Semantic Particularity Measure for Functional Characterization of Gene Sets Using Gene Ontology

Background

Genetic and genomic data analyses are outputting large sets of genes. Functional comparison of these gene sets is a key part of the analysis, as it identifies their shared functions, and the functions that distinguish each set. The Gene Ontology (GO) initiative provides a unified reference for analyzing the genes molecular functions, biological processes and cellular components. Numerous semantic similarity measures have been developed to systematically quantify the weight of the GO terms shared by two genes. We studied how gene set comparisons can be improved by considering gene set particularity in addition to gene set similarity.

Results

We propose a new approach to compute gene set particularities based on the information conveyed by GO terms. A GO term informativeness can be computed using either its information content based on the term frequency in a corpus, or a function of the term''s distance to the root. We defined the semantic particularity of a set of GO terms Sg1 compared to another set of GO terms Sg2. We combined our particularity measure with a similarity measure to compare gene sets. We demonstrated that the combination of semantic similarity and semantic particularity measures was able to identify genes with particular functions from among similar genes. This differentiation was not recognized using only a semantic similarity measure.

Conclusion

Semantic particularity should be used in conjunction with semantic similarity to perform functional analysis of GO-annotated gene sets. The principle is generalizable to other ontologies.     

Aberrant DNA methylation of microRNA genes in human breast cancer – a critical appraisal

Aberrant DNA methylation of regulatory sequences is a well-documented mechanism of functional deletion of genes with anti-tumourigenic properties including microRNAs. This review discusses the publications describing aberrant methylation of microRNA genes in human breast cancer cells. Among the anti-tumourigenic properties of epigenetically inactivated microRNA genes, the inhibition of proliferation and of epithelial-to-mesenchymal transition (EMT) are the best studied. Several studies are conceptually very interesting and present a comprehensive functional characterization of anti-tumorigenic microRNAs. The link between microRNA expression and gene methylation is not addressed directly by all studies and a number of studies are limited in their strength by not including primary breast cancer specimens or by analysing very small sets of primary human specimens. The publications cover a wide range of DNA methylation detection techniques, often making direct comparison of results challenging. Despite the identification and thorough characterization of many interesting candidates and functionally important microRNA genes affected by DNA methylation, the translation of microRNA gene methylation as a new biomarker into the daily routine practice has not yet worked out.     

Key epigenetic changes associated with lung cancer development

Epigenetic alterations are a common event in lung cancer and their identification can serve to inform on the carcinogenic process and provide clinically relevant biomarkers. Using paired tumor and non-tumor lung tissues from 146 individuals from three independent populations we sought to identify common changes in DNA methylation associated with the development of non-small cell lung cancer. Pathologically normal lung tissue taken at the time of cancer resection was matched to tumorous lung tissue and together were probed for methylation using Illumina GoldenGate arrays in the discovery set (n = 47 pairs) followed by bisulfite pyrosequencing for validation sets (n = 99 pairs). For each matched pair the change in methylation at each CpG was calculated (the odds ratio), and these ratios were averaged across individuals and ranked by magnitude to identify the CpGs with the greatest change in methylation associated with tumor development. We identified the top gene-loci representing an increase in methylation (HOXA9, 10.3-fold and SOX1, 5.9-fold) and decrease in methylation (DDR1, 8.1-fold). In replication testing sets, methylation was higher in tumors for HOXA9 (p < 2.2 × 10^?16) and SOX1 (p < 2.2 × 10^?16) and lower for DDR1 (p < 2.2 × 10^?16). The magnitude and strength of these changes were consistent across squamous cell and adenocarcinoma tumors. Our data indicate that the identified genes consistently have altered methylation in lung tumors. Our identified genes should be included in translational studies that aim to develop screening for early disease detection.     

