Method For Gene Expression Analysis Research Articles

Characterization of Reference Genes in Rare Minnow, <I>Gobiocypris Rarus</I> (Actinopterygii: Cypriniformes: Cyprinidae), in Early Postembryonic Development and in Response to Edcs Treatment

Background. Endocrine disrupting chemicals (EDCs) are natural and anthropogenic compounds discharged into the environment known to disrupt the endocrine system of humans and animals by mimicking functions of steroids in vivo. Many important events occurring during early postembryonic development, in relation to the gene expression attracted our attention. Quantitative real-time PCR (qRT-PCR) is a sensitive and highly reproducible method for gene expression analysis, with gene expression levels quantified by normalization to reference gene. The aim of this study was to select the suitable reference gene after EDCs exposure and during early postembryonic development. Materials and methods. For the study of the fish age effect, juveniles of Gobiocypris rarus Ye et Fu, 1983, were obtained at: 18, 22, 26, 30, 34, 38, 42, 46, and 50 days post fertilization (dpf). For mRNA expression analysis of the juvenile fish after EDCs treatment, the juveniles at 31 dpf were exposed to bisphenol A (BPA) (10 nM) and 17α-ethinylestradiol (EE2) (1 nM), respectively dissolved in dimethyl sulfoxide (DMSO) or solvent (0.001% DMSO, v/v) control group for 3 days. Cq values of the reference genes were obtained using qRT-PCR. The stability of these reference genes was analyzed by BestKeeper, geNorm, and NormFinder software, respectively. The expression of each reference gene was calculated using the 2–ΔCq method. In parallel, the mRNA expressions of cyp19a1b were normalized by the single most/least stable reference gene and the combinations of top-ranked reference genes.  Results. In this study, six candidate reference genes, actb, ef1a, gapdh, g6pd, tbp, and tuba1, were chosen to analyze their expression stability in relation to fish age and in the juvenile fish exposed to BPA and EE2. During early postembryonic development of Gobiocypris rarus, actb，ef1a, and gapdh were identified as the most stably expressed reference genes. In the juvenile fish exposed to BPA and EE2 for three days, gapdh, and actb were the most stable. However, g6pd and tuba1 were identified as the least stably expressed genes during the early postembryonic development and under BPA and EE2 exposure.  Conclusion. The presently reported study suggested that the mRNA expressions of the reference genes could be affected by chemical exposure or different physiological periods. In addition, it was indicated that stable reference gene should be selected to normalize the target gene expression to assure the correctness and accuracy of the experiment results. The last but not the least, we successfully obtained five commonly used reference genes of Gobiocypris rarus Ye et Fu, 1983, which can be applied in future studies serving as the stable reference gene and providing a broader range of selecting the stable reference gene.

A novel method for cross-species gene expression analysis

BackgroundAnalysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. However, due to evolutionary events such as gene duplication, there is no one-to-one correspondence between genes from different species which makes comparison of their expression profiles complex.ResultsIn this paper we describe a new method for cross-species meta-analysis of gene expression. The method takes the homology structure between compared species into account and can therefore compare expression data from genes with any number of orthologs and paralogs. A simulation study shows that the proposed method results in a substantial increase in statistical power compared to previously suggested procedures. As a proof of concept, we analyzed microarray data from heat stress experiments performed in eight species and identified several well-known evolutionarily conserved transcriptional responses. The method was also applied to gene expression profiles from five studies of estrogen exposed fish and both known and potentially novel responses were identified.ConclusionsThe method described in this paper will further increase the potential and reliability of meta-analysis of gene expression profiles from evolutionarily distant species. The method has been implemented in R and is freely available at http://bioinformatics.math.chalmers.se/Xspecies/.

