Evidence map›Paper›PMID 24858468›Full record

ReviewAnalytical and bioanalytical chemistry2014

Considerations for accurate gene expression measurement by reverse transcription quantitative PCR when analysing clinical samples.

Rebecca Sanders, Deborah J Mason, Carole A Foy, Jim F Huggett

Abstract readReview
In one paragraph

Review in Analytical and bioanalytical chemistry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing 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

45 citing papers in PubMed.

  1. Expression of AR-V7 and ARvClinical cancer research : an official journal of the American Association for Cancer Research · 2019
    Trial
  2. Rapid detection ofMicrobiology spectrum · 2026
    Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Review
  9. When Two-Fold Is Not Enough: Quantifying Uncertainty in Low-Copy qPCR.International journal of molecular sciences · 2025
    Article
  10. MIQE 2.0 and the Urgent Need to Rethink qPCR Standards.International journal of molecular sciences · 2025
    Article
  11. Review
  12. Article
  13. Article
  14. Comparison of Cefotaxime-ResistantAntibiotics (Basel, Switzerland) · 2023
    Article
  15. Oncology letters · 2021
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

4 authors.

Rebecca SandersMolecular & Cell Biology, LGC, Queens Road, Teddington, TW11 0LY, UK, rebecca.sanders@lgcgroup.com.
Deborah J Mason
Carole A Foy
Jim F Huggett

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reverse transcription quantitative PCR is an established, simple and effective method for RNA measurement. However, technical standardisation challenges combined with frequent insufficient experimental detail render replication of many published findings challenging. Consequently, without adequate consideration of experimental standardisation, such findings may be sufficient for a given publication but cannot be translated to wider clinical application. This article builds on earlier standardisation work and the MIQE guidelines, discussing processes that need consideration for accurate, reproducible analysis when dealing with patient samples. By applying considerations common to the science of measurement (metrology), one can maximise the impact of gene expression studies, increasing the likelihood of their translation to clinical tools.

Indexed as

AnimalsGene Expression ProfilingGenetic TestingHumansReverse Transcriptase Polymerase Chain ReactionReverse TranscriptionRNARNA

Identifiers

PMID24858468
PMCPMC4182594

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.