Decorin protein core affects the global gene expression profile of the tumor microenvironment in a triple-negative orthotopic breast carcinoma xenograft model

Decorin, a member of the small leucine-rich proteoglycan gene family, exists and functions wholly within the tumor microenvironment to suppress tumorigenesis by directly targeting and antagonizing multiple receptor tyrosine kinases, such as the EGFR and Met. This leads to potent and sustained signal attenuation, growth arrest, and angiostasis. We thus sought to evaluate the tumoricidal benefits of systemic decorin on a triple-negative orthotopic breast carcinoma xenograft model. To this end, we employed a novel high-density mixed expression array capable of differentiating and simultaneously measuring gene signatures of both Mus musculus (stromal) and Homo sapiens (epithelial) tissue origins. We found that decorin protein core modulated the differential expression of 374 genes within the stromal compartment of the tumor xenograft. Further, our top gene ontology classes strongly suggests an unexpected and preferential role for decorin protein core to inhibit genes necessary for immunomodulatory responses while simultaneously inducing expression of those possessing cellular adhesion and tumor suppressive gene properties. Rigorous verification of the top scoring candidates led to the discovery of three genes heretofore unlinked to malignant breast cancer that were reproducibly found to be induced in several models of tumor stroma. Collectively, our data provide highly novel and unexpected stromal gene signatures as a direct function of systemic administration of decorin protein core and reveals a fundamental basis of action for decorin to modulate the tumor stroma as a biological mechanism for the ascribed anti-tumorigenic properties

Comparison of regional gene expression differences in the brains of the domestic dog and human

Comparison of the expression profiles of 2,721 genes in the cerebellum, cortex and pituitary gland of three American Staffordshire terriers, one beagle and one fox hound revealed regional expression differences in the brain but failed to reveal marked differences among breeds, or even individual dogs. Approximately 85 per cent (42 of 49 orthologue comparisons) of the regional differences in the dog are similar to those that differentiate the analogous human brain regions. A smaller percentage of human differences were replicated in the dog, particularly in the cortex, which may generally be evolving more rapidly than other brain regions in mammals. This study lays the foundation for detailed analysis of the population structure of transcriptional variation as it relates to cognitive and neurological phenotypes in the domestic dog

CARMA: A platform for analyzing microarray datasets that incorporate replicate measures

BACKGROUND: The incorporation of statistical models that account for experimental variability provides a necessary framework for the interpretation of microarray data. A robust experimental design coupled with an analysis of variance (ANOVA) incorporating a model that accounts for known sources of experimental variability can significantly improve the determination of differences in gene expression and estimations of their significance. RESULTS: To realize the full benefits of performing analysis of variance on microarray data we have developed CARMA, a microarray analysis platform that reads data files generated by most microarray image processing software packages, performs ANOVA using a user-defined linear model, and produces easily interpretable graphical and numeric results. No pre-processing of the data is required and user-specified parameters control most aspects of the analysis including statistical significance criterion. The software also performs location and intensity dependent lowess normalization, automatic outlier detection and removal, and accommodates missing data. CONCLUSION: CARMA provides a clear quantitative and statistical characterization of each measured gene that can be used to assess marginally acceptable measures and improve confidence in the interpretation of microarray results. Overall, applying CARMA to microarray datasets incorporating repeated measures effectively reduces the number of gene incorrectly identified as differentially expressed and results in a more robust and reliable analysis

Statistical tests for differential expression in cDNA microarray experiments

Extracting biological information from microarray data requires appropriate statistical methods. The simplest statistical method for detecting differential expression is the t test, which can be used to compare two conditions when there is replication of samples. With more than two conditions, analysis of variance (ANOVA) can be used, and the mixed ANOVA model is a general and powerful approach for microarray experiments with multiple factors and/or several sources of variation

Effect of normalisation on detection of differentially expressed genes in cDNA microarray data analysis

Four different normalisation techniques were applied for the corrections of fluorescence data generated by a cDNA microarray experiment. Correction for inaccurate signals and possible bias induced by fluorescence intensity, background intensity and dye effect were used in different combinations. Results of the present study highlight a pronounced role for the normalisation techniques in the absolute number of genes different expressed and a low concordance between different methods. Moreover, a significant effect of the dependent variable used, mean or median fluorescence intensity, was observed

aFold – using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data

Data normalization and identification of significant differential expression represent crucial steps in RNA-Seq analysis. Many available tools rely on assumptions that are often not met by real data, including the common assumption of symmetrical distribution of up- and down-regulated genes, the presence of only few differentially expressed genes and/or few outliers. Moreover, the cut-off for selecting significantly differentially expressed genes for further downstream analysis often depend on arbitrary choices

Microarray experiments: Considerations for experimental design

Microarrays are useful tools to investigate the expression of thousands of genes rapidly. Some researchers remain reluctant to use the technology, however, largely because of its expense. Careful design of a microarray experiment is key to generating cost-effective results. This article explores issues that researchers face when embarking on a microarray experiment for the first time. These include decisions about which microarray platform is available for the organism of interest, the degree of replication (biological and technical) needed and which design (direct or indirect, loop or balanced block) is suitable