Selection of Reference Genes for Quantitative Real-Time PCR in Bamboo (Phyllostachys edulis)

BackgroundThe Moso bamboo (Phyllostachys edulis) is one of the most important forestry resources and plays essential ecological roles in southern China. A draft nuclear genome sequence is expected to be publicly available in the near future; an explosion of gene expression data related to the unique traits of Moso bamboo will undoubtedly follow. Reverse transcription quantitative real-time PCR ((RT-)qPCR) is a widely used method for gene expression analysis. A necessary prerequisite of exact and reliable data is the accurate choice of reference genes.ResultIn this study, 14 candidate reference genes were chosen, and their expression levels were assessed by (RT-)qPCR in a set of six tissue samples (root, stem, mature stem, leaf, flower, and leaf sheath) and at two developmental stages (before and after flowering) in bamboo specimens obtained in three locations. The stability and suitability of the candidate reference genes were validated using the geNorm, NormFinder and BestKeeper programs. The results showed that TIP41 and NTB were suitable reference genes across all the tissues and at the different developmental stages examined in this study. While the expression of the NTB, TIP41 and UBQ were the mostly stable in different plant tissues samples, the expression of the TIP41, NTB and CAC were ranked the most stable in bamboo plants at various developmental stages. AP2-like gene was further assessed by using the reference genes TIP41 and NTB in comparison to ACT. Significant difference of the expression profile of AP2-like demonstrated the importance of choosing adequate reference genes in bamboo.Conclusion TIP41 and NTB were found to be homogeneously expressed and were adequate for normalization purposes, showing equivalent transcript levels in different samples. They are therefore the recommended reference genes for measuring gene expression in P. edulis.

Puma 3.0: improved uncertainty propagation methods for gene and transcript expression analysis

BackgroundMicroarrays have been a popular tool for gene expression profiling at genome-scale for over a decade due to the low cost, short turn-around time, excellent quantitative accuracy and ease of data generation. The Bioconductor package puma incorporates a suite of analysis methods for determining uncertainties from Affymetrix GeneChip data and propagating these uncertainties to downstream analysis. As isoform level expression profiling receives more and more interest within genomics in recent years, exon microarray technology offers an important tool to quantify expression level of the majority of exons and enables the possibility of measuring isoform level expression. However, puma does not include methods for the analysis of exon array data. Moreover, the current expression summarisation method for Affymetrix 3’ GeneChip data suffers from instability for low expression genes. For the downstream analysis, the method for differential expression detection is computationally intensive and the original expression clustering method does not consider the variance across the replicated technical and biological measurements. It is therefore necessary to develop improved uncertainty propagation methods for gene and transcript expression analysis.ResultsWe extend the previously developed Bioconductor package puma with a new method especially designed for GeneChip Exon arrays and a set of improved downstream approaches. The improvements include: (i) a new gamma model for exon arrays which calculates isoform and gene expression measurements and a level of uncertainty associated with the estimates, using the multi-mappings between probes, isoforms and genes, (ii) a variant of the existing approach for the probe-level analysis of Affymetrix 3’ GeneChip data to produce more stable gene expression estimates, (iii) an improved method for detecting differential expression which is computationally more efficient than the existing approach in the package and (iv) an improved method for robust model-based clustering of gene expression, which takes technical and biological replicate information into consideration.ConclusionsWith the extensions and improvements, the puma package is now applicable to the analysis of both Affymetrix 3’ GeneChips and Exon arrays for gene and isoform expression estimation. It propagates the uncertainty of expression measurements into more efficient and comprehensive downstream analysis at both gene and isoform level. Downstream methods are also applicable to other expression quantification platforms, such as RNA-Seq, when uncertainty information is available from expression measurements. puma is available through Bioconductor and can be found at http://www.bioconductor.org.

Troubleshooting methods for microarray gene expression analysis in the onset of diabetic kidney disease

Introduction: Microarrays have become the standard technique for discovering new genes involved in the development of (kidney) disease. Diabetic nephropathy is a frequent complication of diabetes and is characterized by renal fibrosis. As the pathways leading to fibrosis are initiated early in diabetes and in the current study, we aimed at identifying genes associated with renal fibrosis in the first week after induction of diabetes in the rat streptozotocin (STZ) model. Methods: Conventional microarray analysis methods comparing gene expression to a common reference are not very suitable for time series as gene lists for all time point are very heterogeneous. We therefore sought an analysis technique that would allow us to easily find genes that we either substantially up or down regulated during the first week of diabetes. In the new method, the normalized expression of individual genes was plotted in time. Subsequently, the area under the curve (AUC) was calculated to quantify the overall level of changes in expression of individual genes. Results: AUCs for all genes were plotted in a histogram showing a normal distribution with a mean of close to 0, indicating no change in expression for the majority of genes. Genes with AUCs outside 3 standard deviations of the mean were considered significantly different from control. Discussion: Using this technique, a total of 290 genes were found to be significantly changed in the first week of diabetes. Data on a subset of genes were confirmed by real-time PCR, indicating the validity of the employed new analysis method.

Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data.

A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.

Stable Internal Reference Genes for the Normalization of Real-Time PCR in Different Sweetpotato Cultivars Subjected to Abiotic Stress Conditions

Reverse transcription quantitative real-time PCR (RT-qPCR) has become one of the most widely used methods for gene expression analysis, but its successful application depends on the stability of suitable reference genes used for data normalization. In plant studies, the choice and optimal number of reference genes must be experimentally determined for the specific conditions, plant species, and cultivars. In this study, ten candidate reference genes of sweetpotato (Ipomoea batatas) were isolated and the stability of their expression was analyzed using two algorithms, geNorm and NormFinder. The samples consisted of tissues from four sweetpotato cultivars subjected to four different environmental stress treatments, i.e., cold, drought, salt and oxidative stress. The results showed that, for sweetpotato, individual reference genes or combinations thereof should be selected for use in data normalization depending on the experimental conditions and the particular cultivar. In general, the genes ARF, UBI, COX, GAP and RPL were validated as the most suitable reference gene set for every cultivar across total tested samples. Interestingly, the genes ACT and TUB, although widely used, were not the most suitable reference genes in different sweetpotato sample sets. Taken together, these results provide guidelines for reference gene(s) selection under different experimental conditions. In addition, they serve as a foundation for the more accurate and widespread use of RT-qPCR in various sweetpotato cultivars.

Reconstructing differentially co-expressed gene modules and regulatory networks of soybean cells

BackgroundCurrent experimental evidence indicates that functionally related genes show coordinated expression in order to perform their cellular functions. In this way, the cell transcriptional machinery can respond optimally to internal or external stimuli. This provides a research opportunity to identify and study co-expressed gene modules whose transcription is controlled by shared gene regulatory networks.ResultsWe developed and integrated a set of computational methods of differential gene expression analysis, gene clustering, gene network inference, gene function prediction, and DNA motif identification to automatically identify differentially co-expressed gene modules, reconstruct their regulatory networks, and validate their correctness. We tested the methods using microarray data derived from soybean cells grown under various stress conditions. Our methods were able to identify 42 coherent gene modules within which average gene expression correlation coefficients are greater than 0.8 and reconstruct their putative regulatory networks. A total of 32 modules and their regulatory networks were further validated by the coherence of predicted gene functions and the consistency of putative transcription factor binding motifs. Approximately half of the 32 modules were partially supported by the literature, which demonstrates that the bioinformatic methods used can help elucidate the molecular responses of soybean cells upon various environmental stresses.ConclusionsThe bioinformatics methods and genome-wide data sources for gene expression, clustering, regulation, and function analysis were integrated seamlessly into one modular protocol to systematically analyze and infer modules and networks from only differential expression genes in soybean cells grown under stress conditions. Our approach appears to effectively reduce the complexity of the problem, and is sufficiently robust and accurate to generate a rather complete and detailed view of putative soybean gene transcription logic potentially underlying the responses to the various environmental challenges. The same automated method can also be applied to reconstruct differentially co-expressed gene modules and their regulatory networks from gene expression data of any other transcriptome.

A novel method for RNA extraction from Andosols using casein and its application to amoA gene expression study in soil

The lack of a universal method to extract RNA from soil hinders the progress of studies related to nitrification in soil, which is an important step in the nitrogen cycle. It is particularly difficult to extract RNA from certain types of soils such as Andosols (volcanic ash soils), which is the dominant agricultural soil in Japan, because of RNA adsorption by soil. To obtain RNA from these challenging soils to study the bacteria involved in nitrification, we developed a soil RNA extraction method for gene expression analysis. Autoclaved casein was added to an RNA extraction buffer to recover RNA from soil, and high-quality RNA was successfully extracted from eight types of agricultural soils that were significantly different in their physicochemical characteristics. To detect bacterial ammonia monooxygenase subunit A gene (amoA) transcripts, bacterial genomic DNA and messenger RNA were co-extracted from two different types of Andosols during incubation with ammonium sulfate. Polymerase chain reaction-denaturing gradient gel electrophoresis and reverse transcription polymerase chain reaction-denaturing gradient gel electrophoresis analyses of amoA in soil microcosms revealed that only few amoA, which had the highest similarities to those in Nitrosospira multiformis, were expressed in these soils after treatment with ammonium sulfate, although multiple amoA genes were present in the soil microcosms examined.