Impact of Statins on Gene Expression in Human Lung Tissues

Statins are 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors that alter the synthesis of cholesterol. Some studies have shown a significant association of statins with improved respiratory health outcomes of patients with asthma, chronic obstructive pulmonary disease and lung cancer. Here we hypothesize that statins impact gene expression in human lungs and may reveal the pleiotropic effects of statins that are taking place directly in lung tissues. Human lung tissues were obtained from patients who underwent lung resection or transplantation. Gene expression was measured on a custom Affymetrix array in a discovery cohort (n = 408) and two replication sets (n = 341 and 282). Gene expression was evaluated by linear regression between statin users and non-users, adjusting for age, gender, smoking status, and other covariables. The results of each cohort were combined in a meta-analysis and biological pathways were studied using Gene Set Enrichment Analysis. The discovery set included 141 statin users. The lung mRNA expression levels of eighteen and three genes were up-regulated and down-regulated in statin users (FDR < 0.05), respectively. Twelve of the up-regulated genes were replicated in the first replication set, but none in the second (p-value < 0.05). Combining the discovery and replication sets into a meta-analysis improved the significance of the 12 up-regulated genes, which includes genes encoding enzymes and membrane proteins involved in cholesterol biosynthesis. Canonical biological pathways altered by statins in the lung include cholesterol, steroid, and terpenoid backbone biosynthesis. No genes encoding inflammatory, proteases, pro-fibrotic or growth factors were altered by statins, suggesting that the direct effect of statin in the lung do not go beyond its antilipidemic action. Although more studies are needed with specific lung cell types and different classes and doses of statins, the improved health outcomes and survival observed in statin users with chronic lung diseases do not seem to be mediated through direct regulation of gene expression in the lung.     

食管癌LAPTM4B mRNA表达及其启动子区甲基化

研究溶酶体相关4次跨膜蛋白B（lysosome associated protein transmembrane 4 beta,LAPTM4B）基因在食管癌中的表达,及其启动子区甲基化状态,为进一步揭示LAPTM4B在不同肿瘤中表达高低机理提供参考.采用半定量RT-PCR法,确定42对食管癌中LAPTM4B mRNA表达.采用5对肝癌中LAPTM4B mRNA表达做内对照（利用灰度值比较）,分析该基因在食管癌中的表达强度.选取其中3对食管癌组织样品（癌组织和癌旁正常组织）,提取基因组DNA,采用亚硫酸氢钠修饰法,联合基因测序法分析LAPTM4B启动子区是否有甲基化修饰位点存在.结果发现,在42对食管癌组织中,癌组织和癌旁正常组织LAPTM4B mRNA表达存在差异：癌组织中LAPTM4B mRNA表达阳性为37/42（88.1%）,癌旁正常组织中LAPTM4B mRNA表达阳性为26/42（61.9%）.经基因测序法分析3对食管癌组织经通用引物PCR扩增的片段,发现1例癌旁正常组织样品中有3个CpG位点.以上结果表明,LAPTM4B基因与肝癌比较在食管癌中低表达,其启动子区1例癌旁正常组织在靠近转录起始点上游-418、-416和-398位置,存在3个CpG位点,而其他2例癌旁正常组织和3例癌组织中,没有发现CpG位点.这提示,LAPTM4B基因启动子区甲基化是其表达调节的重要方式.     

Identification and Comparison of Aberrant Key Regulatory Networks in Breast,Colon, Liver,Lung, and Stomach Cancers through Methylome Database Analysis

Aberrant methylation of specific CpG sites at the promoter is widely responsible for genesis and development of various cancer types. Even though the microarray-based methylome analyzing techniques have contributed to the elucidation of the methylation change at the genome-wide level, the identification of key methylation markers or top regulatory networks appearing common in highly incident cancers through comparison analysis is still limited. In this study, we in silico performed the genome-wide methylation analysis on each 10 sets of normal and cancer pairs of five tissues: breast, colon, liver, lung, and stomach. The methylation array covers 27,578 CpG sites, corresponding to 14,495 genes, and significantly hypermethylated or hypomethylated genes in the cancer were collected (FDR adjusted p-value <0.05; methylation difference >0.3). Analysis of the dataset confirmed the methylation of previously known methylation markers and further identified novel methylation markers, such as GPX2, CLDN15, and KL. Cluster analysis using the methylome dataset resulted in a diagram with a bipartite mode distinguishing cancer cells from normal cells regardless of tissue types. The analysis further revealed that breast cancer was closest with lung cancer, whereas it was farthest from colon cancer. Pathway analysis identified that either the “cancer” related network or the “cancer” related bio-function appeared as the highest confidence in all the five cancers, whereas each cancer type represents its tissue-specific gene sets. Our results contribute toward understanding the essential abnormal epigenetic pathways involved in carcinogenesis. Further, the novel methylation markers could be applied to establish markers for cancer prognosis.     

