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Identification of potential biomarkers to differentially diagnose solid pseudopapillary tumors and pancreatic malignancies via a gene regulatory network

Overview of attention for article published in Journal of Translational Medicine, November 2015
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Title
Identification of potential biomarkers to differentially diagnose solid pseudopapillary tumors and pancreatic malignancies via a gene regulatory network
Published in
Journal of Translational Medicine, November 2015
DOI 10.1186/s12967-015-0718-3
Pubmed ID
Authors

Pengping Li, Yuebing Hu, Jiao Yi, Jie Li, Jie Yang, Jin Wang

Abstract

Solid pseudopapillary neoplasms (SPN) are pancreatic tumors with low malignant potential and good prognosis. However, differential diagnosis between SPN and pancreatic malignancies including pancreatic neuroendocrine tumor (PanNET) and ductal adenocarcinoma (PDAC) is difficult. This study tried to identify candidate biomarkers for the distinction between SPN and the two malignant pancreatic tumors by examining the gene regulatory network of SPN. The gene regulatory network for SPN was constructed by a co-expression model. Genes that have been reported to be correlated with SPN were used as the clues to hunt more SPN-related genes in the network according to a shortest path approach. By means of the K-nearest neighbor algorithm (KNN) classifier evaluated by the jackknife test, sets of genes to distinguish SPN and malignant pancreatic tumors were determined. We took a new strategy to identify candidate biomarkers for differentiating SPN from the two malignant pancreatic tumors PanNET and PDAC by analyzing shortest paths among SPN-related genes in the gene regulatory network. 43 new SPN-relevant genes were discovered, among which, we found hsa-miR-194 and hsa-miR-7 along with 7 transcription factors (TFs) such as SOX11, SMAD3 and SOX4 etc. could correctly differentiate SPN from PanNET, while hsa-miR-204 and 4 TFs such as SOX9, TCF7 and PPARD etc. were demonstrated as the potential markers for SPN versus PDAC. 14 genes were demonstrated to serve as the candidate biomarkers for distinguishing SPN from PanNET and PDAC when considering them as malignant pancreatic tumors together. This study provides new candidate genes related to SPN and the potential biomarkers to differentiate SPN from PanNET and PDAC, which may help to diagnose patients with SPN in clinical setting. Furthermore, candidate biomarkers such as SOX11 and hsa-miR-204 which could cause cell proliferation but inhibit invasion or metastasis may be of importance in understanding the molecular mechanism of pancreatic oncogenesis and could be possible therapeutic targets for malignant pancreatic tumors.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 17%
Researcher 4 17%
Student > Master 4 17%
Student > Doctoral Student 3 13%
Student > Bachelor 2 9%
Other 4 17%
Unknown 2 9%
Readers by discipline Count As %
Medicine and Dentistry 10 43%
Agricultural and Biological Sciences 4 17%
Computer Science 2 9%
Biochemistry, Genetics and Molecular Biology 2 9%
Engineering 2 9%
Other 1 4%
Unknown 2 9%
Attention Score in Context

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 25 September 2016.
All research outputs
#18,430,915
of 22,833,393 outputs
Outputs from Journal of Translational Medicine
#2,947
of 3,994 outputs
Outputs of similar age
#201,995
of 281,503 outputs
Outputs of similar age from Journal of Translational Medicine
#63
of 68 outputs
Altmetric has tracked 22,833,393 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,994 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.5. This one is in the 17th percentile – i.e., 17% of its peers scored the same or lower than it.
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 281,503 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 68 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.