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Clinical implications of quantitative real-time RT-PCR analysis of hTERT gene expression in human gliomas.

ABSTRACT: The presence of telomerase activity in a glioma may be a predictor of its malignant potential. Activation of telomerase is regulated at the transcriptional level of the human telomerase reverse transcriptase (hTERT). Here, we evaluated whether the amount of hTERT mRNA provides a molecular marker of glioma malignancy that would have clinical utility. We used a real-time RT-PCR to assess the number of hTERT transcripts in primary tumour samples derived from 70 glioma patients. Results were standardised by quantifying the number of ABL transcripts as internal control and expressed as hTERT/ABL ratio. The percentage of patients with detectable hTERT mRNA markedly increased with enhanced malignancy: low-grade gliomas expressed hTERT in one out of 14 cases (7.1%), anaplastic gliomas in four out of 13 cases (30.8%) and glioblastoma multiforme (GBM) tumours in 30 out of 43 cases (69.8%). The mean hTERT/ABL ratio was significantly higher in GBMs than in non-GBMs. Subdividing hTERT/ABL ratios as low (< pr = 25%) and high (>25%), we found that the overall survival among hTERT-positive GBMs was significantly worse in high hTERT expressors than in low hTERT expressors (P=0.0082). We conclude that the amount of hTERT mRNA may represent a diagnostic and prognostic indicator for GBM patients.

SUBMITTER: Tchirkov A

PROVIDER: S-EPMC2377176 | biostudies-other | 2003 Feb

REPOSITORIES: biostudies-other

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Project description:BackgroundPseudorabies virus (PRV), a neurotropic herpesvirus of pigs, serves as an excellent model system with which to investigate the herpesvirus life cycle both in cultured cells and in vivo. Real-time RT-PCR is a very sensitive, accurate and reproducible technique that can be used to detect very small amounts of RNA molecules, and it can therefore be applied for analysis of the expression of herpesvirus genes from the very early period of infection.ResultsIn this study, we have developed and applied a quantitative reverse transcriptase-based real-time PCR technique in order to profile transcription from the whole genome of PRV after lytic infection in porcine kidney cells. We calculated the relative expression ratios in a novel way, which allowed us to compare different PRV genes with respect to their expression dynamics, and to divide the PRV genes into distinct kinetic classes. This is the first publication on the whole-genome analysis of the gene expression of an alpha-herpesvirus by qRT2-PCR. We additionally established the kinetic properties of uncharacterized PRV genes and revised or confirmed data on PRV genes earlier examined by traditional methods such as Northern blot analysis. Our investigations revealed that genes with the same expression properties form clusters on the PRV genome: nested overlapping genes belong in the same kinetic class, while most convergent genes belong in different kinetic classes. Further, we detected inverse relationships as concerns the expressions of EP0 and IE180 mRNAs and their antisense partners.ConclusionMost (if not all) PRV genes begin to be expressed from the onset of viral expression. No sharp boundary was found between the groups of early and late genes classified on the basis of their requirement for viral DNA synthesis. The expressions of the PRV genes were analyzed, categorized and compared by qRT2-PCR assay, with the average of the minimum cycle threshold used as a control for the calculation of a particular R value. In principle, this new calculation technique is applicable for the analysis of gene expression in all temporally changing genetic systems.

Project description:BackgroundReal-time RT-PCR is the recommended method for quantitative gene expression analysis. A compulsory step is the selection of good reference genes for normalization. A few genes often referred to as HouseKeeping Genes (HSK), such as ACT1, RDN18 or PDA1 are among the most commonly used, as their expression is assumed to remain unchanged over a wide range of conditions. Since this assumption is very unlikely, a geometric averaging of multiple, carefully selected internal control genes is now strongly recommended for normalization to avoid this problem of expression variation of single reference genes. The aim of this work was to search for a set of reference genes for reliable gene expression analysis in Saccharomyces cerevisiae.ResultsFrom public microarray datasets, we selected potential reference genes whose expression remained apparently invariable during long-term growth on glucose. Using the algorithm geNorm, ALG9, TAF10, TFC1 and UBC6 turned out to be genes whose expression remained stable, independent of the growth conditions and the strain backgrounds tested in this study. We then showed that the geometric averaging of any subset of three genes among the six most stable genes resulted in very similar normalized data, which contrasted with inconsistent results among various biological samples when the normalization was performed with ACT1. Normalization with multiple selected genes was therefore applied to transcriptional analysis of genes involved in glycogen metabolism. We determined an induction ratio of 100-fold for GPH1 and 20-fold for GSY2 between the exponential phase and the diauxic shift on glucose. There was no induction of these two genes at this transition phase on galactose, although in both cases, the kinetics of glycogen accumulation was similar. In contrast, SGA1 expression was independent of the carbon source and increased by 3-fold in stationary phase.ConclusionIn this work, we provided a set of genes that are suitable reference genes for quantitative gene expression analysis by real-time RT-PCR in yeast biological samples covering a large panel of physiological states. In contrast, we invalidated and discourage the use of ACT1 as well as other commonly used reference genes (PDA1, TDH3, RDN18, etc) as internal controls for quantitative gene expression analysis in yeast.

Project description:BackgroundReal-time quantitative reverse transcription PCR (RT-qPCR) data needs to be normalized for its proper interpretation. Housekeeping genes are routinely employed for this purpose, but their expression level cannot be assumed to remain constant under all possible experimental conditions. Thus, a systematic validation of reference genes is required to ensure proper normalization. For soybean, only a small number of validated reference genes are available to date.ResultsA systematic comparison of 14 potential reference genes for soybean is presented. These included seven commonly used (ACT2, ACT11, TUB4, TUA5, CYP, UBQ10, EF1b) and seven new candidates (SKIP16, MTP, PEPKR1, HDC, TIP41, UKN1, UKN2). Expression stability was examined by RT-qPCR across 116 biological samples, representing tissues at various developmental stages, varied photoperiodic treatments, and a range of soybean cultivars. Expression of all 14 genes was variable to some extent, but that of SKIP16, UKN1 and UKN2 was overall the most stable. A combination of ACT11, UKN1 and UKN2 would be appropriate as a reference panel for normalizing gene expression data among different tissues, whereas the combination SKIP16, UKN1 and MTP was most suitable for developmental stages. ACT11, TUA5 and TIP41 were the most stably expressed when the photoperiod was altered, and TIP41, UKN1 and UKN2 when the light quality was changed. For six different cultivars in long day (LD) and short day (SD), their expression stability did not vary significantly with ACT11, UKN2 and TUB4 being the most stable genes. The relative gene expression level of GmFTL3, an ortholog of Arabidopsis FT (FLOWERING LOCUS T) was detected to validate the reference genes selected in this study.ConclusionNone of the candidate reference genes was uniformly expressed across all experimental conditions, and the most suitable reference genes are conditional-, tissue-specific-, developmental-, and cultivar-dependent. Most of the new reference genes performed better than the conventional housekeeping genes. These results should guide the selection of reference genes for gene expression studies in soybean.

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Clinical implications of quantitative real-time RT-PCR analysis of hTERT gene expression in human gliomas.

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