Evidence map›Paper›PMID 37189697›Full record

ArticleBiomedicines2023

A Strategy for the Selection of RT-qPCR Reference Genes Based on Publicly Available Transcriptomic Datasets.

Alice Nevone, Francesca Lattarulo, Monica Russo, Giada Panno, Paolo Milani, Marco Basset, Maria Antonietta Avanzini, Giampaolo Merlini, Giovanni Palladini, Mario Nuvolone

Open access · goldAbstract read
In one paragraph

Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.0field-weighted citation impact, top 14% 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

10 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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

10 authors at 2 institutions in 1 country.

Alice NevoneDepartment of Molecular Medicine, University of Pavia, 27100 Pavia, Italy.ORCID 0000-0002-7724-1043
Francesca LattaruloDepartment of Molecular Medicine, University of Pavia, 27100 Pavia, Italy.
Monica RussoDepartment of Molecular Medicine, University of Pavia, 27100 Pavia, Italy.
Giada PannoDepartment of Molecular Medicine, University of Pavia, 27100 Pavia, Italy.
Paolo MilaniDepartment of Molecular Medicine, University of Pavia, 27100 Pavia, Italy.
Marco BassetDepartment of Molecular Medicine, University of Pavia, 27100 Pavia, Italy.ORCID 0000-0003-3829-0499
Maria Antonietta AvanziniPediatric Hematology Oncology, Cell Factory, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy.ORCID 0000-0002-6056-9018
Giampaolo MerliniDepartment of Molecular Medicine, University of Pavia, 27100 Pavia, Italy.ORCID 0000-0001-7680-3254
Giovanni PalladiniDepartment of Molecular Medicine, University of Pavia, 27100 Pavia, Italy.ORCID 0000-0001-5994-5138
Mario NuvoloneDepartment of Molecular Medicine, University of Pavia, 27100 Pavia, Italy.ORCID 0000-0001-8334-1684
University of Pavia · ITPoliclinico San Matteo Fondazione · IT

Funding

Cancer Research UK Accelerator Award Program
6 · The paper itself

Abstract

In the next-generation sequencing era, RT-qPCR is still widely employed to quantify levels of nucleic acids of interest due to its popularity, versatility, and limited costs. The measurement of transcriptional levels through RT-qPCR critically depends on reference genes used for normalization. Here, we devised a strategy to select appropriate reference genes for a specific clinical/experimental setting based on publicly available transcriptomic datasets and a pipeline for RT-qPCR assay design and validation. As a proof-of-principle, we applied this strategy to identify and validate reference genes for transcriptional studies of bone-marrow plasma cells from patients with AL amyloidosis. We performed a systematic review of published literature to compile a list of 163 candidate reference genes for RT-qPCR experiments employing human samples. Next, we interrogated the Gene Expression Omnibus to assess expression levels of these genes in published transcriptomic studies on bone-marrow plasma cells from patients with different plasma cell dyscrasias and identified the most stably expressed genes as candidate normalizing genes. Experimental validation on bone-marrow plasma cells showed the superiority of candidate reference genes identified through this strategy over commonly employed "housekeeping" genes. The strategy presented here may apply to other clinical and experimental settings for which publicly available transcriptomic datasets are available.

Indexed as

normalizationreference geneRT-qPCR

Identifiers

PMID37189697
PMCPMC10135859
OpenAlexW4362584392

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.