Comparison of microarray and sage techniques in gene expression analysis of human glioblastoma

To enhance glioblastoma (GB) marker discovery we compared gene expression in GB with human normal brain (NB) by accessing SAGE Genie web site and compared obtained results with published data. Nine GB and five NB SAGE-libraries were analyzed using the Digital Gene Expression Displayer (DGED), the results of DGED were tested by Northern blot analysis and RT-PCR of arbitrary selected genes. Review of available data from the articles on gene expression profiling by microarray-based hybridization showed as few as 35 overlapped genes with increased expression in GB. Some of them were identified in four articles, but most genes in three or even in two investigations. There was found also some differences between SAGE results of GB analysis. Digital Gene Expression Displayer approach revealed 676 genes differentially expressed in GB vs. NB with cut-off ratio: twofold change and P < or = 0.05. Differential expression of selectedgenes obtained by DGED was confirmed by Northern analysis and RT-PCR. Altogether, only 105 of 955 genes presented in published investigations were among the genes obtained by DGED. Comparison of the results obtained by microarrays and SAGE is very complicated because authors present only the most prominent differentially expressed genes. However, even available data give quite poor overlapping of genes revealed by microarrays. Some differences between results obtained by SAGE in different investigations can be explained by high dependence on the statistical methods used. As for now, the best solution to search for molecular tumor markers is to compare all available results and to select only those genes, which significant expression in tumor combined with very low expression in normal tissues was reproduced in several articles. 105 differentially expressed genes, common to both methods, can be included in the list of candidates for the molecular typing of GBs. Some genes, encoded cell surface or extra-cellular proteins may be useful for targeting gliomas with antibody-based therapy.     

Errors in CGAP xProfiler and cDNA DGED: the importance of library parsing and gene selection algorithms

Background  

The Cancer Genome Anatomy Project (CGAP) xProfiler and cDNA Digital Gene Expression Displayer (DGED) have been made available to the scientific community over a decade ago and since then were used widely to find genes which are differentially expressed between cancer and normal tissues. The tissue types are usually chosen according to the ontology hierarchy developed by NCBI. The xProfiler uses an internally available flat file database to determine the presence or absence of genes in the chosen libraries, while cDNA DGED uses the publicly available UniGene Expression and Gene relational databases to count the sequences found for each gene in the presented libraries.     

Mating flight causes genome-wide transcriptional changes in sexually mature honeybee queens

In this study, we analyzed the gene and miRNA expression differences between the courted virgin queen (CVQ) and non-courted virgin queen (NCVQ) of Apis mellifera using a high-throughput sequencing method. Through Digital Gene Expression (DGE) sequencing, 452 genes were differentially expressed, out of which, 90 genes were up-regulated and 362 genes were down-regulated in CVQ compared with NCVQ. Through small RNA sequencing, 27 miRNAs showed significant expression difference between these two samples. Moreover, 9 of the differentially expressed genes are the targets of the 11 differentially expressed miRNAs. Besides, 47 novel miRNA candidates were predicted in these two samples. Our results provided valuable information for understanding the molecular mechanism of the transition to functional queens.     

Genes that code for T cell signaling proteins establish transcriptional regulatory networks during thymus ontogeny

Gene expression profiling by cDNA microarrays during murine thymus ontogeny has contributed to dissecting the large-scale molecular genetics of T cell maturation. Gene profiling, although useful for characterizing the thymus developmental phases and identifying the differentially expressed genes, does not permit the determination of possible interactions between genes. In order to reconstruct genetic interactions, on RNA level, within thymocyte differentiation, a pair of microarrays containing a total of 1,576 cDNA sequences derived from the IMAGE MTB library was applied on samples of developing thymuses (14-17 days of gestation). The data were analyzed using the GeneNetwork program. Genes that were previously identified as differentially expressed during thymus ontogeny showed their relationships with several other genes. The present method provided the detection of gene nodes coding for proteins implicated in the calcium signaling pathway, such as Prrg2 and Stxbp3, and in protein transport toward the cell membrane, such as Gosr2. The results demonstrate the feasibility of reconstructing networks based on cDNA microarray gene expression determinations, contributing to a clearer understanding of the complex interactions between genes involved in thymus/thymocyte development.     

