↓ Skip to main content

The prognostic potential of alternative transcript isoforms across human tumors

Overview of attention for article published in Genome Medicine, August 2016
Altmetric Badge

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 (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

twitter
20 tweeters

Citations

dimensions_citation
33 Dimensions

Readers on

mendeley
76 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
The prognostic potential of alternative transcript isoforms across human tumors
Published in
Genome Medicine, August 2016
DOI 10.1186/s13073-016-0339-3
Pubmed ID
Authors

Juan L. Trincado, E. Sebestyén, A. Pagés, E. Eyras

Abstract

Phenotypic changes during cancer progression are associated with alterations in gene expression, which can be exploited to build molecular signatures for tumor stage identification and prognosis. However, it is not yet known whether the relative abundance of transcript isoforms may be informative for clinical stage and survival. Using information theory and machine learning methods, we integrated RNA sequencing and clinical data from The Cancer Genome Atlas project to perform the first systematic analysis of the prognostic potential of transcript isoforms in 12 solid tumors to build new signatures for stage and prognosis. This study was also performed in breast tumors according to estrogen receptor (ER) status and melanoma tumors with proliferative and invasive phenotypes. Transcript isoform signatures accurately separate early from late-stage groups and metastatic from non-metastatic tumors, and are predictive of the survival of patients with undetermined lymph node invasion or metastatic status. These signatures show similar, and sometimes better, accuracies compared with known gene expression signatures in retrospective data and are largely independent of gene expression changes. Furthermore, we show frequent transcript isoform changes in breast tumors according to ER status, and in melanoma tumors according to the invasive or proliferative phenotype, and derive accurate predictive models of stage and survival within each patient subgroup. Our analyses reveal new signatures based on transcript isoform abundances that characterize tumor phenotypes and their progression independently of gene expression. Transcript isoform signatures appear especially relevant to determine lymph node invasion and metastasis and may potentially contribute towards current strategies of precision cancer medicine.

Twitter Demographics

The data shown below were collected from the profiles of 20 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 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
France 1 1%
Unknown 74 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 15 20%
Student > Ph. D. Student 13 17%
Researcher 12 16%
Student > Bachelor 6 8%
Student > Doctoral Student 5 7%
Other 15 20%
Unknown 10 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 28 37%
Medicine and Dentistry 13 17%
Agricultural and Biological Sciences 12 16%
Computer Science 5 7%
Immunology and Microbiology 3 4%
Other 2 3%
Unknown 13 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 2020.
All research outputs
#2,308,602
of 17,663,872 outputs
Outputs from Genome Medicine
#537
of 1,182 outputs
Outputs of similar age
#45,504
of 272,533 outputs
Outputs of similar age from Genome Medicine
#2
of 8 outputs
Altmetric has tracked 17,663,872 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,182 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.3. This one has gotten more attention than average, scoring higher than 54% 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 272,533 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 83% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.