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Network Modeling Identifies Molecular Functions Targeted by miR-204 to Suppress Head and Neck Tumor Metastasis

Overview of attention for article published in PLoS Computational Biology, April 2010
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

news
1 news outlet
twitter
1 X user
patent
4 patents

Readers on

mendeley
125 Mendeley
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3 CiteULike
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Title
Network Modeling Identifies Molecular Functions Targeted by miR-204 to Suppress Head and Neck Tumor Metastasis
Published in
PLoS Computational Biology, April 2010
DOI 10.1371/journal.pcbi.1000730
Pubmed ID
Authors

Younghee Lee, Xinan Yang, Yong Huang, Hanli Fan, Qingbei Zhang, Youngfei Wu, Jianrong Li, Rifat Hasina, Chao Cheng, Mark W. Lingen, Mark B. Gerstein, Ralph R. Weichselbaum, H. Rosie Xing, Yves A. Lussier

Abstract

Due to the large number of putative microRNA gene targets predicted by sequence-alignment databases and the relative low accuracy of such predictions which are conducted independently of biological context by design, systematic experimental identification and validation of every functional microRNA target is currently challenging. Consequently, biological studies have yet to identify, on a genome scale, key regulatory networks perturbed by altered microRNA functions in the context of cancer. In this report, we demonstrate for the first time how phenotypic knowledge of inheritable cancer traits and of risk factor loci can be utilized jointly with gene expression analysis to efficiently prioritize deregulated microRNAs for biological characterization. Using this approach we characterize miR-204 as a tumor suppressor microRNA and uncover previously unknown connections between microRNA regulation, network topology, and expression dynamics. Specifically, we validate 18 gene targets of miR-204 that show elevated mRNA expression and are enriched in biological processes associated with tumor progression in squamous cell carcinoma of the head and neck (HNSCC). We further demonstrate the enrichment of bottleneckness, a key molecular network topology, among miR-204 gene targets. Restoration of miR-204 function in HNSCC cell lines inhibits the expression of its functionally related gene targets, leads to the reduced adhesion, migration and invasion in vitro and attenuates experimental lung metastasis in vivo. As importantly, our investigation also provides experimental evidence linking the function of microRNAs that are located in the cancer-associated genomic regions (CAGRs) to the observed predisposition to human cancers. Specifically, we show miR-204 may serve as a tumor suppressor gene at the 9q21.1-22.3 CAGR locus, a well established risk factor locus in head and neck cancers for which tumor suppressor genes have not been identified. This new strategy that integrates expression profiling, genetics and novel computational biology approaches provides for improved efficiency in characterization and modeling of microRNA functions in cancer as compared to the state of art and is applicable to the investigation of microRNA functions in other biological processes and diseases.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 125 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 4%
Denmark 3 2%
Germany 2 2%
Malaysia 1 <1%
United Kingdom 1 <1%
Portugal 1 <1%
Brazil 1 <1%
Luxembourg 1 <1%
Unknown 110 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 35 28%
Student > Ph. D. Student 31 25%
Professor > Associate Professor 15 12%
Other 8 6%
Professor 7 6%
Other 19 15%
Unknown 10 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 59 47%
Medicine and Dentistry 22 18%
Biochemistry, Genetics and Molecular Biology 19 15%
Computer Science 8 6%
Mathematics 1 <1%
Other 5 4%
Unknown 11 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 19 October 2016.
All research outputs
#1,893,819
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#1,672
of 8,964 outputs
Outputs of similar age
#6,569
of 103,584 outputs
Outputs of similar age from PLoS Computational Biology
#11
of 55 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done well, scoring higher than 81% 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 103,584 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 55 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.