Canis lupus familiaris
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA296756 | ENA |
REPOSITORIES: ENA
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Project description:The aim of our study was to evaluate gene expression of canine mammary cancer stem-like cells co-cultured with tumor associated macrophages. Two canine mammary cancer cell lines (CMT-U27 and P114) were stained using anti-Sca1 (stem cell antigen 1), anti-EpCAM (Epithelial cell adhesion molecule) and anti-CD44 antibody. The FACS analysis showed 0,02-0,05% of Sca1+/EpCAM+/CD44+ in each of the cell line. Cancer stem-like cells were collected using FACS Aria II then co-cultured with tumor associated macrophages and used for further analysis of gene expression ( using Agilent Gene Expression Hybridization Kit ). Canine mammary cancer cell lines were stained using anti-Sca1 (stem cell antigen 1), anti-EpCAM (Epithelial cell adhesion molecule) and anti-CD44 antibodies. Next using FACS Aria II and Sca1+/EpCAM+/CD44+ cells were collected and co-cultured with tumor associated macrophages. Then, total RNA was isolated and hybridized at Gene Expression microarray.
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Project description:Comparative oncology is a developing research discipline that is being used to assist our understanding of human neoplastic diseases. Companion canines are a preferred animal oncology model due to spontaneous tumor development and similarity to human disease at the pathophysiological level. We use a paired RNA sequencing (RNA-Seq)/microarray analysis of a set of four normal canine lymph nodes and ten canine lymphoma fine needle aspirates to identify technical biases and variation between the technologies and convergence on biological disease pathways. Surrogate Variable Analysis (SVA) provides a formal multivariate analysis of the combined RNA-Seq/microarray data set. Applying SVA to the data allows us to decompose variation into contributions associated with transcript abundance, differences between the technology, and latent variation within each technology. A substantial and highly statistically significant component of the variation reflects transcript abundance, and RNA-Seq proved more sensitive for detection of transcripts expressed at low levels. Latent random variation among RNA-Seq samples is also distinct in character from that impacting microarray samples. In particular, we observed variation between RNA-Seq samples that reflects transcript GC content. Platform-independent variable decomposition without a priori knowledge of the sources of variation using SVA represents a generalizable method for accomplishing cross-platform data analysis. We identified genes differentially expressed between normal lymph nodes of disease free dogs and a subset of the diseased dogs diagnosed with B-cell lymphoma using each technology. There is statistically significant overlap between the RNA-Seq and microarray sets of differentially expressed genes. Analysis of overlapping genes in the context of biological systems suggests elevated expression and activity of PI3K signaling in B-cell lymphoma biopsies compared with normal biopsies, consistent with literature describing successful use of drugs targeting this pathway in lymphomas.
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