No abstract available.
Gene Expression Profiling* ; RNA ; Transcriptome*

Transcriptome analysis has been widely used to make biomarker panels to diagnose cancers. In breast cancer, the age of the patient has been known to be associated with clinical features. As clinical transcriptome data have accumulated significantly, we classified all human genes based on age-specific differential expression between normal and breast cancer cells using public data. We retrieved the values for gene expression levels in breast cancer and matched normal cells from The Cancer Genome Atlas. We divided genes into two classes by paired t test without considering age in the first classification. We carried out a secondary classification of genes for each class into eight groups, based on the patterns of the p-values, which were calculated for each of the three age groups we defined. Through this two-step classification, gene expression was eventually grouped into 16 classes. We showed that this classification method could be applied to establish a more accurate prediction model to diagnose breast cancer by comparing the performance of prediction models with different combinations of genes. We expect that our scheme of classification could be used for other types of cancer data.
Biomarkers ; Breast Neoplasms* ; Breast* ; Classification* ; Gene Expression ; Gene Expression Profiling ; Genome ; Humans ; Methods ; Transcriptome

We present a latent variable model-based approach to the analysis of gene expression patterns, coupled with topographic clustering. Aspect model, a latent variable model for dyadic data, is applied to extract latent patterns underlying complex variations of gene expression levels. Then a topographic clustering is performed to find coherent groups of genes, based on the extracted latent patterns as well as individual gene expression behaviors. Applied to cell cycle regulated genes of the yeast Saccharomyces cerevisiae, the proposed method could discover biologically meaningful patterns related with characteristic expression behavior in particular cell cycle phases. In addition, the display of the variation in the composition of these latent patterns on the cluster map provided more facilitated interpretation of the resulting cluster structure. From this, we argue that latent variable models, coupled with topographic clustering, are a promising tool for explorative analysis of gene expression data.
Cell Cycle ; Cluster Analysis* ; Gene Expression Profiling* ; Gene Expression* ; Saccharomyces cerevisiae ; Yeasts

PURPOSE: We sought to identify asthma-related genes and to examine the potential of these genes to predict asthma, based on expression levels. METHODS: The subjects were 42 asthmatics and 10 normal healthy controls. PBMC RNA was subjected to microarray analysis using a 35K array; t-tests were used to identify genes that were expressed differentially between the two groups. A multiple logistic regression analysis was applied to the differentially expressed genes, and area under the curve (AUC) values from receiver operating characteristic (ROC) curves were obtained. RESULTS: In total, 170 genes were selected using the following criteria: P< or =0.001 and > or =2-fold change. Among these genes, 57 were up-regulated and 113 were down-regulated in asthmatics versus normal controls. A multiple logistic regression analysis was done using more stringent criteria (P< or =0.001 and > or =5-fold change), and eight genes were selected as candidate asthma biomarkers. Using these genes, 255 models (2(8)-1) were generated. Among them, only 85 showed P< or =0.05 by multiple logistic regression analysis. Based on the AUCs from ROC curves for the 85 models, we found that the best model consisted of the genes MEPE, MLSTD1, and TRIM37. The model showed 0.9928 of the AUC with 98% sensitivity and 80% specificity. CONCLUSIONS: MEPE, MLSTD1, and TRIM37 may be useful biomarkers for asthma.
Area Under Curve ; Asthma ; Biomarkers ; Gene Expression ; Gene Expression Profiling ; Genetic Markers ; Logistic Models ; Microarray Analysis ; RNA ; ROC Curve

High-throughput transcriptome sequencing, also known as RNA sequencing (RNA-Seq), is a standard technology for measuring gene expression with unprecedented accuracy. Numerous bioconductor packages have been developed for the statistical analysis of RNA-Seq data. However, these tools focus on specific aspects of the data analysis pipeline, and are difficult to appropriately integrate with one another due to their disparate data structures and processing methods. They also lack visualization methods to confirm the integrity of the data and the process. In this paper, we propose an R-based RNA-Seq analysis pipeline called TRAPR, an integrated tool that facilitates the statistical analysis and visualization of RNA-Seq expression data. TRAPR provides various functions for data management, the filtering of low-quality data, normalization, transformation, statistical analysis, data visualization, and result visualization that allow researchers to build customized analysis pipelines.
Base Sequence ; Gene Expression ; Gene Expression Profiling ; Molecular Sequence Data ; Programming Languages ; Sequence Analysis, RNA ; Statistics as Topic ; Transcriptome

OBJECTIVE: Granulomatosis with polyangiitis (GPA), formally known as Wegener's granulomatosis, is a systemic vasculitis with necrotizing granulomatous inflammation. As treatment directed against tumor necrosis factor (TNF)-alpha failed in GPA, we investigated whether "TNF-alpha signature" (i.e. gene expression profile of TNF-alpha activation) was present in peripheral blood mononuclear cells (PBMCs) of patients with GPA. METHODS: Gene expression profiling was performed using total RNA from PBMCs of 41 patients with GPA and 23 healthy control subjects using the Illumina microarray technique. Gene set enrichment analysis (GSEA) was performed to detect the presence of TNF-alpha signature using the curated list C2-V3.0 by the Broad Institute. False discovery rate<0.05 was considered statistically significant. RESULTS: GSEA did not show significant enrichment of any TNF-alpha associated gene sets in GPA. Expression levels of genes up-regulated by TNF-alpha did not differ between healthy control subjects, patients in remission (Birmingham Vasculitis Activity Score [BVAS]=0), and those with active disease (BVAS> or =1). In addition, an unsupervised hierarchical clustering of those genes failed to cluster the samples into healthy control subjects and GPA in remission or with active disease. B cell activation signature was enriched in GPA patients. CONCLUSION: Absence of a TNF-alpha signature in PBMCs may suggest that TNF-alpha plays a less important role in the pathogenesis of GPA than previously accepted.
Gene Expression ; Gene Expression Profiling ; Humans ; Inflammation ; RNA ; Systemic Vasculitis ; Transcriptome ; Tumor Necrosis Factor-alpha* ; Vasculitis ; Wegener Granulomatosis

