<Emphasis Type="Italic">RPN1</Emphasis>, a new reference gene for quantitative data normalization in lung and kidney cancer

Quantitative methods of gene expression analysis in tumors require accurate data normalization, which allows comparison of different specimens with unknown mRNA/cDNA concentrations. For this purpose, reference genes with stable expression are used (e.g., GAPDH, ACTB, HPRT1, or TBP). The problem of choosing proper reference genes is still a topical issue, because well-known reference genes can be unsuitable for certain cancer types and their inappropriate use without additional testing can lead to wrong conclusions. A recently developed bioinformatical approach was employed to identify a new potential reference gene for lung and kidney tumors, RPN1, located on the long arm of chromosome 3. The method employed the mining of the dbEST and Oncomine databases and functional analysis of genes. RPN1 was selected from approximately 1500 candidate housekeeping genes. Using comparative genomic hybridization with NotI microarrays, we found no methylation, deletions, and/or amplifications in the RPN1-containing locus in 56 nonsmall cell lung and 42 clear cell renal cell cancer specimens. Real-time PCR showed that variation of RPN1 mRNA levels in nonsmall cell lung cancer and clear-cell renal cancer was low and comparable to that of the known reference genes GAPDH and GUSB, respectively. Expression levels of two hyalouronidase genes, HYAL1 and HYAL2, were assessed using the suggested references gene pairs (RPN1-GAPDH for lung cancer and RPN1-GUSB for kidney cancer), and these combinations were shown to produce accurate and reproducible data. These results suggest that RPN1 is a new, promising reference gene for quantitative data normalization in gene expression studies for lung and kidney cancers.     

Selection of suitable reference genes for quantitative real-time PCR in trabecular meshwork cells under oxidative stress

Oxidative stress-induced dysfunction in trabecular meshwork (TM) cells is considered a major alteration that can lead to glaucoma. Hydrogen peroxide (H₂O₂) is the most widely used agent for inducing oxidation in TM cells in vitro. Quantitative real-time PCR (qPCR) is an important method for studying alterations in gene expression, and suitable (i.e. invariant) reference genes must be defined to normalize expression levels. In this study, eight common reference genes, i.e. PRS18, ACTB, B2M, GAPDH, PPIA, HPRT1, YWHAZ, and TBP, were evaluated for use in studies of H₂O₂-induced dysfunction in TM cells. Three established algorithms, geNorm, NormFinder, and BestKeeper, were used to analyze the reference genes. ACTB expression was least affected by H₂O₂ treatment in TM cells, and the combination of PPIA and HPRT1 was the most suitable gene pair for normalization. GAPDH and TBP were the most unstable genes and accordingly should be avoided in experiments with TM cells. These results provide a foundation for analyses of the mechanisms underlying glaucoma, and emphasize the importance of selecting suitable reference genes for qPCR studies.     

Evaluation of reference genes for real‐time PCR quantification of gene expression in the Australian sheep blowfly,Lucilia cuprina

The Australian sheep blowfly, Lucilia cuprina (Wiedemann) (Diptera: Calliphoridae), is an important pest of sheep in Australia and other parts of the world. However, the paucity of information on many fundamental molecular aspects of this species limits the ability to exploit functional genomics techniques for the discovery of new drug targets for its control. The present study aimed to facilitate gene expression studies in this species by identifying the most suitable reference genes for normalization of mRNA expression data. Quantitative real‐time polymerase chain reaction (PCR) was performed with 11 genes across RNA samples from eggs, L1, L3, pupae and adult life stages, and two normalization programs, Normfinder and geNorm, were then applied to the data. The results showed an ideal set of genes (18S rRNA, 28S rRNA, GST1, β‐tubulin and RPLPO) for data normalization across all life stages. The most suitable reference genes for studies within specific life stages were also identified. GAPDH was shown to be a poor reference gene. The reference gene recommendations in this study will be of use to laboratories investigating gene expression in L. cuprina and related blowfly species     

Selection of Reference Genes for Gene Expression Studies Related to Intramuscular Fat Deposition in Capra hircus Skeletal Muscle