Functional cohesion of gene sets determined by latent semantic indexing of PubMed abstracts

High-throughput genomic technologies enable researchers to identify genes that are co-regulated with respect to specific experimental conditions. Numerous statistical approaches have been developed to identify differentially expressed genes. Because each approach can produce distinct gene sets, it is difficult for biologists to determine which statistical approach yields biologically relevant gene sets and is appropriate for their study. To address this issue, we implemented Latent Semantic Indexing (LSI) to determine the functional coherence of gene sets. An LSI model was built using over 1 million Medline abstracts for over 20,000 mouse and human genes annotated in Entrez Gene. The gene-to-gene LSI-derived similarities were used to calculate a literature cohesion p-value (LPv) for a given gene set using a Fisher's exact test. We tested this method against genes in more than 6,000 functional pathways annotated in Gene Ontology (GO) and found that approximately 75% of gene sets in GO biological process category and 90% of the gene sets in GO molecular function and cellular component categories were functionally cohesive (LPv<0.05). These results indicate that the LPv methodology is both robust and accurate. Application of this method to previously published microarray datasets demonstrated that LPv can be helpful in selecting the appropriate feature extraction methods. To enable real-time calculation of LPv for mouse or human gene sets, we developed a web tool called Gene-set Cohesion Analysis Tool (GCAT). GCAT can complement other gene set enrichment approaches by determining the overall functional cohesion of data sets, taking into account both explicit and implicit gene interactions reported in the biomedical literature. Availability: GCAT is freely available at http://binf1.memphis.edu/gcat.     

DNA methylation at promoter regions of interleukin 1B, interleukin 6, and interleukin 8 in non-small cell lung cancer

Epidemiologic and experimental evidences support the concept that inflammation promotes the development and progression of cancers. Interleukins (ILs) regulate the expression of several molecules and signaling pathways involved in inflammation. High expression of some ILs in the tumor microenvironment has been associated with a more virulent tumor phenotype. To examine the role of IL-1β, IL-6, and IL-8 in non-small cell lung cancer, we measured mRNA levels and promoter DNA methylation in a panel of cultured human lung cells (n = 23) and in matched pair lung tumor versus adjacent non-tumorous tissues (n = 24). We found that lung cancer cells or tissues had significantly different DNA methylation and mRNA levels than normal human bronchial epithelial cells or adjacent non-tumorous tissues, respectively. High DNA methylation of ILs promoters in lung cancer cells or tissues was associated with low mRNA levels. We found an inverse correlation between DNA methylation of IL1B, IL6, and IL8 gene promoters and their corresponding mRNA levels, such inverse correlation was more significant for IL1B (i.e., all cancer cell lines used in this study had a hypermethylated IL1B promoter which was associated with silencing of the gene). Our results underline for the first time the role of epigenetic modifications in the regulation of the expression of key cytokines involved in the inflammatory response during lung cancer development.     

NDRG2基因启动子甲基化与肺腺癌细胞中mRNA表达相关性的实验研究

目的:探讨肺腺癌细胞中NDRG2基因启动子甲基化状态及其与基因表达的关系。方法:甲基化焦磷酸测序技术检测启动子区域甲基化状态,荧光定量PCR技术检测不同药物浓度下培养细胞中NDRG2基因mRNA的表达水平,分析启动子区域甲基化与基因表达之间的关系。结果:在体外培养细胞中检测到NDRG2基因启动子区域呈现不同程度的甲基化,甲基化频率分别为肺癌A549细胞71.8%、GLC-82细胞86.1%、人脐静脉内皮ECV-304细胞36.8%、胃上皮GES-1细胞42.9%。NDRG2基因mRNA表达与其启动子甲基化程度成反比,甲基转移酶抑制剂5-杂氮-2-脱氧胞苷(5-Aza-CdR)作用于细胞后,A549和GLC-82细胞中NDRG2基因的mRNA转录明显上调,至72 h差异显著(P0.05)。结论:肺腺癌细胞中NDRG2基因启动子CpG岛存在高甲基化,甲基化程度与该基因的表达具有负相关性,5-Aza-CdR能在一定程度上提高NDRG2的转录水平。     

Identification of prognostic signature in cancer based on DNA methylation interaction network

Background

The identification of prognostic biomarkers for cancer patients is essential for cancer research. These days, DNA methylation has been proved to be associated with cancer prognosis. However, there are few methods which identify the prognostic markers based on DNA methylation data systematically, especially considering the interaction among DNA methylation sites.

