Evidence map›Paper›PMID 27059462›Full record

ArticleBMC medical genomics2016

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

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

Open access · goldAbstract read
In one paragraph

Article in BMC medical genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
0.7field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 24 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Qing Yan XieDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.
Anthony AlmudevarDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.
Christa L Whitney-MillerDepartment of Pathology, University of Rochester Medical Center, Rochester, NY, USA.
Christopher T BarryDepartment of Surgery, University of Rochester Medical Center, Rochester, NY, USA.
Matthew N McCallDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA. mccallm@gmail.com.
University of Rochester Medical Center · US

Funding

Statistical Methods for Estimation of Gene Regulatory NetworksR00HG006853 · NHGRI · UNIVERSITY OF ROCHESTER · PI MCCALL, MATTHEW NICHOLSON · 2014 to 2016
$726k
Statistical Methods for Estimation of Gene Regulatory NetworksK99HG006853 · NHGRI · UNIVERSITY OF ROCHESTER · PI MCCALL, MATTHEW NICHOLSON · 2013 to 2013
$80k
NHGRI NIH HHS K99 HG006853NHGRI NIH HHS R00 HG006853NHGRI NIH HHS R00HG006853
6 · The paper itself

Abstract

backgroundLiver 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.

methodsMicroRNA 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.

resultsMicroRNA 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.

conclusionsMicroRNA 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.

Indexed as

Genetic HeterogeneityLiver TransplantationBiomarkers, TumorCarcinoma, HepatocellularCluster AnalysisDisease-Free SurvivalGene Expression ProfilingGene Expression Regulation, NeoplasticHumansKaplan-Meier EstimateLiver NeoplasmsMicroRNAsNeoplasm Recurrence, LocalBiomarkers, TumorMicroRNAsBiomarkerHepatocellular carcinoma (HCC)Liver transplantationmicroRNARecurrence

Identifiers

PMID27059462
PMCPMC4826548
OpenAlexW2314528514

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.