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Key elements involved in Epstein–Barr virus-associated gastric cancer and their network regulation

Overview of attention for article published in Cancer Cell International, September 2018
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Title
Key elements involved in Epstein–Barr virus-associated gastric cancer and their network regulation
Published in
Cancer Cell International, September 2018
DOI 10.1186/s12935-018-0637-5
Pubmed ID
Authors

Jing-jing Jing, Ze-yang Wang, Hao Li, Li-ping Sun, Yuan

Abstract

The molecular mechanism of Epstein-Barr virus (EBV)-associated gastric cancer (EBVaGC) remains elusive. A collection of molecular regulators including transcription factor and noncoding RNA (ncRNAs) may affect the carcinogenesis of EBVaGC by regulating the expression and function of key genes. In this study, integration of multi-level expression data and bioinformatics approach was used to identify key elements and their interactions involved in mechanism of EBVaGC and their network regulation. Data of the gene expression profiling data sets (GSE51575) was downloaded from GEO database. Differentially expressed genes between EBVaGC and normal samples were identified by GEO2R. Gene ontology and pathway enrichment analyses were performed using R packages Cluster profiler. STRING database was used to find interacting proteins between different genes. Transcription factors in differentially expressed genes were obtained from TF Checkpoint database. Using Cytoscape, we built transcription factor regulation network. miRNAs involved in the gene-interacting proteins and the miRNA-targeted lncRNA were predicted through miRWalk. Using ViRBase, EBV related miRNA regulation network was built. Overlapping genes and regulators of the above three networks were further identified, and the cross network was constructed using Cytoscape software. Moreover, the differential expressions of the target genes and transcription factors in the cross network were explored in different molecular subtypes of GC using cBioPortal. By histological verification, the expression of two main target genes in the cross network were further analyzed. A total of 104 genes showed differential expressions between EBVaGC and normal tissues, which were associated with digestion, G-protein coupled receptor binding, gastric acid secretion, etc. Pathway analysis showed that the differentially expressed genes were mainly enriched in gastric acid secretion and protein digestion and absorption. Using STRING dataset, a total of 54 proteins interacted with each other. Based on the transcription factor network, the hub transcription factors IRX3, NKX6-2, PTGER3 and SMAD5 were identified to regulate their target genes SST and GDF5, etc. After screening and matching in miRwalk datasets, a ceRNA network was established, in which the top five miRNAs were hsa-miR-4446-3p, hsa-miR-5787, hsa-miR-1915-3p, hsa-miR-335-3p and hsa-miR-6877-3p, and the top two lncRNAs were RP5-1039K5.19 and TP73-AS1. According to the EBV related miRNA regulation network, CXCL10 and SMAD5 were found to be regulated by EBV-miR-BART1-3p and EBV-mir-BART22, respectively. By overlapping the three networks, CXCL10, GDF5, PTGER3, SMAD5, miR-6877-3p, RP5-1039K5.19, TP73-AS1, EBV-miR-BART1-3p and EBV-mir-BART22 were found to be key elements of regulation mechanism of EBVaGC. CXCL10, GDF5, PTGER3 and SMAD5 were also differentially expressed among the four molecular subtypes of GC. The histological verification experiment showed differential expressions of the two main target genes GDF5 and CXCL10 between EBVaGC and non-tumor tissues as well as EBVnGC. In the current study, our results revealed key elements and their interactions involved in EBVaGC. Some hub transcription factors, miRNAs, lncRNAs and EBV related miRNAs were observed to regulate their target genes. Overlapping genes and regulators were observed in diverse regulation networks, such as CXCL10, GDF5, PTGER3, SMAD5, miR-6877-3p, RP5-1039K5.19, TP73-AS1, EBV-miR-BART1-3p and EBV-mir-BART22. Moreover, CXCL10, GDF5, PTGER3 and SMAD5 were also differentially expressed among the four molecular subtypes of GC. The histological verification experiment showed differential expressions of the two main target genes GDF5 and CXCL10 between EBVaGC and non-tumor tissues as well as EBVnGC. Therefore, the identified key elements and their network regulation may be specifically involved in EBVaGC mechanisms.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 18%
Other 3 14%
Student > Master 3 14%
Professor > Associate Professor 2 9%
Student > Ph. D. Student 1 5%
Other 2 9%
Unknown 7 32%
Readers by discipline Count As %
Medicine and Dentistry 5 23%
Agricultural and Biological Sciences 3 14%
Biochemistry, Genetics and Molecular Biology 2 9%
Pharmacology, Toxicology and Pharmaceutical Science 2 9%
Mathematics 1 5%
Other 2 9%
Unknown 7 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 September 2018.
All research outputs
#8,653,446
of 13,810,416 outputs
Outputs from Cancer Cell International
#193
of 602 outputs
Outputs of similar age
#161,716
of 269,474 outputs
Outputs of similar age from Cancer Cell International
#1
of 1 outputs
Altmetric has tracked 13,810,416 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 602 research outputs from this source. They receive a mean Attention Score of 2.5. This one has gotten more attention than average, scoring higher than 58% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 269,474 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them