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Identification of reference genes for RT-qPCR data normalisation in aging studies.

ABSTRACT: Aging is associated with changes in gene expression levels that affect cellular functions and predispose to age-related diseases. The use of candidate genes whose expression remains stable during aging is required to correctly address the age-associated variations in expression levels. Reverse transcription quantitative-polymerase chain reaction (RT-qPCR) has become a powerful approach for sensitive gene expression analysis. Reliable RT-qPCR assays rely on the normalisation of the results to stable reference genes. Taken these data together, here we evaluated the expression stability of eight frequently used reference genes in three aging models: oncogene-induced senescence (OIS), in vitro and in vivo aging. Using NormFinder and geNorm algorithms, we identified that the most stable reference gene pairs were PUM1 and TBP in OIS, GUSB and PUM1 for in vitro aging and GUSB and OAZ1 for in vivo aging. To validate these candidates, we used them to normalise the expression data of CDKN1A, APOD and TFRC genes, whose expression is known to be affected during OIS, in vitro and in vivo aging. This study demonstrates that accurate normalisation of RT-qPCR data is crucial in aging research and provides a specific subset of stable reference genes for future aging studies.

SUBMITTER: Gonzalez-Bermudez L

PROVIDER: S-EPMC6764958 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Identification of reference genes for RT-qPCR data normalisation in aging studies.

González-Bermúdez Lourdes L Anglada Teresa T Genescà Anna A Martín Marta M Terradas Mariona M

Scientific reports 20190927 1

Aging is associated with changes in gene expression levels that affect cellular functions and predispose to age-related diseases. The use of candidate genes whose expression remains stable during aging is required to correctly address the age-associated variations in expression levels. Reverse transcription quantitative-polymerase chain reaction (RT-qPCR) has become a powerful approach for sensitive gene expression analysis. Reliable RT-qPCR assays rely on the normalisation of the results to sta ...[more]

PMID: 31562345

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Project description:Hypoxia is an adverse factor in cervical cancer, and hypoxia-related gene expression could be a powerful biomarker for identifying the aggressive hypoxic tumors. Reverse transcription quantitative PCR (RT-qPCR) is a valuable method for gene expression studies, but suitable reference genes for data normalization that are independent of hypoxia status and clinical parameters of cervical tumors are lacking. In the present work, we aimed to identify reference genes for RT-qPCR studies of hypoxia in squamous cervical cancer. From 422 candidate reference genes selected from the literature, we used Illumina array-based expression profiles to identify 182 genes not affected by hypoxia in cervical cancer, i.e. genes regulated by hypoxia in eight cervical cancer cell lines or correlating with the hypoxia-associated dynamic contrast-enhanced magnetic resonance imaging parameter ABrix in 42 patients, were excluded. Among the 182 genes, nine candidates (CHCHD1, GNB2L1, IPO8, LASP1, RPL27A, RPS12, SOD1, SRSF9, TMBIM6) that were not associated with tumor volume, stage, lymph node involvement or disease progression in array data of 150 patients, were selected for further testing by RT-qPCR. geNorm and NormFinder analyses of RT-qPCR data of 74 patients identified CHCHD1, SRSF9 and TMBIM6 as the optimal set of reference genes, with stable expression both overall and across patient subgroups with different hypoxia status (ABrix) and clinical parameters. The suitability of the three reference genes were validated in studies of the hypoxia-induced genes DDIT3, ERO1A, and STC2. After normalization, the RT-qPCR data of these genes showed a significant correlation with Illumina expression (P<0.001, n = 74) and ABrix (P<0.05, n = 32), and the STC2 data were associated with clinical outcome, in accordance with the Illumina data. Thus, CHCHD1, SRSF9 and TMBIM6 seem to be suitable reference genes for studying hypoxia-related gene expression in squamous cervical cancer samples by RT-qPCR. Moreover, STC2 is a promising prognostic hypoxia biomarker in cervical cancer.

Dataset Information

Identification of reference genes for RT-qPCR data normalisation in aging studies.

Publications

Identification of reference genes for RT-qPCR data normalisation in aging studies.

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