Tu1668 Highly Metastatic Murine Colorectal Cancer Cells Share Transcriptome Identity Indicative of Cancer Cell Fusion With Macrophages

Abstract

G A A b st ra ct s a*sqrt(X(i,j) > μ(i) and low if, X(i,j) + a*sqrt(X(i,j) < μ(i), where a=6. Genes were grouped into pathways using Ingenuity pathway analysis. RESULTS: Based on their relative expression levels at any one of 4 time points studied, all the genes from the raw RNAseq reads were grouped into 24 gene sets (table1). Four gene-sets figure (1 a-d). appear most interesting and coincide with change of morphology in the BEC-40W and loss of adherence to substrate (colony formation) observed in BEC-60W cells. FPKM data analysis supported upregulation of stem cell, immune response and epigenetic signatures, oxidative phosphorylation and stress response pathways. Pathway analysis revealed these genes are members of VEGF, RB, PTEN, ATF2, TP53 and other known oncogenic pathways. CONCLUSIONS: A statistical model identified four interesting gene sets from RNA sequencing data of the BEC model. These gene sets comprise oncogenes, tumor suppressors as well as regulators of signal transduction that correlated with and may be responsible for the transformed phenotype observed in BEC-40W and BEC-60W cells. Our observation from the BEC model corroborate that chronic exposure to B4 leads to genetic changes that can promote carcinogenesis in BE. Characterization of these potential candidate genes and pathways may lead to innovative biomarkers and therapeutic targets for potentially progressive BE. Table 1: The number of genes in each of the four interesting states (out of the 14 possible states). The expression state is denoted by binary digits 1=upregulation, 0= down regulation.