↓ Skip to main content

Centrosome associated genes pattern for risk sub-stratification in multiple myeloma

Overview of attention for article published in Journal of Translational Medicine, May 2016
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
18 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
Centrosome associated genes pattern for risk sub-stratification in multiple myeloma
Published in
Journal of Translational Medicine, May 2016
DOI 10.1186/s12967-016-0906-9
Pubmed ID
Authors

Fedor Kryukov, Pavel Nemec, Lenka Radova, Elena Kryukova, Samuel Okubote, Jiri Minarik, Zdena Stefanikova, Ludek Pour, Roman Hajek

Abstract

The genome of multiple myeloma (MM) cells is extremely unstable, characterized by a complex combination of structure and numerical abnormalities. It seems that there are several "myeloma subgroups" which differ in expression profile, clinical manifestations, prognoses and treatment response. In our previous work, the list of 35 candidate genes with a known role in carcinogenesis and associated with centrosome structure/function was used as a display of molecular heterogeneity with an impact in myeloma pathogenesis. The current study was devoted to establish a risk stratification model based on the aforementioned candidate genes. A total of 151 patients were included in this study. CD138+ cells were separated by magnetic-activated cell sorting (MACS). Gene expression profiling (GEP) and Interphase FISH with cytoplasmic immunoglobulin light chain staining (cIg FISH) were performed on plasma cells (PCs). All statistical analyses were performed using freeware R and its additional packages. Training and validation cohort includes 73 and 78 patients, respectively. We have finally established a model that includes 12 selected genes (centrosome associated gene pattern, CAGP) which appears to be an independent prognostic factor for MM stratification. We have shown that the new CAGP model can sub-stratify prognosis in patients without TP53 loss as well as in IMWG high risk patients' group. We assume that newly established risk stratification model complements the current prognostic panel used in multiple myeloma and refines the classification of patients in relation to the disease risks. This approach can be used independently as well as in combination with other factors.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 6%
Unknown 17 94%

Demographic breakdown

Readers by professional status Count As %
Student > Postgraduate 3 17%
Professor > Associate Professor 3 17%
Student > Ph. D. Student 3 17%
Student > Master 2 11%
Researcher 2 11%
Other 3 17%
Unknown 2 11%
Readers by discipline Count As %
Medicine and Dentistry 8 44%
Biochemistry, Genetics and Molecular Biology 3 17%
Agricultural and Biological Sciences 2 11%
Physics and Astronomy 1 6%
Business, Management and Accounting 1 6%
Other 1 6%
Unknown 2 11%

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 21 October 2017.
All research outputs
#3,173,096
of 12,022,940 outputs
Outputs from Journal of Translational Medicine
#508
of 2,331 outputs
Outputs of similar age
#83,547
of 278,607 outputs
Outputs of similar age from Journal of Translational Medicine
#23
of 112 outputs
Altmetric has tracked 12,022,940 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 2,331 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has done well, scoring higher than 77% 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 278,607 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 69% of its contemporaries.
We're also able to compare this research output to 112 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.