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MicroRNA-Gene Association As a Prognostic Biomarker in Cancer Exposes Disease Mechanisms

Overview of attention for article published in PLoS Computational Biology, November 2013
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  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Average Attention Score compared to outputs of the same age and source

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1 Google+ user

Citations

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15 Dimensions

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46 Mendeley
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1 CiteULike
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Title
MicroRNA-Gene Association As a Prognostic Biomarker in Cancer Exposes Disease Mechanisms
Published in
PLoS Computational Biology, November 2013
DOI 10.1371/journal.pcbi.1003351
Pubmed ID
Authors

Rotem Ben-Hamo, Sol Efroni

Abstract

The transcriptional networks that regulate gene expression and modifications to this network are at the core of the cancer phenotype. MicroRNAs, a well-studied species of small non-coding RNA molecules, have been shown to have a central role in regulating gene expression as part of this transcriptional network. Further, microRNA deregulation is associated with cancer development and with tumor progression. Glioblastoma Multiform (GBM) is the most common, aggressive and malignant primary tumor of the brain and is associated with one of the worst 5-year survival rates among all human cancers. To study the transcriptional network and its modifications in GBM, we utilized gene expression, microRNA sequencing, whole genome sequencing and clinical data from hundreds of patients from different datasets. Using these data and a novel microRNA-gene association approach we introduce, we have identified unique microRNAs and their associated genes. This unique behavior is composed of the ability of the quantifiable association of the microRNA and the gene expression levels, which we show stratify patients into clinical subgroups of high statistical significance. Importantly, this stratification goes unobserved by other methods and is not affiliated by other subsets or phenotypes within the data. To investigate the robustness of the introduced approach, we demonstrate, in unrelated datasets, robustness of findings. Among the set of identified microRNA-gene associations, we closely study the example of MAF and hsa-miR-330-3p, and show how their co-behavior stratifies patients into prognosis clinical groups and how whole genome sequences tells us more about a specific genomic variation as a possible basis for patient variances. We argue that these identified associations may indicate previously unexplored specific disease control mechanisms and may be used as basis for further study and for possible therapeutic intervention.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 9%
United Kingdom 1 2%
Belgium 1 2%
Mexico 1 2%
Spain 1 2%
Denmark 1 2%
Unknown 37 80%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 20%
Researcher 9 20%
Student > Bachelor 8 17%
Student > Master 6 13%
Professor > Associate Professor 4 9%
Other 6 13%
Unknown 4 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 46%
Medicine and Dentistry 6 13%
Computer Science 5 11%
Engineering 3 7%
Mathematics 1 2%
Other 5 11%
Unknown 5 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 06 December 2013.
All research outputs
#7,355,930
of 25,373,627 outputs
Outputs from PLoS Computational Biology
#4,994
of 8,960 outputs
Outputs of similar age
#79,359
of 315,413 outputs
Outputs of similar age from PLoS Computational Biology
#81
of 146 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 43rd percentile – i.e., 43% 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 315,413 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.