Evidence map›Paper›PMID 20579843›Full record

ReviewJournal of clinical epidemiology2010

Expectations, validity, and reality in gene expression profiling.

Kyoungmi Kim, Stanislav O Zakharkin, David B Allison

Abstract readReview
In one paragraph

Review in Journal of clinical epidemiology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 1 pooled it
3.5field-weighted citation impact, top 7% 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

28 citing papers in PubMed, 1 synthesis or guideline pooled it, 67 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
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  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Finding Correlations Between mRNA and Protein Levels inFrontiers in cellular and infection microbiology · 2022
    Review
  12. Review
  13. Article
  14. Article
  15. Article
  16. Random gene sets in predicting survival of patients with hepatocellular carcinoma.Journal of molecular medicine (Berlin, Germany) · 2019
    Article
  17. Article
  18. Article
  19. Article
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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

3 authors at 2 institutions in 1 country.

Kyoungmi KimDivision of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA 95616, USA. kmkim@ucdavis.edu
Stanislav O Zakharkin
David B Allison
University of Alabama at Birmingham · USUniversity of California System · US

Funding

Why is the prevalence of obesity so high in U.S. Southern States? Regional predictors of BMI and obesity treatment response.P30DK056336 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI James O Hill · 2000 to 2026
$31.9M
TRANSLOCATION OF p66Shc: KINETICS, FUNCTION, APOPTOSISP01AG025532 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI KIM, KYOUNGMI · 2007 to 2017
$12.1M
NIA NIH HHS P01 AG025532NIA NIH HHS P01AG025532NIDDK NIH HHS P30 DK056336NIDDK NIH HHS P30DK056336
6 · The paper itself

Abstract

objectiveTo provide a critical overview of gene expression profiling methodology and discuss areas of future development.

resultsGene expression profiling has been used extensively in biological research and has resulted in significant advances in the understanding of the molecular mechanisms of complex disorders, including cancer, heart disease, and metabolic disorders. However, translating this technology into genomic medicine for use in diagnosis and prognosis faces many challenges. In addition, gene expression profile analysis is frequently controversial, because its conclusions often lack reproducibility and claims of effective dissemination into translational medicine have, in some cases, been remarkably unjustified. In the last decade, a large number of methodological and technical solutions have been offered to overcome the challenges. STUDY DESIGN AND

settingWe consider the strengths, limitations, and appropriate applications of gene expression profiling techniques, with particular reference to the clinical relevance.

conclusionSome studies have demonstrated the ability and clinical utility of gene expression profiling for use as diagnostic, prognostic, and predictive molecular markers. The challenges of gene expression profiling lie with the standardization of analytic approaches and the evaluation of the clinical merit in broader heterogeneous populations by prospective clinical trials.

Indexed as

BiomarkersClinical Trials as TopicDatabases, GeneticGene Expression ProfilingHumansInformation Storage and RetrievalMicroarray AnalysisNeoplasmsReproducibility of ResultsTranscription, GeneticBiomarkers

Identifiers

PMID20579843
PMCPMC2910173
OpenAlexW1987024849

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.