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Comprehensive molecular biomarker identification in breast cancer brain metastases

Overview of attention for article published in Journal of Translational Medicine, December 2017
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (68th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

twitter
8 tweeters

Citations

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66 Dimensions

Readers on

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99 Mendeley
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Title
Comprehensive molecular biomarker identification in breast cancer brain metastases
Published in
Journal of Translational Medicine, December 2017
DOI 10.1186/s12967-017-1370-x
Pubmed ID
Authors

Hans-Juergen Schulten, Mohammed Bangash, Sajjad Karim, Ashraf Dallol, Deema Hussein, Adnan Merdad, Fatma K. Al-Thoubaity, Jaudah Al-Maghrabi, Awatif Jamal, Fahad Al-Ghamdi, Hani Choudhry, Saleh S. Baeesa, Adeel G. Chaudhary, Mohammed H. Al-Qahtani

Abstract

Breast cancer brain metastases (BCBM) develop in about 20-30% of breast cancer (BC) patients. BCBM are associated with dismal prognosis not at least due to lack of valuable molecular therapeutic targets. The aim of the study was to identify new molecular biomarkers and targets in BCBM by using complementary state-of-the-art techniques. We compared array expression profiles of three BCBM with 16 non-brain metastatic BC and 16 primary brain tumors (prBT) using a false discovery rate (FDR) p < 0.05 and fold change (FC) > 2. Biofunctional analysis was conducted on the differentially expressed probe sets. High-density arrays were employed to detect copy number variations (CNVs) and whole exome sequencing (WES) with paired-end reads of 150 bp was utilized to detect gene mutations in the three BCBM. The top 370 probe sets that were differentially expressed between BCBM and both BC and prBT were in the majority comparably overexpressed in BCBM and included, e.g. the coding genes BCL3, BNIP3, BNIP3P1, BRIP1, CASP14, CDC25A, DMBT1, IDH2, E2F1, MYCN, RAD51, RAD54L, and VDR. A number of small nucleolar RNAs (snoRNAs) were comparably overexpressed in BCBM and included SNORA1, SNORA2A, SNORA9, SNORA10, SNORA22, SNORA24, SNORA30, SNORA37, SNORA38, SNORA52, SNORA71A, SNORA71B, SNORA71C, SNORD13P2, SNORD15A, SNORD34, SNORD35A, SNORD41, SNORD53, and SCARNA22. The top canonical pathway was entitled, role of BRCA1 in DNA damage response. Network analysis revealed key nodes as Akt, ERK1/2, NFkB, and Ras in a predicted activation stage. Downregulated genes in a data set that was shared between BCBM and prBT comprised, e.g. BC cell line invasion markers JUN, MMP3, TFF1, and HAS2. Important cancer genes affected by CNVs included TP53, BRCA1, BRCA2, ERBB2, IDH1, and IDH2. WES detected numerous mutations, some of which affecting BC associated genes as CDH1, HEPACAM, and LOXHD1. Using complementary molecular genetic techniques, this study identified shared and unshared molecular events in three highly aberrant BCBM emphasizing the challenge to detect new molecular biomarkers and targets with translational implications. Among new findings with the capacity to gain clinical relevance is the detection of overexpressed snoRNAs known to regulate some critical cellular functions as ribosome biogenesis.

Twitter Demographics

The data shown below were collected from the profiles of 8 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 99 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 99 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 18%
Student > Bachelor 17 17%
Researcher 7 7%
Student > Master 7 7%
Professor > Associate Professor 5 5%
Other 19 19%
Unknown 26 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 24 24%
Medicine and Dentistry 13 13%
Agricultural and Biological Sciences 10 10%
Engineering 4 4%
Computer Science 3 3%
Other 13 13%
Unknown 32 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 14 September 2019.
All research outputs
#6,245,300
of 22,518,353 outputs
Outputs from Journal of Translational Medicine
#952
of 3,928 outputs
Outputs of similar age
#138,956
of 448,391 outputs
Outputs of similar age from Journal of Translational Medicine
#56
of 261 outputs
Altmetric has tracked 22,518,353 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 3,928 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one has done well, scoring higher than 75% 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 448,391 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 68% of its contemporaries.
We're also able to compare this research output to 261 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.