ArticleCells2020
Validation of Reference Genes for Gene Expression Studies by RT-qPCR in HepaRG Cells during Toxicity Testing and Disease Modelling.
Article in Cells, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Reference Gene Validation for Quantitative PCR Analysis in 2D and 3D AML12 Hepatocyte Models.Biomedicines · 2026Article
- MiRNA sequencing of platelet and exosome revealed platelet miR-199b-3p as a potential biomarker in lung adenocarcinoma.Frontiers in immunology · 2025Article
- Housekeeping gene expression variability in differentiating and non-differentiating 3T3-L1 cells.Adipocyte · 2023Article
- A Strategy for the Selection of RT-qPCR Reference Genes Based on Publicly Available Transcriptomic Datasets.Biomedicines · 2023Article
- Selection and Validation of Reference Genes for Pan-Cancer in Platelets Based on RNA-Sequence Data.Frontiers in genetics · 2022Article
- Deacetylase Plus Bromodomain Inhibition Downregulates ERCC2 and Suppresses the Growth of Metastatic Colon Cancer Cells.Cancers · 2021Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
Abstract
Gene expression analysis by quantitative real-time polymerase chain reaction (RT-qPCR) is routinely used in biomedical studies. The reproducibility and reliability of the data fundamentally depends on experimental design and data interpretation. Despite the wide application of this assay, there is significant variation in the validation process of gene expression data from research laboratories. Since the validity of results depends on appropriate normalisation, it is crucial to select appropriate reference gene(s), where transcription of the selected gene is unaffected by experimental setting. In this study we have applied geNorm technology to investigate the transcription of 12 'housekeeping' genes for use in the normalisation of RT-qPCR data acquired using a widely accepted HepaRG hepatic cell line in studies examining models of pre-clinical drug testing. geNorm data identified a number of genes unaffected by specific drug treatments and showed that different genes remained invariant in response to different drug treatments, whereas the transcription of 'classical' reference genes such as GAPDH (glyceralde- hyde-3-phosphate dehydrogenase) was altered by drug treatment. Comparing data normalised using the reference genes identified by geNorm with normalisation using classical housekeeping genes demonstrated substantial differences in the final results. In light of cell therapy application, RT-qPCR analyses has to be carefully evaluated to accurately interpret data obtained from dynamic cellular models undergoing sequential stages of phenotypic change.
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Registered trials
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