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A microRNA biomarker of hepatocellular carcinoma recurrence following liver transplantation accounting for within-patient heterogeneity

Overview of attention for article published in BMC Medical Genomics, April 2016
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  • Above-average Attention Score compared to outputs of the same age (64th percentile)
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6 tweeters

Citations

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14 Dimensions

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22 Mendeley
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Title
A microRNA biomarker of hepatocellular carcinoma recurrence following liver transplantation accounting for within-patient heterogeneity
Published in
BMC Medical Genomics, April 2016
DOI 10.1186/s12920-016-0179-4
Pubmed ID
Authors

Qing Yan Xie, Anthony Almudevar, Christa L. Whitney-Miller, Christopher T. Barry, Matthew N. McCall

Abstract

Liver cancer, of which hepatocellular carcinoma (HCC) is by far the most common type, is the second most deadly cancer (746,000 deaths in 2012). Currently, the only curative treatment for HCC is surgery to remove the malignancy (resection) or to remove the entire diseased liver followed by transplantation of healthy liver tissue. Given the shortage of healthy livers, it is crucial to provide transplants to patients that have the best chance of long-term survival. Currently, transplantation is determined via the Milan criteria-patients within Milan (single tumor < 5 cm or 2-3 tumors < 3 cm with no extrahepatic spread nor intrahepatic vascular invasion) are typically eligible for transplantation. However, combining microRNA expression profiling with the Milan criteria can improve prediction of recurrence. HCC often presents with multiple distinct tumor foci arising from local spread of a primary tumor or from the oncogenic predisposition of the diseased liver. Substantial genomic heterogeneity between tumor foci within a single patient has been reported; therefore, biomarker development must account for the possibility of highly heterogeneous genomic profiles from the same individual. MicroRNA profiling was performed on 180 HCC tumor samples from 89 patients who underwent liver transplantation at the University of Rochester Medical Center. The primary outcome was recurrence-free survival time, and patients were observed for 3 years post-transplantation. MicroRNA expression profiles were used to develop a biomarker that distinguishes HCC patients at greater risk of recurrence post-transplantation. Unsupervised clustering uncovered two distinct subgroups with vast differences in standard transplantation selection criteria and recurrence-free survival times. These subgroups were subsequently used to identify microRNAs strongly associated with HCC recurrence. Our results show that reduced expression of five specific microRNAs is significantly associated with HCC recurrence post-transplantation. MicroRNA profiling of distinct tumor foci, coupled with methods that address within-subject tumor heterogeneity, has the potential to significantly improve prediction of HCC recurrence post-transplantation. The development of a clinically applicable HCC biomarker would inform treatment options for patients and contribute to liver transplant selection criteria for practitioners.

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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 22 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 27%
Student > Master 4 18%
Student > Bachelor 3 14%
Student > Doctoral Student 1 5%
Professor 1 5%
Other 5 23%
Unknown 2 9%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 32%
Medicine and Dentistry 7 32%
Agricultural and Biological Sciences 2 9%
Mathematics 1 5%
Psychology 1 5%
Other 1 5%
Unknown 3 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 11 May 2016.
All research outputs
#2,936,322
of 7,684,314 outputs
Outputs from BMC Medical Genomics
#146
of 414 outputs
Outputs of similar age
#94,487
of 272,813 outputs
Outputs of similar age from BMC Medical Genomics
#7
of 16 outputs
Altmetric has tracked 7,684,314 research outputs across all sources so far. This one has received more attention than most of these and is in the 61st percentile.
So far Altmetric has tracked 414 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 62% 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 272,813 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 64% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.