Methods

In this paper, we first evaluated the stabilities of microRNA, mRNA, and DNA methylation data in prognosis of cancer. After that, a rank-based method was applied to construct a DNA methylation interaction network. In this network, nodes with the largest degrees (10% of all the nodes) were selected as hubs. Cox regression was applied to select the hubs as prognostic signature. In this prognostic signature, DNA methylation levels of each DNA methylation site are correlated with the outcomes of cancer patients. After obtaining these prognostic genes, we performed the survival analysis in the training group and the test group to verify the reliability of these genes.

Results

We applied our method in three cancers (ovarian cancer, breast cancer and Glioblastoma Multiforme).In all the three cancers, there are more common ones of prognostic genes selected from different samples in DNA methylation data, compared with gene expression data and miRNA expression data, which indicates the DNA methylation data may be more stable in cancer prognosis. Power-law distribution fitting suggests that the DNA methylation interaction networks are scale-free. And the hubs selected from the three networks are all enriched by cancer related pathways. The gene signatures were obtained for the three cancers respectively, and survival analysis shows they can distinguish the outcomes of tumor patients in both the training data sets and test data sets, which outperformed the control signatures.

Conclusions

A computational method was proposed to construct DNA methylation interaction network and this network could be used to select prognostic signatures in cancer.

     

Key epigenetic changes associated with lung cancer development: Results from dense methylation array profiling

Epigenetic alterations are a common event in lung cancer and their identification can serve to inform on the carcinogenic process and provide clinically relevant biomarkers. Using paired tumor and non-tumor lung tissues from 146 individuals from three independent populations we sought to identify common changes in DNA methylation associated with the development of non-small cell lung cancer. Pathologically normal lung tissue taken at the time of cancer resection was matched to tumorous lung tissue and together were probed for methylation using Illumina GoldenGate arrays in the discovery set (n = 47 pairs) followed by bisulfite pyrosequencing for validation sets (n = 99 pairs). For each matched pair the change in methylation at each CpG was calculated (the odds ratio), and these ratios were averaged across individuals and ranked by magnitude to identify the CpGs with the greatest change in methylation associated with tumor development. We identified the top gene-loci representing an increase in methylation (HOXA9, 10.3-fold and SOX1, 5.9-fold) and decrease in methylation (DDR1, 8.1-fold). In replication testing sets, methylation was higher in tumors for HOXA9 (p < 2.2 × 10⁻¹⁶) and SOX1 (p < 2.2 × 10⁻¹⁶) and lower for DDR1 (p < 2.2 × 10⁻¹⁶). The magnitude and strength of these changes were consistent across squamous cell and adenocarcinoma tumors. Our data indicate that the identified genes consistently have altered methylation in lung tumors. Our identified genes should be included in translational studies that aim to develop screening for early disease detection.     

Identifying candidate genes for wood formation in poplar based on microarray network analysis and graph theory

In poplar, genetic research on wood properties is very important for the improvement of wood quality. Studies of wood formation genes at each developmental stage using modern biotechnology have often been limited to several genes or gene families. Because of the complex regulatory network involved in the co-expression and interactions of thousands of genes, however, the genetic mechanisms of wood formation must be surveyed on a genome-wide scale. In this study, we identified wood formation-related genes using a differentially co-expressed (DCE) gene subset approach based on biological networks inferred from microarray data. Gene co-expression networks in leaf, root, and wood tissues were first constructed and topologically analyzed using microarray data collected from the Gene Expression Omnibus. The DCE gene modules in wood-forming tissue were then detected based on graph theory, which was followed by gene ontology (GO) enrichment analysis and GO annotation of probe sets. Finally, 72 probe sets were identified in the largest cohesive subgroup of the DCE gene network in wood tissue, with most of the probe sets associated with wood formation-related biological processes and GO cellular component categories. The approach described in this paper provides an effective strategy to identify wood formation genes in poplar and should contribute to the better understanding of the genetic and molecular mechanisms underlying wood properties in trees.     

