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Integrative analysis of survival-associated gene sets in breast cancer

Overview of attention for article published in BMC Medical Genomics, March 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)

Mentioned by

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13 tweeters

Citations

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17 Dimensions

Readers on

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26 Mendeley
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Title
Integrative analysis of survival-associated gene sets in breast cancer
Published in
BMC Medical Genomics, March 2015
DOI 10.1186/s12920-015-0086-0
Pubmed ID
Authors

Frederick S Varn, Matthew H Ung, Shao Ke Lou, Chao Cheng

Abstract

Patient gene expression information has recently become a clinical feature used to evaluate breast cancer prognosis. The emergence of prognostic gene sets that take advantage of these data has led to a rich library of information that can be used to characterize the molecular nature of a patient's cancer. Identifying robust gene sets that are consistently predictive of a patient's clinical outcome has become one of the main challenges in the field. We inputted our previously established BASE algorithm with patient gene expression data and gene sets from MSigDB to develop the gene set activity score (GSAS), a metric that quantitatively assesses a gene set's activity level in a given patient. We utilized this metric, along with patient time-to-event data, to perform survival analyses to identify the gene sets that were significantly correlated with patient survival. We then performed cross-dataset analyses to identify robust prognostic gene sets and to classify patients by metastasis status. Additionally, we created a gene set network based on component gene overlap to explore the relationship between gene sets derived from MSigDB. We developed a novel gene set based on this network's topology and applied the GSAS metric to characterize its role in patient survival. Using the GSAS metric, we identified 120 gene sets that were significantly associated with patient survival in all datasets tested. The gene overlap network analysis yielded a novel gene set enriched in genes shared by the robustly predictive gene sets. This gene set was highly correlated to patient survival when used alone. Most interestingly, removal of the genes in this gene set from the gene pool on MSigDB resulted in a large reduction in the number of predictive gene sets, suggesting a prominent role for these genes in breast cancer progression. The GSAS metric provided a useful medium by which we systematically investigated how gene sets from MSigDB relate to breast cancer patient survival. We used this metric to identify predictive gene sets and to construct a novel gene set containing genes heavily involved in cancer progression.

Twitter Demographics

The data shown below were collected from the profiles of 13 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 4%
Taiwan 1 4%
Unknown 24 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 31%
Researcher 6 23%
Student > Master 4 15%
Student > Doctoral Student 2 8%
Professor 1 4%
Other 2 8%
Unknown 3 12%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 27%
Medicine and Dentistry 6 23%
Agricultural and Biological Sciences 4 15%
Computer Science 3 12%
Neuroscience 1 4%
Other 1 4%
Unknown 4 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 30 January 2016.
All research outputs
#2,336,828
of 14,395,088 outputs
Outputs from BMC Medical Genomics
#126
of 760 outputs
Outputs of similar age
#40,148
of 217,279 outputs
Outputs of similar age from BMC Medical Genomics
#2
of 4 outputs
Altmetric has tracked 14,395,088 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 760 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 83% 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 217,279 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.