Evidence map›Paper›PMID 27385437›Full record

ArticleScientific reports2016

Limits of Peripheral Blood Mononuclear Cells for Gene Expression-Based Biomarkers in Juvenile Idiopathic Arthritis.

Laiping Wong, Kaiyu Jiang, Yanmin Chen, Teresa Hennon, Lucy Holmes, Carol A Wallace, James N Jarvis

Abstract read
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
–field-weighted citation impact
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

19 citing papers in PubMed.

  1. Differential transcriptional and epigenetic signatures in CD4Journal of translational autoimmunity · 2026
    Article
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  15. Immunomics in Pediatric Rheumatic Diseases.Frontiers in medicine · 2019
    Review
  16. Article
  17. Review
  18. Article
  19. Article
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

7 authors.

Laiping WongDepartment of Pediatrics, University at Buffalo, Buffalo, NY, USA.
Kaiyu JiangDepartment of Pediatrics, University at Buffalo, Buffalo, NY, USA.
Yanmin ChenDepartment of Pediatrics, University at Buffalo, Buffalo, NY, USA.
Teresa HennonDepartment of Pediatrics, University at Buffalo, Buffalo, NY, USA.
Lucy HolmesDepartment of Pediatrics, University at Buffalo, Buffalo, NY, USA.
Carol A WallaceDepartment of Pediatrics, University of Washington, Seattle, WA, USA.
James N JarvisDepartment of Pediatrics, University at Buffalo, Buffalo, NY, USA.

Funding

Microarray-Based Biomarkers in Juvenile Idiopathic ArthritisR01AR060604 · NIAMS · UNIVERSITY OF OKLAHOMA HLTH SCIENCES CTR · PI JARVIS, JAMES N · 2011 to 2015
$2.6M
NIAMS NIH HHS R01 AR060604
6 · The paper itself

Abstract

Juvenile Idiopathic Arthritis (JIA) is one of the most common chronic disease conditions affecting children in the USA. As with many rheumatic diseases, there is growing interest in using genomic technologies to develop biomarkers for either diagnosis or to guide treatment ("personalized medicine"). Here, we explore the use of gene expression patterns in peripheral blood mononuclear cells (PBMC) as a first step approach to developing such biomarkers. Although PBMC carry many theoretical advantages for translational research, we have found that sample heterogeneity makes RNASeq on PBMC unsuitable as a first-step method for screening biomarker candidates in JIA. RNASeq studies of homogeneous cell populations are more likely to be useful and informative.

Indexed as

Genetic MarkersAdolescentArthritis, JuvenileBiomarkersChildFemaleGene Expression ProfilingGenetic HeterogeneityHigh-Throughput Nucleotide SequencingHumansLeukocytes, MononuclearMaleReproducibility of ResultsSequence Analysis, RNABiomarkersGenetic Markers

Identifiers

PMID27385437
PMCPMC4935846

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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.