Evidence map›Paper›PMID 30036517›Full record

ArticleThe American journal of pathology2018

Distinct Gene Expression Profiles Define Anaplastic Grade in Retinoblastoma.

Lauren E Hudson, Pia Mendoza, William H Hudson, Alison Ziesel, G Baker Hubbard, Jill Wells, Bhakti Dwivedi, Jeanne Kowalski, Sandra Seby, Viren Patel and 3 more

Open access · bronzeAbstract read
In one paragraph

Article in The American journal of pathology, 2018. 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
2.6field-weighted citation impact, top 11% 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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  14. Biomarkers in retinoblastoma.International journal of ophthalmology · 2020
    Review
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

13 authors at 2 institutions in 1 country.

Lauren E HudsonDepartment of Ophthalmology, Rollins School of Public Health, Emory University, Atlanta, Georgia.
Pia MendozaDepartment of Ophthalmology, Rollins School of Public Health, Emory University, Atlanta, Georgia; Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, Georgia.
William H HudsonDepartment of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia.
Alison ZieselDepartment of Ophthalmology, Rollins School of Public Health, Emory University, Atlanta, Georgia.
G Baker HubbardDepartment of Ophthalmology, Rollins School of Public Health, Emory University, Atlanta, Georgia.
Jill WellsDepartment of Ophthalmology, Rollins School of Public Health, Emory University, Atlanta, Georgia.
Bhakti DwivediWinship Cancer Institute, Rollins School of Public Health, Emory University, Atlanta, Georgia.
Jeanne KowalskiWinship Cancer Institute, Rollins School of Public Health, Emory University, Atlanta, Georgia; Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia.
Sandra SebyWinship Cancer Institute, Rollins School of Public Health, Emory University, Atlanta, Georgia.
Viren PatelEmory University Integrated Computational Core, Atlanta, Georgia.
Eldon GeisertDepartment of Ophthalmology, Rollins School of Public Health, Emory University, Atlanta, Georgia.
Charles SpechtDepartment of Pathology, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania.
Hans E GrossniklausDepartment of Ophthalmology, Rollins School of Public Health, Emory University, Atlanta, Georgia; Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, Georgia. Electronic address: ophtheg@emory.edu.
Emory University · USPenn State Milton S. Hershey Medical Center · US

Funding

P30-Core Grant for Vision Research Core CP30EY006360 · NEI · EMORY UNIVERSITY · PI BOATRIGHT, JEFFREY H · 1986 to 2025
$15.3M
NEI NIH HHS P30 EY006360
6 · The paper itself

Abstract

Morbidity and mortality associated with retinoblastoma have decreased drastically in recent decades, in large part owing to better prediction of high-risk disease and appropriate treatment stratification. High-risk histopathologic features and severe anaplasia both predict the need for more aggressive treatment; however, not all centers are able to assess tumor samples easily for the degree of anaplasia. Instead, identification of genetic signatures that are able to distinguish among anaplastic grades and thus predict high- versus low-risk retinoblastoma would facilitate appropriate risk stratification in a wider patient population. A better understanding of genes dysregulated in anaplasia also would yield valuable insights into pathways underlying the development of more severe retinoblastoma. Here, we present the histopathologic and gene expression analysis of 28 retinoblastoma cases using microarray analysis. Tumors of differing anaplastic grade show clear differential gene expression, with significant dysregulation of unique genes and pathways in severe anaplasia. Photoreceptor and nucleoporin expression in particular are identified as highly dysregulated in severe anaplasia and suggest particular cellular processes contributing to the development of increased retinoblastoma severity. A limited set of highly differentially expressed genes also are able to predict severe anaplasia accurately in our data set. Together, these data contribute to the understanding of the development of anaplasia and facilitate the identification of genetic markers of high-risk retinoblastoma.

Indexed as

AnaplasiaChild, PreschoolFemaleGene ExpressionGene Expression ProfilingGenes, RetinoblastomaGenetic MarkersHumansInfantMaleNeoplasm GradingRetinal NeoplasmsRetinoblastomaRisk FactorsGenetic Markers

Identifiers

PMID30036517
PMCPMC6168968
OpenAlexW2883052277

What OpenQuestion holds

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Read underepoch 390

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.