Comparative Analysis of Gene Expression Level by Quantitative Real-Time PCR Has Limited Application in Objects with Different Morphology

qRT-PCR is a generally acknowledged method for gene expression analysis due to its precision and reproducibility. However, it is well known that the accuracy of qRT-PCR data varies greatly depending on the experimental design and data analysis. Recently, a set of guidelines has been proposed that aims to improve the reliability of qRT-PCR. However, there are additional factors that have not been taken into consideration in these guidelines that can seriously affect the data obtained using this method. In this study, we report the influence that object morphology can have on qRT-PCR data. We have used a number of Arabidopsis thaliana mutants with altered floral morphology as models for this study. These mutants have been well characterised (including in terms of gene expression levels and patterns) by other techniques. This allows us to compare the results from the qRT-PCR with the results inferred from other methods. We demonstrate that the comparison of gene expression levels in objects that differ greatly in their morphology can lead to erroneous results.

A Comprehensive Proteome ofMycoplasma genitalium

Mycoplasma genitalium is a human pathogen associated with several sexually transmitted diseases. Proteomic technologies, along with other methods for global gene expression analysis, play a key role in understanding the mechanisms of bacterial pathogenesis and physiology. The proteome of M. genitalium, model of a minimal cell, has been extended using a combination of different proteomic approaches and technologies. The total proteome of this microorganism has been analyzed using gel-based and gel-free approaches, achieving the identification of 85.3% of the predicted ORFs. In addition, a comprehensive analysis of membrane subproteome has been performed. For this purpose, the TX-114 soluble fraction has been analyzed as well as the surface proteins, using cell-surface protein labeling with CyDye. Finally, the serological response of M. genitalium-infected patients and healthy donors has been analyzed to identify proteins that trigger immunological response. Here, we present the most extensive M. genitalium proteome analysis (85.3% of predicted ORFs), a comprehensive M. genitalium membrane analysis, and a study of the human serological response to M. genitalium.

Internal standards for quantitative RT-PCR studies of gene expression under drought treatment in barley (Hordeum vulgare L.): the effects of developmental stage and leaf age

Drought is the most significant abiotic stress in agriculture; thus, this area of studies seems to be one of the most important challenges in plant biology. Data about gene expression under drought are crucial to study drought response mechanisms and to select the genes for a transgenic approach. Quantitative RT-PCR is a powerful method for gene expression analysis; however, obtaining proper data normalization requires internal reference genes with stable level of expression. In the present paper ten potential reference genes were examined in two developmental stages of barley for their expression stability during leaf growth and increasing drought level. The results indicated that leaf growth per se affects the expression of studied genes to the similar extent as the drought and showed that different genes were most stably expressed in the seedling and the heading stage. As a result, different sets of reference genes were selected for different applications. For instance, ADP-ribozylation factor 1 and ubiquitin encoding genes were most suitable to study drought-induced changes in gene expression at the seedling stage, whereas actin and GAPDH genes were useful during heading, and ADP-ribozylation factor 1 and HSP90 allowed for the comparison between these two stages. Our data proved the necessity for validation of commonly used reference genes. The results indicate that expression of ADP seems to be the least affected by all the factors studied in the present experiment. However, when the effect of only one factor among those investigated in this work will be studied, different genes should be considered to be used as the references due to the higher stability of their expression.