Chinese Glioma Genome Atlas(CGGA):A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients

Gliomas are the most common and malignant intracranial tumors in adults. Recent studies have revealed the significance of functional genomics for glioma pathophysiological studies and treatments. However, access to comprehensive genomic data and analytical platforms is often limited. Here, we developed the Chinese Glioma Genome Atlas(CGGA), a user-friendly data portal for the storage and interactive exploration of cross-omics data, including nearly 2000 primary and recurrent glioma samples from Chinese cohort. Currently, open access is provided to whole-exome sequencing data(286 samples), mRNA sequencing(1018 samples) and microarray data(301 samples), DNA methylation microarray data(159 samples), and microRNA microarray data(198 samples), and to detailed clinical information(age, gender, chemoradiotherapy status,WHO grade, histological type, critical molecular pathological information, and survival data). In addition, we have developed several tools for users to analyze the mutation profiles,mRNA/microRNA expression, and DNA methylation profiles, and to perform survival and gene correlation analyses of specific glioma subtypes. This database removes the barriers for researchers,providing rapid and convenient access to high-quality functional genomic data resources for biological studies and clinical applications. CGGA is available at http://www.cgga.org.cn.     

Evaluating the consistency of gene sets used in the analysis of bacterial gene expression data

ABSTRACT: BACKGROUND: Statistical analyses of whole genome expression data require functional information about genes in order to yield meaningful biological conclusions. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) are common sources of functionally grouped gene sets. For bacteria, the SEED and MicrobesOnline provide alternative, complementary sources of gene sets. To date, no comprehensive evaluation of the data obtained from these resources has been performed. RESULTS: We define a series of gene set consistency metrics directly related to the most common classes of statistical analyses for gene expression data, and then perform a comprehensive analysis of 3581 Affymetrix gene expression arrays across 17 diverse bacteria. We find that gene sets obtained from GO and KEGG demonstrate lower consistency than those obtained from the SEED and MicrobesOnline, regardless of gene set size. CONCLUSIONS: Despite the widespread use of GO and KEGG gene sets in bacterial gene expression data analysis, the SEED and MicrobesOnline provide more consistent sets for a wide variety of statistical analyses such data. Increased use of the SEED and MicrobesOnline gene sets in the analysis of bacterial gene expression data may improve statistical power and utility of expression data.     

基于蛋白互作网络识别非小细胞肺癌相关基因功能模块

本研究对非小细胞肺癌(non-small cell lung carcinoma,NSCLC)基因表达数据进行差异表达分析,并与蛋白质相互作用网络(PPIN)数据进行整合,进一步利用Heinz搜索算法识别NSCLC相关的基因功能模块,并对模块中的基因进行功能(GO term)和通路(KEGG)富集分析,旨在探究肺癌发病分子机制。蛋白互作网络分析得到一个包含96个基因和117个相互作用的功能模块,以及8个对NSCLC的发生和发展起到关键作用候选基因标志物。富集分析结果表明,这些基因主要富集于基因转录催化及染色质调控等生物学过程,并在基础转录因子、黏着连接、细胞周期、Wnt信号通路及HTLV-Ⅰ感染等生物学通路中发挥重要作用。本研究对非小细胞肺癌相关的基因和生物学通路进行预测,可用于肺癌的早期诊断和早期治疗,以降低肺癌死亡率。     

Gene Class expression: analysis tool of Gene Ontology terms with gene expression data

Serial analysis of gene expression (SAGE) technology produces large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in these gene sets. We present an interactive web-based tool, called Gene Class, which allows functional annotation of SAGE data using the Gene Ontology (GO) database. This tool performs searches in the GO database for each SAGE tag, making associations in the selected GO category for a level selected in the hierarchy. This system provides user-friendly data navigation and visualization for mapping SAGE data onto the gene ontology structure. This tool also provides graphical visualization of the percentage of SAGE tags in each GO category, along with confidence intervals and hypothesis testing.     

