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Harmful somatic amino acid substitutions affect key pathways in cancers

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

  • Above-average Attention Score compared to outputs of the same age (53rd percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

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Title
Harmful somatic amino acid substitutions affect key pathways in cancers
Published in
BMC Medical Genomics, August 2015
DOI 10.1186/s12920-015-0125-x
Pubmed ID
Authors

Abhishek Niroula, Mauno Vihinen

Abstract

Cancer is characterized by the accumulation of large numbers of genetic variations and alterations of multiple biological phenomena. Cancer genomics has largely focused on the identification of such genetic alterations and the genes containing them, known as 'cancer genes'. However, the non-functional somatic variations out-number functional variations and remain as a major challenge. Recurrent somatic variations are thought to be cancer drivers but they are present in only a small fraction of patients. We performed an extensive analysis of amino acid substitutions (AASs) from 6,861 cancer samples (whole genome or exome sequences) classified into 30 cancer types and performed pathway enrichment analysis. We also studied the overlap between the cancers based on proteins containing harmful AASs and pathways affected by them. We found that only a fraction of AASs (39.88 %) are harmful even in known cancer genes. In addition, we found that proteins containing harmful AASs in cancers are often centrally located in protein interaction networks. Based on the proteins containing harmful AASs, we identified significantly affected pathways in 28 cancer types and indicate that proteins containing harmful AASs can affect pathways despite the frequency of AASs in them. Our cross-cancer overlap analysis showed that it would be more beneficial to identify affected pathways in cancers rather than individual genes and variations. Pathways affected by harmful AASs reveal key processes involved in cancer development. Our approach filters out the putative benign AASs thus reducing the list of cancer variations allowing reliable identification of affected pathways. The pathways identified in individual cancer and overlap between cancer types open avenues for further experimental research and for developing targeted therapies and interventions.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Sweden 1 3%
Canada 1 3%
Unknown 29 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 25%
Researcher 4 13%
Professor 4 13%
Student > Master 3 9%
Student > Doctoral Student 2 6%
Other 2 6%
Unknown 9 28%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 28%
Agricultural and Biological Sciences 4 13%
Computer Science 2 6%
Engineering 2 6%
Medicine and Dentistry 1 3%
Other 1 3%
Unknown 13 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 09 November 2016.
All research outputs
#14,536,007
of 25,373,627 outputs
Outputs from BMC Medical Genomics
#888
of 2,444 outputs
Outputs of similar age
#127,165
of 277,605 outputs
Outputs of similar age from BMC Medical Genomics
#19
of 61 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,444 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 63% 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 277,605 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 53% of its contemporaries.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.