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New global analysis of the microRNA transcriptome of primary tumors and lymph node metastases of papillary thyroid cancer

Overview of attention for article published in BMC Genomics, October 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

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Title
New global analysis of the microRNA transcriptome of primary tumors and lymph node metastases of papillary thyroid cancer
Published in
BMC Genomics, October 2015
DOI 10.1186/s12864-015-2082-3
Pubmed ID
Authors

Manuel Saiselet, David Gacquer, Alex Spinette, Ligia Craciun, Myriam Decaussin-Petrucci, Guy Andry, Vincent Detours, Carine Maenhaut

Abstract

Papillary Thyroid Cancer (PTC) is the most prevalent type of endocrine cancer. Its incidence has rapidly increased in recent decades but little is known regarding its complete microRNA transcriptome (miRNome). In addition, there is a need for molecular biomarkers allowing improved PTC diagnosis. We performed small RNA deep-sequencing of 3 PTC, their matching normal tissues and lymph node metastases (LNM). We designed a new bioinformatics framework to handle each aspect of the miRNome: whole expression profiles, isomiRs distribution, non-templated additions distributions, RNA-editing or mutation. Results were validated experimentally by qRT-PCR on normal samples, tumors and LNM from 14 independent patients and in silico using the dataset from The Cancer Genome Atlas (small RNA deepsequencing of 59 normal samples, 495 PTC, and 8 LNM). We performed small RNA deep-sequencing of 3 PTC, their matching normal tissues and lymph node metastases (LNM). We designed a new bioinformatics framework to handle each aspect of the miRNome: whole expression profiles, isomiRs distribution, non-templated additions distributions, RNA-editing or mutation. Results were validated experimentally by qRT-PCR on normal samples, tumors and LNM from 14 independent patients and in silico using the dataset from The Cancer Genome Atlas (small RNA deep-sequencing of 59 normal samples, 495 PTC, and 8 LNM). We confirmed already described up-regulations of microRNAs in PTC, such as miR-146b-5p or miR-222-3p, but we also identified down-regulated microRNAs, such as miR-7-5p or miR-30c-2-3p. We showed that these down-regulations are linked to the tumorigenesis process of thyrocytes. We selected the 14 most down-regulated microRNAs in PTC and we showed that they are potential biomarkers of PTC samples. Nevertheless, they can distinguish histological classical variants and follicular variants of PTC in the TCGA dataset. In addition, 12 of the 14 down-regulated microRNAs are significantly less expressed in aggressive PTC compared to non-aggressive PTC. We showed that the associated aggressive expression profile is mainly due to the presence of the BRAF V600E mutation. In general, primary tumors and LNM presented similar microRNA expression profiles but specific variations like the down-regulation of miR-7-2-3p and miR-30c-2-3p in LNM were observed. Investigations of the 5p-to-3p arm expression ratios, non-templated additions or isomiRs distributions revealed no major implication in PTC tumorigenesis process or LNM appearance. Our results showed that down-regulated microRNAs can be used as new potential common biomarkers of PTC and to distinguish main subtypes of PTC. MicroRNA expressions can be linked to the development of LNM of PTC. The bioinformatics framework that we have developed can be used as a starting point for the global analysis of any microRNA deep-sequencing data in an unbiased way.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hong Kong 1 2%
United States 1 2%
Unknown 55 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 21%
Researcher 12 21%
Student > Master 8 14%
Other 5 9%
Professor 4 7%
Other 10 18%
Unknown 6 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 30%
Biochemistry, Genetics and Molecular Biology 13 23%
Medicine and Dentistry 13 23%
Nursing and Health Professions 1 2%
Unspecified 1 2%
Other 2 4%
Unknown 10 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 01 November 2015.
All research outputs
#5,457,355
of 22,830,751 outputs
Outputs from BMC Genomics
#2,164
of 10,655 outputs
Outputs of similar age
#67,762
of 283,225 outputs
Outputs of similar age from BMC Genomics
#59
of 355 outputs
Altmetric has tracked 22,830,751 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,655 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 79% 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 283,225 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 75% of its contemporaries.
We're also able to compare this research output to 355 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.