To identify genes that participate in the abortion process, normal pregnant uteri were compared to lipopolysaccharide (LPS)-induced abortion uteri. At day 6 of pregnancy, mice were treated with LPS at various time points to induce an abortion. Total RNAs were applied to a cDNA microarray to analyze genes with altered expression. At the early stage (2 hours) of LPS-induced abortion, upregulated genes were mainly composed of immune responsive genes, including Ccl4, Ccl2, Cxcl13, Gbp3, Gbp2, Mx2, H2-Eb1, Irf1 and Ifi203. Genes related to toll-like receptor signaling were also overexpressed. At late stages of abortion (12-24 hours), many genes were suppressed rather than activated, and these were mainly related to the extracellular matrix, cytoskeleton, and anti-apoptosis. Altered expression of several selected genes was confirmed by real time reverse transcription-polymerase chain reaction. The results demonstrated that many known genes were altered in the LPS-treated pregnant uterus, implying that the molecular mechanisms of the genes involved in LPS-induced abortion are complicated. Further analysis of this expression profile will help our understanding of the pathophysiological basis for abortion.
Animals ; Cytoskeleton ; Extracellular Matrix ; Gene Expression ; Gene Expression Profiling ; Mice ; Oligonucleotide Array Sequence Analysis ; Pregnancy ; RNA ; Toll-Like Receptors ; Uterus

BACKGROUND: Quantitative real-time polymerase chain reaction (qPCR) is the most reliable tool for gene expression studies. Selection of housekeeping genes (HKGs) that are having most stable expression is critical to carry out accurate gene expression profiling. There is no 'universal' HKG having stable expression in all kinds of tissues under all experimental conditions. METHODS: The present study aims to identify most appropriate HKGs for gene expression analysis in glioblastoma (GBM) samples. Based on literature survey, six most commonly used HKGs that are invariant in GBM were chosen. We performed qPCR using RNA from formalin fixed paraffin embedded GBM samples and normal brain samples to investigate the expression pattern of HPRT, GAPDH, TBP, B2M, B2M, RPL13A, and RN18S1 with different abundance. A simple Deltacycle threshold approach was employed to calculate the fold change. RESULTS: Our study shows that the expression of RPL13A and TBP were found to be most stable across all the samples and are thus suitable for gene expression analysis in human GBM. Except for TBP, none of the other conventionally used HKGs in GBM studies e.g., HPRT and GAPDH were found to be suitable as they showed variation in RNA expression. CONCLUSION: Validation of HKGs is therefore immensely specific for a particular experimental setup and is crucial in assessing any new setup.
Brain ; Formaldehyde ; Gene Expression Profiling ; Gene Expression* ; Genes, Essential* ; Glioblastoma* ; Humans ; Hypoxanthine Phosphoribosyltransferase ; Paraffin ; Real-Time Polymerase Chain Reaction* ; RNA

Human stem cells have been isolated from embryos as well as diverse somatic tissues. Much efforts have been made to apply those stem cells for therapeutic purpose. Molecular dissection of "stemness" and differentiation process will provide us a clue to further understanding of stem cell biology. The fate of stem cell is determined in part by internal regulation mediated by transcriptional control. A set of genes related to specific functions might be expressed and suppressed upon external or internal signaling. Therefore, it is necessary to identify the complete gene expression profile that defines the stem cell. Moreover, we should verify the potential of therapeutic stem cells regarding differentiation, specific function, tumorigenecity, and toxicity rigorously before the clinical application. At present we have only limited information on "stemness" and differentiation into specific lineages. In the genome era, we can draw a molecular portrait on these functional and morphologic changes along the differentiation, trans-differentiation as well as de-differentiation using the DNA chip technology. We need to accumulate database for gene expression at various steps of stem cell differentiation, which will enhance our knowledge for research on and medical application of stem cell.
Biology ; Embryonic Structures ; Gene Expression ; Gene Expression Profiling ; Genome ; Genomics ; Humans ; Oligonucleotide Array Sequence Analysis ; Stem Cells ; Transcriptome

The KA1 kainate receptor (KAR) subunit in the substantia gelatinosa (SG) of the trigeminal subnucleus caudalis (Vc) has been implicated in the processing of nociceptive information from the orofacial region. This study compared the expression of the KA1 KAR subunit in the SG of the Vc in juvenile, prepubescent and adult mice. RT-PCR, Western blot and immunohistochemistry analyses were used to examine the expression level in SG area. The expression levels of the KA1 KAR subunit mRNA and protein were higher in juvenile mice than in prepubescent or adult mice. Quantitative data revealed that the KA1 KAR subunit mRNA and protein were expressed at levels approximately two and three times higher, respectively, in juvenile mice than in adult mice. A similar expression pattern of the KA1 KAR subunit was observed in an immunohistochemical study that showed higher expression in the juvenile (59%) than those of adult (35%) mice. These results show that the KA1 KAR subunits are expressed in the SG of the Vc in mice and that the expression level of the KA1 KAR subunit decreases gradually with postnatal development. These findings suggest that age-dependent KA1 KAR subunit expression can be a potential mechanism of age-dependent pain perception.
Age Factors ; Animals ; Gene Expression Profiling ; Gene Expression Regulation, Developmental ; Mice ; Receptors, Kainic Acid/metabolism ; Substantia Gelatinosa/metabolism