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Transcriptional profiling of long non-coding RNAs and novel transcribed regions across a diverse panel of archived human cancers

Overview of attention for article published in Genome Biology, August 2012
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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 (95th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

blogs
2 blogs
twitter
15 X users
patent
1 patent

Citations

dimensions_citation
229 Dimensions

Readers on

mendeley
264 Mendeley
citeulike
5 CiteULike
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Title
Transcriptional profiling of long non-coding RNAs and novel transcribed regions across a diverse panel of archived human cancers
Published in
Genome Biology, August 2012
DOI 10.1186/gb-2012-13-8-r75
Pubmed ID
Authors

Alayne L Brunner, Andrew H Beck, Badreddin Edris, Robert T Sweeney, Shirley X Zhu, Rui Li, Kelli Montgomery, Sushama Varma, Thea Gilks, Xiangqian Guo, Joseph W Foley, Daniela M Witten, Craig P Giacomini, Ryan A Flynn, Jonathan R Pollack, Robert Tibshirani, Howard Y Chang, Matt van de Rijn, Robert B West

Abstract

BACKGROUND: Molecular characterization of tumors has been critical for identifying important genes in cancer biology and for improving tumor classification and diagnosis. Long non-coding RNAs, as a new, relatively unstudied class of transcripts, provide a rich opportunity to identify both functional drivers and cancer-type-specific biomarkers. However, despite the potential importance of long non-coding RNAs to the cancer field, no comprehensive survey of long non-coding RNA expression across various cancers has been reported. RESULTS: We performed a sequencing-based transcriptional survey of both known long non-coding RNAs and novel intergenic transcripts across a panel of 64 archival tumor samples comprising 17 diagnostic subtypes of adenocarcinomas, squamous cell carcinomas and sarcomas. We identified hundreds of transcripts from among the known 1,065 long non-coding RNAs surveyed that showed variability in transcript levels between the tumor types and are therefore potential biomarker candidates. We discovered 1,071 novel intergenic transcribed regions and demonstrate that these show similar patterns of variability between tumor types. We found that many of these differentially expressed cancer transcripts are also expressed in normal tissues. One such novel transcript specifically expressed in breast tissue was further evaluated using RNA in situ hybridization on a panel of breast tumors. It was shown to correlate with low tumor grade and estrogen receptor expression, thereby representing a potentially important new breast cancer biomarker. CONCLUSIONS: This study provides the first large survey of long non-coding RNA expression within a panel of solid cancers and also identifies a number of novel transcribed regions differentially expressed across distinct cancer types that represent candidate biomarkers for future research.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 264 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 3%
United Kingdom 3 1%
Brazil 2 <1%
India 2 <1%
France 1 <1%
Germany 1 <1%
China 1 <1%
Sweden 1 <1%
Unknown 246 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 70 27%
Researcher 67 25%
Student > Master 32 12%
Student > Bachelor 17 6%
Professor > Associate Professor 14 5%
Other 39 15%
Unknown 25 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 121 46%
Biochemistry, Genetics and Molecular Biology 73 28%
Medicine and Dentistry 21 8%
Computer Science 4 2%
Immunology and Microbiology 3 1%
Other 13 5%
Unknown 29 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 17 October 2023.
All research outputs
#1,466,516
of 25,374,647 outputs
Outputs from Genome Biology
#1,168
of 4,467 outputs
Outputs of similar age
#8,781
of 187,808 outputs
Outputs of similar age from Genome Biology
#11
of 56 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 73% 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 187,808 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.