The identification of suitable reference genes is critical for obtaining reliable results from gene expression studies using quantitative real-time PCR (qPCR) because the expression of reference genes may vary considerably under different experimental conditions. In most cases, however, commonly used reference genes are employed in data normalization without proper validation, which may lead to incorrect data interpretation. Here, we aim to select a set of optimal reference genes for the accurate normalization of gene expression associated with intramuscular fat (IMF) deposition during development. In the present study, eight reference genes (PPIB, HMBS, RPLP0, B2M, YWHAZ, 18S, GAPDH and ACTB) were evaluated by three different algorithms (geNorm, NormFinder and BestKeeper) in two types of muscle tissues (longissimus dorsi muscle and biceps femoris muscle) across different developmental stages. All three algorithms gave similar results. PPIB and HMBS were identified as the most stable reference genes, while the commonly used reference genes 18S and GAPDH were the most variably expressed, with expression varying dramatically across different developmental stages. Furthermore, to reveal the crucial role of appropriate reference genes in obtaining a reliable result, analysis of PPARG expression was performed by normalization to the most and the least stable reference genes. The relative expression levels of PPARG normalized to the most stable reference genes greatly differed from those normalized to the least stable one. Therefore, evaluation of reference genes must be performed for a given experimental condition before the reference genes are used. PPIB and HMBS are the optimal reference genes for analysis of gene expression associated with IMF deposition in skeletal muscle during development.     

Selection of Stable Reference Genes for Quantitative Real-Time PCR on <i>Paeonia ostii</i> T. Hong et J. X. Zhang Leaves Exposed to Different Drought Stress Conditions

The definition of relatively stable expressed internal reference genes is essential in both traditional blotting quantification and as a modern data quantitative strategy. Appropriate internal reference genes can accurately standardize the expression abundance of target genes to avoid serious experimental errors. In this study, the expression profiles of ten candidate genes, ACT1, ACT2, GAPDH, eIF1, eIF2, α-TUB, β-TUB, TBP, RNA Pol II and RP II, were calculated for a suitable reference gene selection in Paeonia ostii T. Hong et J. X. Zhang leaves under various drought stress conditions. Data were processed by the four regularly used evaluation software. A comprehensive analysis revealed that RNA Pol II was the most stable gene and eIF2 was the least stable one. In addition, the geNorm program provided the optimal choice of two reference gene combination, RNA Pol II and β-TUB, for qRT-PCR normalization in P. ostii subjected to different drought stress levels. Our research provided convenience for gene expression analysis in P. ostii under drought stress and promoted research of effective methods to alleviate P. ostii drought stress in the future.     

Reference Gene Selection for Real-Time Quantitative PCR Analysis of the Mouse Uterus in the Peri-Implantation Period

The study of uterine gene expression patterns is valuable for understanding the biological and molecular mechanisms that occur during embryo implantation. Real-time quantitative RT-PCR (qRT-PCR) is an extremely sensitive technique that allows for the precise quantification of mRNA abundance; however, selecting stable reference genes suitable for the normalization of qRT-PCR data is required to avoid the misinterpretation of experimental results and erroneous analyses. This study employs several mouse models, including an early pregnancy, a pseudopregnancy, a delayed implantation and activation, an artificial decidualization and a hormonal treatment model; ten candidate reference genes (PPIA, RPLP0, HPRT1, GAPDH, ACTB, TBP, B2M, 18S, UBC and TUBA) that are found in uterine tissues were assessed for their suitability as internal controls for relative qRT-PCR quantification. GeNorm^PLUS, NormFinder, and BestKeeper were used to evaluate these candidate reference genes, and all of these methods identified RPLP0 and GAPDH as the most stable candidates and B2M and 18S as the least stable candidates. However, when the different models were analyzed separately, the reference genes exhibited some variation in their expression levels.     

