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Development of prognostic signatures for intermediate-risk papillary thyroid cancer

Overview of attention for article published in BMC Cancer, September 2016
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Title
Development of prognostic signatures for intermediate-risk papillary thyroid cancer
Published in
BMC Cancer, September 2016
DOI 10.1186/s12885-016-2771-6
Pubmed ID
Authors

Kevin Brennan, Christopher Holsinger, Chrysoula Dosiou, John B. Sunwoo, Haruko Akatsu, Robert Haile, Olivier Gevaert

Abstract

The incidence of Papillary thyroid carcinoma (PTC), the most common type of thyroid malignancy, has risen rapidly worldwide. PTC usually has an excellent prognosis. However, the rising incidence of PTC, due at least partially to widespread use of neck imaging studies with increased detection of small cancers, has created a clinical issue of overdiagnosis, and consequential overtreatment. We investigated how molecular data can be used to develop a prognostics signature for PTC. The Cancer Genome Atlas (TCGA) recently reported on the genomic landscape of a large cohort of PTC cases. In order to decrease unnecessary morbidity associated with over diagnosing PTC patient with good prognosis, we used TCGA data to develop a gene expression signature to distinguish between patients with good and poor prognosis. We selected a set of clinical phenotypes to define an 'extreme poor' prognosis group and an 'extreme good' prognosis group and developed a gene signature that characterized these. We discovered a gene expression signature that distinguished the extreme good from extreme poor prognosis patients. Next, we applied this signature to the remaining intermediate risk patients, and show that they can be classified in clinically meaningful risk groups, characterized by established prognostic disease phenotypes. Analysis of the genes in the signature shows many known and novel genes involved in PTC prognosis. This work demonstrates that using a selection of clinical phenotypes and treatment variables, it is possible to develop a statistically useful and biologically meaningful gene signature of PTC prognosis, which may be developed as a biomarker to help prevent overdiagnosis.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 4%
Unknown 48 96%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 9 18%
Researcher 7 14%
Other 4 8%
Student > Master 4 8%
Student > Ph. D. Student 4 8%
Other 8 16%
Unknown 14 28%
Readers by discipline Count As %
Medicine and Dentistry 14 28%
Biochemistry, Genetics and Molecular Biology 6 12%
Nursing and Health Professions 3 6%
Agricultural and Biological Sciences 3 6%
Philosophy 1 2%
Other 8 16%
Unknown 15 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 September 2016.
All research outputs
#20,341,859
of 22,888,307 outputs
Outputs from BMC Cancer
#6,507
of 8,326 outputs
Outputs of similar age
#278,673
of 321,166 outputs
Outputs of similar age from BMC Cancer
#133
of 187 outputs
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