筛选差异表达基因方法的新进展

了解不同细胞或同类细胞在不同发育阶段、不同生理状态下的基因表达状况,可以为研究生命活动过程提供重要信息。以差别筛选、削减杂交等基本方法为出发点,研究基因表达差异的方法不断完善,先后出现了DDRT—PCR、RDA、SSH、cDNA微阵列(基因芯片)、基因表达的系统分析(SAGE)等技术。本着重对这些方法的优缺点及改进进行论述和评介,并对技术的发展趋势进行了分析。     

Gene expression profiling of human diseases by serial analysis of gene expression

Until recently, the approach to understanding the molecular basis of complex syndromes such as cancer, coronary artery disease, and diabetes was to study the behavior of individual genes. However, it is generally recognized that expression of a number of genes is coordinated both spatially and temporally and that this coordination changes during the development and progression of diseases. Newly developed functional genomic approaches, such as serial analysis of gene expression (SAGE) and DNA microarrays have enabled researchers to determine the expression pattern of thousands of genes simultaneously. One attractive feature of SAGE compared to microarrays is its ability to quantify gene expression without prior sequence information or information about genes that are thought to be expressed. SAGE has been successfully applied to the gene expression profiling of a number of human diseases. In this review, we will first discuss SAGE technique and contrast it to microarray. We will then highlight new biological insights that have emerged from its application to the study of human diseases.     

Multiomics analysis on DNA methylation and the expression of both messenger RNA and microRNA in lung adenocarcinoma

Lung adenocarcinoma (LUAD) poses a significant threat to public health worldwide, while the genetic and epigenetic abnormalities involved in the oncogenesis of LUAD remains unknown. This study aimed to identify and validate key genes during the development and progression of LUAD by multiomics analysis. First, Empirical Analysis of Digital Gene Expression Data in R (EdgeR) was used to identify differentially regulated genes between normal samples and LUAD samples. Then significance analysis of microarrays (SAM) was used to identify differentially methylated genes and regulated microRNAs (miRNAs) between normal samples and LUAD samples. Following that, Kyoto Encyclopedia of Genes and Genomes (KEGG)-enrichment analysis was used to analyze the function that these genes enriched in. A total of 4,816 genes, 419 miRNAs, and 4,476 methylated genes that were significantly differentially expressed corresponding to the normal tissues in LUAD were obtained, and some of the pathways these genes enriched in were the same. Moreover, 255 genes differentially methylated and expressed at the same time were also found, and these 255 genes were the target genes of the miRNAs differentially expressed in LUAD. Finally, nine genes (BRCA1, COL1A1, ESR1, FGFR2, HNF4A, IGFBP3, MET, MMP3, and PAK1) network analysis, and two of which were found to be related to the survival of LUAD patients. In summary, a total of nine genes that may play important roles in the development of LUAD were identified, and two (PAK1 and FGFR2) of them can be served as prognostic biomarkers for LUAD patients. The genes found in this study played different roles in the tumor progression of LUAD, indicating these genes may be considered as potential target genes for LUAD treatment.     

Integrative analysis and validation of robust gene signature in lung cancer

We studied robust gene signature (RGS) in lung cancer by using an approach of integrating a highly diverse collection of cancer genome-wide datasets, which were six public microarray datasets, one pair of Suppression Subtractive Hybridization EST library, one pair of Serial Analysis of Gene Expression (SAGE) experiments, and 191 Loss of Heterozygosity (LOH) reports obtained from 388 publications. Among the 109 RGS genes identified from our study, 14 of the 15 reported differentially expressed genes (DEGs) based on literature verification were consistent with our predictions. Out of the remaining 94 genes that were not reported as DEGs in lung cancer by any publication, we randomly picked eight and verified their expression in lung cancer versus normal tissues by semi-quantitative RT-PCR amplification, and all showed consistent expression pattern with our findings. System assessment analysis revealed that our integrative method had an accuracy of 95% and a correlation coefficients value of 0.92.