Genome-Wide Analysis of Barrett's Adenocarcinoma. A First Step Towards Identifying Patients at Risk and Developing Therapeutic Paths

Dai, Yiyang; Wang, Qing; González-López, Adrián; Anders, Mario; Malfertheiner, Peter; Kemmner, Wolfgang

doi:10.1016/j.tranon.2017.10.003

Cited by 10 publications

(10 citation statements)

References 46 publications

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“…Multiple studies indicated that MMP1 participates in the progression of various cancers and is associated with an increased cancer risk[32,33]. Up-regulation of MMP1 has already been observed in EAC and BE samples[34], but has not been fully investigated. In our study, the ROC analysis found that the sensitivity, specificity, and AUC were relatively high in both datasets.…”

Section: Discussionmentioning

confidence: 99%

Biomarker identification and trans-regulatory network analyses in esophageal adenocarcinoma and Barrett’s esophagus

Guo

Wang

et al. 2019

WJG

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BACKGROUNDEsophageal adenocarcinoma (EAC) is an aggressive disease with high mortality and an overall 5-year survival rate of less than 20%. Barrett’s esophagus (BE) is the only known precursor of EAC, and patients with BE have a persistent and excessive risk of EAC over time. Individuals with BE are up to 30-125 times more likely to develop EAC than the general population. Thus, early detection of EAC and BE 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, molecular biomarkers should be considered and thoroughly investigated.AIMTo explore the transcriptome changes in the progression from normal esophagus (NE) to BE and EAC.METHODSTwo datasets from the Gene Expression Omnibus (GEO) in NCBI Database (https://www.ncbi.nlm.nih.gov/geo/) were retrieved and used as a training and a test dataset separately, since NE, BE, and EAC samples were included and the sample sizes were adequate. This study identified differentially expressed genes (DEGs) using the R/Bioconductor project and constructed trans-regulatory networks based on the Transcriptional Regulatory Element Database and Cytoscape software. Enrichment of Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) terms was identified using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) Bioinformatics Resources. The diagnostic potential of certain DEGs was assessed in both datasets.RESULTSIn the GSE1420 dataset, the number of up-regulated DEGs was larger than that of down-regulated DEGs when comparing EAC vs NE and BE vs NE. Among these DEGs, five differentially expressed transcription factors (DETFs) displayed the same trend in expression across all the comparison groups. Of these five DETFs, E2F3, FOXA2, and HOXB7 were up-regulated, while PAX9 and TFAP2C were down-regulated. Additionally, 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 above. The receiver operating characteristic curve analysis was performed for both datasets and found that TIMP1 and COL1A1 could discriminate EAC from NE tissue, while REG1A, MMP1, and CA2 could distinguish BE from NE tissue. 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 KEGG and GO terms that are important for inflammation and cancer.CONCLUSIONAfter the construction and analyses of the trans-regulatory networks in EAC and BE, the results indicate that COL1A1 and MMP1 could be potential biomarkers for EAC and BE, respectively.

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Section: Discussionmentioning

confidence: 99%

Biomarker identification and trans-regulatory network analyses in esophageal adenocarcinoma and Barrett’s esophagus

Guo

Wang

et al. 2019

WJG

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“…The EDC, located on chromosome 1q21.3, contains clustered multigene families of genes associated with cornified envelope formation in stratified squamous epithelia, such as the S100 and the small proline-rich region (SPRR) genes 34 . Among the overlapping downregulated genes in both cell models, ANXA9 is also associated with differentiating keratinocytes 35 , and EVPL , SCEL , and KLK7 are cornified envelope genes 36 – 38 . EMP1 and SERPINB13 downregulation is associated with increased disease severity in gastric cancer ( EMP1 ) and head and neck squamous cell carcinoma (SCC) ( SERPINB13 ) 39 , 40 .…”

Section: Resultsmentioning

confidence: 99%

“…These experiments also provide mechanistic support for the notion that HOXA13 expression may offer an explanation as to why BE is prone to progression to EAC. HOXA13 mediates down-regulation of the EDC and many EDC and cornified envelope genes are progressively down-regulated in the BE to EAC cascade 36 , 41 . In BE, EAC, and esophageal SCC, loss of heterozygosity (LOH) of the EDC is common 42 – 44 .…”

Section: Resultsmentioning

confidence: 99%

HOXA13 in etiology and oncogenic potential of Barrett’s esophagus

et al. 2021

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Barrett’s esophagus in gastrointestinal reflux patients constitutes a columnar epithelium with distal characteristics, prone to progress to esophageal adenocarcinoma. HOX genes are known mediators of position-dependent morphology. Here we show HOX collinearity in the adult gut while Barrett’s esophagus shows high HOXA13 expression in stem cells and their progeny. HOXA13 overexpression appears sufficient to explain both the phenotype (through downregulation of the epidermal differentiation complex) and the oncogenic potential of Barrett’s esophagus. Intriguingly, employing a mouse model that contains a reporter coupled to the HOXA13 promotor we identify single HOXA13-positive cells distally from the physiological esophagus, which is mirrored in human physiology, but increased in Barrett’s esophagus. Additionally, we observe that HOXA13 expression confers a competitive advantage to cells. We thus propose that Barrett’s esophagus and associated esophageal adenocarcinoma is the consequence of expansion of this gastro-esophageal HOXA13-expressing compartment following epithelial injury.

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“…The presence of activating mutations within EGFR in esophageal adenocarcinomas defines a previously unrecognized subset of gastrointestinal tumors in which EGFR signaling may play an important, biological role 42 . According to an analysis of genes strongly up-regulated in both esophageal adenocarcinoma and Barrett’s esophagus, REG4 might be of particular interest as an early marker for esophageal adenocarcinoma 43 .…”

Section: Resultsmentioning

confidence: 99%

Cancer Characteristic Gene Selection via Sample Learning Based on Deep Sparse Filtering

Liu

Cheng

Wang

et al. 2018

Sci Rep

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Identification of characteristic genes associated with specific biological processes of different cancers could provide insights into the underlying cancer genetics and cancer prognostic assessment. It is of critical importance to select such characteristic genes effectively. In this paper, a novel unsupervised characteristic gene selection method based on sample learning and sparse filtering, Sample Learning based on Deep Sparse Filtering (SLDSF), is proposed. With sample learning, the proposed SLDSF can better represent the gene expression level by the transformed sample space. Most unsupervised characteristic gene selection methods did not consider deep structures, while a multilayer structure may learn more meaningful representations than a single layer, therefore deep sparse filtering is investigated here to implement sample learning in the proposed SLDSF. Experimental studies on several microarray and RNA-Seq datasets demonstrate that the proposed SLDSF is more effective than several representative characteristic gene selection methods (e.g., RGNMF, GNMF, RPCA and PMD) for selecting cancer characteristic genes.

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Genome-Wide Analysis of Barrett's Adenocarcinoma. A First Step Towards Identifying Patients at Risk and Developing Therapeutic Paths

Cited by 10 publications

References 46 publications

Biomarker identification and trans-regulatory network analyses in esophageal adenocarcinoma and Barrett’s esophagus

Biomarker identification and trans-regulatory network analyses in esophageal adenocarcinoma and Barrett’s esophagus

HOXA13 in etiology and oncogenic potential of Barrett’s esophagus

Cancer Characteristic Gene Selection via Sample Learning Based on Deep Sparse Filtering

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