TUG1: a potential endogenous reference gene for long noncoding RNA quantification in blood-based studies

Long noncoding RNAs (lncRNAs) are promising biomarkers, but their accurate quantification by qPCR requires stable endogenous controls. In the present study, we aimed to identify suitable reference lncRNAs for normalization in whole-blood samples. We profiled the expression of 84 lncRNAs and eight commonly used mRNA reference genes by RT-qPCR in samples from 182 individuals. Transcript stability was assessed using three widely applied algorithms: geNorm, NormFinder and BestKeeper. Twenty-nine lncRNAs met predefined expression criteria: consistent detection across all samples, a maximum quantification cycle (Cq) below 33 and a median Cq below 30. Among these, TUG1 consistently ranked as the most stable candidate across all algorithms. FGD5-AS1 and ZFAS1 were also selected based on high stability rankings. We next evaluated the effectiveness of different normalization strategies, comparing them to the gold-standard method, i.e. mean-centering. We included both the selected lncRNAs and eight commonly used mRNA reference genes. TUG1 alone achieved a reduction in expression variability comparable to mean-centering and superior to all other tested strategies, including combinations of multiple reference lncRNAs and mRNAs. Moreover, TUG1 expression showed a minimal association with clinical variables and outcomes. TUG1 was consistently identified among the lncRNAs with the highest expression levels and lowest variability across four independent external RNA-seq datasets. Our findings identify TUG1 as the most stable candidate in this cohort and suggest that it may represent a promising and cost-effective endogenous control for lncRNA quantification in whole-blood studies.

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Journal
Biomarker Research
Published
2025-12-30
DOI
https://doi.org/10.1186/s40364-025-00871-2
Primary Topic
Cancer-related molecular mechanisms research
Type
article
Field-Weighted Citation Impact
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article

TUG1: a potential endogenous reference gene for long noncoding RNA quantification in blood-based studies

Sally Santisteve, Iván D. Benítez, María C. García‐Hidalgo, Anna Moncusí-Moix et al.
Biomarker Research
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article

TUG1: a potential endogenous reference gene for long noncoding RNA quantification in blood-based studies

Sally Santisteve, Iván D. Benítez, María C. García‐Hidalgo, Anna Moncusí-Moix, Jessica González, Anna Sánchez-Cucó, Manel Perez-Pons, Ferran Barbé, Marta Molinero, David de Gonzalo-Calvo, Anna Vila, Carlos Rodríguez-Muñoz
article en

Abstract

Long noncoding RNAs (lncRNAs) are promising biomarkers, but their accurate quantification by qPCR requires stable endogenous controls. In the present study, we aimed to identify suitable reference lncRNAs for normalization in whole-blood samples. We profiled the expression of 84 lncRNAs and eight commonly used mRNA reference genes by RT-qPCR in samples from 182 individuals. Transcript stability was assessed using three widely applied algorithms: geNorm, NormFinder and BestKeeper. Twenty-nine lncRNAs met predefined expression criteria: consistent detection across all samples, a maximum quantification cycle (Cq) below 33 and a median Cq below 30. Among these, TUG1 consistently ranked as the most stable candidate across all algorithms. FGD5-AS1 and ZFAS1 were also selected based on high stability rankings. We next evaluated the effectiveness of different normalization strategies, comparing them to the gold-standard method, i.e. mean-centering. We included both the selected lncRNAs and eight commonly used mRNA reference genes. TUG1 alone achieved a reduction in expression variability comparable to mean-centering and superior to all other tested strategies, including combinations of multiple reference lncRNAs and mRNAs. Moreover, TUG1 expression showed a minimal association with clinical variables and outcomes. TUG1 was consistently identified among the lncRNAs with the highest expression levels and lowest variability across four independent external RNA-seq datasets. Our findings identify TUG1 as the most stable candidate in this cohort and suggest that it may represent a promising and cost-effective endogenous control for lncRNA quantification in whole-blood studies.

Biomarker ResearchVol. 13(1)
Instituto de Salud Carlos III (ES), Hospital Universitari Arnau de Vilanova (ES), Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (ES), Instituto de Investigación Biomédica de Lleida (ES)
Gilead Sciences, Fundación Eugenio Rodríguez Pascual, European Commission, Generalitat de Catalunya, Institució Catalana de Recerca i Estudis Avançats, Universitat de Lleida, Agència de Gestió d'Ajuts Universitaris i de Recerca, Instituto de Salud Carlos III
Openalex Percentile: Top 33%
Cancer-related molecular mechanisms research
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