Genome-wide identification and characterization of a panel of house-keeping genes in Schistosoma japonicum

Quantitative real-time polymerase chain reaction (qPCR), as one of the most sensitive and precise gene expression analysis methods, is frequently used to validate data obtained in high-through-put assays. qPCR requires reference genes with stable transcription for accurate normalization. However, no systematic studies on such genes have been performed in the genus Schistosoma japonicum. In this study, eight novel candidate genes selected from a microarray analysis and four commonly used reference genes were systematically validated in a series of qPCR experiments. Based on the results of geNorm, Normfinder, BestKeeper, and the comparative delta-cycle threshold (ΔCT) integrated analysis, the genes PSMD4, NDUFV2, and TPC2L were found to be most stably expressed in all S. japonicum developmental stages; meanwhile, ACTB and TUBA were found as the least stably expressed genes. This study provided, at the first time, data for genes that can be explored as reliable references in transcriptomic analysis of S. japonicum.

Setting up a gene expression study for tissue cells by method of quantitative real-time PCR

Setting up a gene expression study for tissue cells by method of quantitative real-time PCR Gene expression analysis by quantitative reverse transcription polymerase chain reaction (RT-qPCR) allows accurate and sensitive measurment of gene expression levels. However, a series of steps needs to be taken to ensure the relevance, accuracy, correct interpretation and repeatability of the RT-qPCR experiment. We describe here a simple experiment of determining relative gene expression for caspase 8 gene in chicken chondrocytes treated with an apoptosis inducing compound 5-fluorouracil. We use this example to point out some important guidelines in setting up a gene expression study in tissue cells, analyzing and interpreting the results and reporting

Enchanced Expression of Gene Data

DNA microarray analysis has become the most widely used functional genomics approach in the bioinformatics field. Biologists are vastly plagued by the enormous amount of unprecedented qualities of genome-wide data produced by the DNA Microarray experiment.Clustering is the process of grouping data objects into set of disjoint classes called clusters so that objects within a class are highly similar with one another and dissimilar with the objects in other classes. It is presently the far most used method for gene expression analysis which provides a divide-and–conquer strategy to extract meaningful information from expression profile.In transcriptomics,clustering is used to build groups of genes with related expression patterns (also known as coexpressed genes). Often such groups contain functionally related proteins, such as enzymes for a specific pathway, or genes that are co-regulated. This paper presents a review on the recently development of microarray clustering techniques followed by the different categories of gene expression data clustering.

A Systematic Review of Endoglin Gene Expression in Preeclampsia

To synthesize scientific literature that addresses the role of endoglin (ENG) gene expression in preeclampsia (PE). A literature search of PubMed and Ovid MEDLINE was conducted using the keywords endoglin, gene, and preeclampsia. Restrictions included English language and humans. Additional articles were identified/selected for evaluation via PubMed e-mail updates (keywords: endoglin and preeclampsia) and review of article reference lists obtained from the search. The initial 14 abstracts retrieved from the literature search were reviewed and 9 studies were selected for evaluation. Review articles and studies not addressing ENG expression (messenger RNA [mRNA] level) in the context of PE were excluded. An additional six articles were selected from PubMed e-mail updates and reference lists. Data related to study objective, design, setting, subject information, phenotype, tissue type, data collection method, statistics, and results were extracted. Regardless of PE definition, ancestral background, gene expression analysis method, tissue type, and time of specimen collection, endoglin appears to play a role in PE development. Moreover, results suggest that a variety of biological mechanisms have the ability to modulate ENG expression in PE, demonstrating the potential complexity associated with endoglin's role in PE. This review article is the first to systematically synthesize evidence related to ENG expression in PE. Findings can be utilized to design future studies that (a) address methodological limitations observed in the reviewed studies and (b) specifically examine why ENG expression levels are altered and address mechanisms explaining how these alterations are involved in PE development.