NF2基因甲基化及其mRNA表达与乳腺癌发病的关系

目的:探讨乳腺癌中NF2基因启动子甲基化状态及其mRNA水平与乳腺癌发病的关系.方法:应用甲基化特异性聚合酶链反应(MSP)和逆转录-聚合酶链反应(RT-PCR)技术,检测47例乳腺癌组织及相应的癌旁组织和15例乳腺良性病变组织,分析NF2基因的甲基化与某些临床参数及mRNA表达的关系.结果:NF2基因启动子区在乳腺癌、癌旁和乳腺良性病变组织中的甲基化频率分别为57.4%(27/47)、23.4%(23/47)和0%(0/15).且乳腺癌组明显高于其余两组(P＜0.05).NF2基因发生甲基化与发病年龄、组织分型、转移和组织分级无相关性.乳腺癌组NF2基因mRNA的相对表达量(0.16±0.11)明显低于相应的癌旁组(0.27±0.14)及乳腺良性病变组(0.64±0.17)(P＜0.05).NF2基因启动子区甲基化频率与其mRNA表达呈负相关(Spearman's r=-0.314,P＜0.05).结论:NF2基因发生甲基化与乳腺癌的发生密切相关,NF2mRNA表达与NF2基因启动子高甲基化呈负相关.     

Association of NDRG1 Gene Promoter Methylation with Reduced NDRG1 Expression in Gastric Cancer Cells and Tissue Specimens

NDRG1 (N-myc downstream-regulated gene 1) plays a role in cell differentiation and suppression of tumor metastasis. This study aims to determine the expression of NDRG1 mRNA and protein in gastric cancer cell lines and tissue specimens and then assess the possible cause of its aberrant expression. Six gastric cancer cell lines and 20 pairs of normal and gastric cancer tissue samples were used to assess NDRG1 expression using Real-time PCR and Western blot. High-resolution melting analysis (HRM) and methylation-specific PCR (MSP) were performed to detect gene mutation and methylation, respectively, in cell lines and tissues samples. Expression of NDRG1 mRNA and protein was downregulated in gastric cancer cell lines and tissues. Specifically, expression of NDRG1 mRNA and protein was lower in all six gastric cancer cell lines than that of normal gastric cells, while 15 out of 20 cases of gastric cancer tissues had the reduced levels of NDRG1 mRNA and protein. HRM data showed that there was no mutation in NDRG1 gene, but MSP data showed high levels of NDRG1 gene promoter methylation in the CpG islands in both cell lines and tissue samples. Moreover, treatment with the DNA methyltransferase inhibitor 5-Aza-2′-deoxycytidine upregulated NDRG1 expression in gastric cancer HGC27 cells, but not in the histone deacetylase inhibitor trichostatin A-treated HGC27 cells. In conclusion, this study has shown that expression of NDRG1 mRNA and protein was reduced in gastric cancer cell lines and tissues, which is due to methylation of NDRG1 gene promoter. Further study will unearth the clinical significance of the reduced NDRG1 protein in gastric cancer.     

Novel reference gene RPN1 for normalization of quantitative data in lung and kidney cancer

Quantitative methods of gene expression analysis in tumors require accurate data normalization, which allows comparison of different mRNA/cDNA samples with unknown concentration. For this purpose reference genes with stable expression level (such as GAPDH, ACTB, HPRT1, TBP) are used. The choice of appropriate reference genes is still actual because well-known reference genes are not suitable for certain cancer types frequently and their unreasonable use without additional tests lead to wrong conclusions. We have developed the bioinformatic approach and selected a new potential reference gene RPN1 for lung and kidney tumors. This gene is located at the long arm of chromosome 3. Our method includes mining of the dbEST and Oncomine databases and functional analysis of genes. The RPN1 was selected from 1500 candidate housekeeping genes. Using comparative genomic hybridization with NotI-microarrays we found no methylation, deletions and/or amplifications at the RPN1-containing locus in 56 non-small cell lung and 42 clear cell renal cancer samples. Using RT-qPCR we showed low variability of RPN1 mRNA level comparable to those of reference genes GAPDH and GUSB in lung and kidney cancer. The mRNA levels of two target genes coding hyalouronidases--HYAL1 and HYAL2--were estimated and normalized relative to pair RPN1--GAPDH genes for lung cancer and RPN1--GUSB for kidney cancer. These combinations were shown to be optimal for obtaining accurate and reproducible data. All obtained results allow us to suggest RPN1 as novel reference gene for quantitative data normalization in gene expression studies for lung and kidney cancers.