Gene Expression Studies in Formalin-Fixed Paraffin-Embedded Samples of Cutaneous Cancer: The Need for Reference Genes

Formalin-fixed paraffin-embedded (FFPE) tumour samples may provide crucial data regarding biomarkers for neoplasm progression. Analysis of gene expression is frequently used for this purpose. Therefore, mRNA expression needs to be normalized through comparison to reference genes. In this study, we establish which of the usually reported reference genes is the most reliable one in cutaneous malignant melanoma (MM) and cutaneous squamous cell carcinoma (CSCC). ACTB, TFRC, HPRT1 and TBP expression was quantified in 123 FFPE samples (74 MM and 49 CSCC biopsies) using qPCR. Expression stability was analysed by NormFinder and Bestkeeper softwares, and the direct comparison method between means and SD. The in-silico analysis with BestKeeper indicated that HPRT1 was more stable than ACTB and TFRC in MM (1.85 vs. 2.15) and CSCC tissues (2.09 vs. 2.33). The best option to NormFinder was ACTB gene (0.56) in MM and TFRC (0.26) in CSCC. The direct comparison method showed lower SD means of ACTB expression in MM (1.17) and TFRC expression in CSCC samples (1.00). When analysing the combination of two reference genes for improving stability, NormFinder indicated HPRT1 and ACTB to be the best for MM samples, and HPRT1 and TFRC genes for CSCC. In conclusion, HPRT1 and ACTB genes in combination are the most appropriate choice for normalization in gene expression studies in MM FFPE tissue, while the combination of HPRT1 and TFRC genes are the best option in analysing CSCC FFPE samples. These may be used consistently in forthcoming studies on gene expression in both tumours.     

Selection of reference genes for expression studies in Cicer arietinum L.: analysis of cyp81E3 gene expression against Ascochyta rabiei

Normalisation to a reference gene is the most common method of internally controlling for error in quantitative PCR (qPCR) experiments. Studies based on qPCR in chickpea have been carried out using potential reference genes exclusively. Inappropriate normalisation may result in the acquisition of biologically irrelevant data. We have tested the expression of 12 candidate internal control genes in 36 samples representing different organs/developmental stages, genotypes and stress conditions. The most stably expressed genes were PUBQ, GAPDH, UBQ and bHLH, whereas 18S rRNA and EF-1a showed considerable regulation. The most suitable combination of reference genes for the particular experimental sets tested is provided. To illustrate the use of chickpea reference genes, we checked the expression of a putative defence gene in two different genotypes infected with Ascochyta rabiei (Pass.) Lab. The set of reference genes presented here will enable the more accurate and reliable normalisation of qPCR results for gene expression studies in this important legume crop. Our findings can be used as a starting point for reference gene selection in experimental conditions different from those tested here.     

Reference gene for primary culture of prostate cancer cells

Selection of reference genes to normalize mRNA levels between samples is critical for gene expression studies because their expression can vary depending on the tissues or cells used and the experimental conditions. We performed ten cell cultures from samples of prostate cancer. Cells were divided into three groups: control (with no transfection protocol), cells transfected with siRNA specific to knockdown the androgen receptor and cells transfected with inespecific siRNAs. After 24 h, mRNA was extracted and gene expression was analyzed by Real-time qPCR. Nine candidates to reference genes for gene expression studies in this model were analyzed (aminolevulinate, delta-, synthase 1 (ALAS1); beta-actin (ACTB); beta-2-microglobulin (B2M); glyceraldehyde-3-phosphate dehydrogenase (GAPDH); hypoxanthine phosphoribosyltransferase 1 (HPRT1); succinate dehydrogenase complex, subunit A, flavoprotein (Fp) (SDHA); TATA box binding protein (TBP); ubiquitin C (UBC); tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta polypeptide (YWHAZ)). Expression stability was calculated NormFinder algorithm to find the most stable genes. NormFinder calculated SDHA as the most stable gene and the gene with the lowest intergroup and intragroup variation, and indicated GAPDH and SDHA as the best combination of two genes for the purpose of normalization. Androgen receptor mRNA expression was evaluated after normalization by each candidate gene and showed statistical difference in the transfected group compared to control group only when normalized by combination of GAPDH and SDHA. Based on the algorithm analysis, the combination of SDHA and GAPDH should be used to normalize target genes mRNA levels in primary culture of prostate cancer cells submitted to transfection with siRNAs.