Evaluation and identification of reliable reference genes for pharmacogenomics, toxicogenomics, and small RNA expression analysis

Pharmacogenomics, toxicogenomics, and small RNA expression analysis are three of the most active research topics in the biological, biomedical, pharmaceutical, and toxicological fields. All of these studies are based on gene expression analysis, which requires reference genes to reduce the variations derived from different amounts of starting materials and different efficiencies of RNA extraction and cDNA synthesis. Thus, accurate normalization to one or several constitutively expressed reference genes is a prerequisite to valid gene expression studies. Although selection of reliable reference genes has been conducted in previous studies in several animals and plants, no research has been focused on pharmacological targets, and very few studies have had a toxicological context. More interestingly, no studies have been performed to identify reference genes for small RNA analysis although small RNA, particularly microRNA (miRNA)-related research is currently one of the fastest-moving topics. In this study, using MCF-7 breast cancer cells as a model, we employed quantitative real-time PCR (qRT-PCR), one of the most reliable methods for gene expression analysis in many research fields, to evaluate and to determine the most reliable reference genes for pharmacogenomics and toxicogenomics studies as well as for small RNA expression analysis. We tested the transcriptional expression of five protein-coding genes as well as five non-coding genes in MCF-7 cells treated with five different pharmaceuticals or toxicants [paclitaxel (PTX), gossypol (GOS), methyl jasmonate (JAS), L-nicotine (NIC), and melamine (mela)] and analyzed the stability of the selected reference genes by four different methods: geNorm, NormFinder, BestKeeper, and the comparative ΔCt method. According to our analysis, a protein-coding gene, hTBCA and four non-coding genes, hRNU44, hRNU48, hU6, and hRNU47, appear to be the most reliable reference genes for the five chemical treatments. Similar results were also obtained in dose-response and time-course assays with gossypol (GOS) treatment. Our results demonstrated that traditionally used reference genes, such as 18s RNA, β-actin, and GAPDH, are not reliable reference genes for pharmacogenomics and toxicogenomics studies. In contrast, hTBCA and small RNAs are more stable during drug treatment, and they are better reference genes for pharmacogenomics and toxicogenomics studies. To widely use these genes as reference genes, these results should be corroborated by studies with other human cell lines and additional drugs classes and hormonal treatments.

Investigating pathogen biology at the level of the proteome

Bacterial infections are a major cause of morbidity and mortality throughout the world. By extending our understanding of the process of bacterial pathogenesis at the molecular level new strategies for their treatment and prevention can be developed. Proteomic technologies, along with other methods for global gene expression analysis, play an important role in understanding the mechanism(s) of bacterial pathogenesis. This review highlights the use of proteomics to identify protein biomarkers for virulent bacterial isolates and how these biomarkers can be correlated with the outcome of bacterial infection. Biomarker identification typically looks at the proteomes of bacteria grown under laboratory conditions. It is, however, the characterisation of the bacterial proteome during in vivo infection of its host that will eventually provide the most significant insights into bacterial pathogenesis. Although this area of research has significant technical challenges, a number of complementary proteome analytical approaches are being developed to identify and characterise the bacterial genes specifically expressed in vivo. Ultimately, the development of newly targeted therapies and vaccines using specific protein targets identified through proteomic analyses will be one of the major practical benefits arising from the proteomic analysis of bacterial pathogens.

Selection and Validation of Reference Genes for Quantitative Real-Time PCR in Buckwheat (Fagopyrum esculentum) Based on Transcriptome Sequence Data

Quantitative reverse transcription PCR (qRT-PCR) is one of the most precise and widely used methods of gene expression analysis. A necessary prerequisite of exact and reliable data is the accurate choice of reference genes. We studied the expression stability of potential reference genes in common buckwheat (Fagopyrum esculentum) in order to find the optimal reference for gene expression analysis in this economically important crop. Recently sequenced buckwheat floral transcriptome was used as source of sequence information. Expression stability of eight candidate reference genes was assessed in different plant structures (leaves and inflorescences at two stages of development and fruits). These genes are the orthologs of Arabidopsis genes identified as stable in a genome-wide survey gene of expression stability and a traditionally used housekeeping gene GAPDH. Three software applications – geNorm, NormFinder and BestKeeper - were used to estimate expression stability and provided congruent results. The orthologs of AT4G33380 (expressed protein of unknown function, Expressed1), AT2G28390 (SAND family protein, SAND) and AT5G46630 (clathrin adapter complex subunit family protein, CACS) are revealed as the most stable. We recommend using the combination of Expressed1, SAND and CACS for the normalization of gene expression data in studies on buckwheat using qRT-PCR. These genes are listed among five the most stably expressed in Arabidopsis that emphasizes utility of the studies on model plants as a framework for other species.

RPN1, 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.