expression during disease progression, including gene chips, serial analysis of gene expression, and proteomics. These new technologies could identify a series of abnormally expressed genes that may be new targets for EAC and BE, especially as more researchers focus on this topic. Recently, gene profiling analysis has been used to explore the molecular changes in many disorders, including EAC and BE. However there are no consistent conclusions among these different studies, and many of the potential genes and pathways have not been thoroughly investigated.
In this study, we focused on the three conditions [normal esophagus (NE), BE, and EAC] to provide a broad view of their underlying mechanisms, and explored changes in the transcriptome to address the complexity of EAC and BE while evaluating potential biomarkers. After using various analytical approaches that are based on several bioinformatics tools, the results showed that COL1A1 was associated with EAC, and that MMP1 was predicted to be associated with BE. These two respective genes were evaluated as potent biomarkers for EAC and BE, which could play certain roles in the pathogenesis and might be utilized for clinical application.
The gene expression profile datasets of EAC and BE were retrieved from the Gene Expression Omnibus database. The expression profiling analysis was conducted using the R/Bioconductor project. The AffyPLM package and the gcRMA algorithm were used for data preprocessing, and the Limma package was used to identify the differentially expressed genes (DEGs) between any two groups. Genes with a fold change of at least 1.5 and a P-value < 0.05 were considered DEGs. The transcription factor (TF)-gene regulation modes of 36 cancer-related TF families were obtained from the Transcriptional Regulatory Element Database. We then integrated the DEGs with the TF-gene regulation modes and constructed the dys-regulated TF-DEGs networks. The trans-regulatory networks were visualized with the help of the Cytoscape software. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) Bioinformatics Resources was used to provide functional annotations. Statistical analyses were performed using the SPSS software and the GraphPad Prism software. The receiver operating characteristic curve (ROC) analysis was conducted using the Med-Calc statistical software.
In this study, we found that the number of up-regulated DEGs was larger than that of down-regulated DEGs when comparing EAC vs. NE and BE vs. NE. And the majority of the DEGs in trans-regulatory networks were up-regulated. The intersection of these potential DEGs displayed the same direction of changes in expression when comparing the DEGs in the GSE26886 dataset to the DEGs in trans-regulatory networks in the GSE1420 dataset. The ROC analysis and DAVID annotation indicated that COL1A1 and MMP1 could be potent biomarkers for EAC and BE, respectively, since they participate in the majority of the enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) terms that are important for inflammation and cancer.
Our data indicated that there is an association between COL1A1 and EAC after trans-regulatory network prediction, ROC evaluation, and DAVID annotation. Similarly, an association between MMP1 and BE was also predicted. This study provides potential genes which could be applied to clinical practice, and a novel perspective on the molecular mechanisms involved in the underlying development of BE and EAC.
The discovery of molecular changes during disease progression may identify biomarkers that might be used for early diagnosis and prognostic prediction. And this study also provide a potential perspective on the molecular mechanisms to the clinical gastroenterologists. In order to confirm the value of these potent genes, the validation groups will be introduced in our future study, which might provide more detailed information.