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GS75657 • Expressed Genes called significant in FACS purified CRC cells (P<0.01, rank products)
Expression of 103 genes were detected in purified samples, but significantly downregulated in whole samples (P<0.05), of which 33 of these were significant at P<0.01 (Table 3) which are displayed in the Heatmaps
Expr.Purif Biop. Co.Can.
Smith MJ, Culhane AC, Donovan M, Coffey JC, Barry BD, Kelly MA, Higgins DG, Wang JH, Kirwan WO, Cotter TG, Redmond HP
Analysis of differential gene expression in colorectal cancer and stroma using fluorescence-activated cell sorting purification.
British journal of cancer
Vol 100, pp. 1452-64
Tumour stroma gene expression in biopsy specimens may obscure the expression of tumour parenchyma, hampering the predictive power of microarrays. We aimed to assess the utility of fluorescence-activated cell sorting (FACS) for generating cell populations for gene expression analysis and to compare the gene expression of FACS-purified tumour parenchyma to that of whole tumour biopsies. Single cell suspensions were generated from colorectal tumour biopsies and tumour parenchyma was separated using FACS. Fluorescence-activated cell sorting allowed reliable estimation and purification of cell populations, generating parenchymal purity above 90%. RNA from FACS-purified and corresponding whole tumour biopsies was hybridised to Affymetrix oligonucleotide microarrays. Whole tumour and parenchymal samples demonstrated differential gene expression, with 289 genes significantly overexpressed in the whole tumour, many of which were consistent with stromal gene expression (e.g., COL6A3, COL1A2, POSTN, TIMP2). Genes characteristic of colorectal carcinoma were overexpressed in the FACS-purified cells (e.g., HOX2D and RHOB). We found FACS to be a robust method for generating samples for gene expression analysis, allowing simultaneous assessment of parenchymal and stromal compartments. Gross stromal contamination may affect the interpretation of cancer gene expression microarray experiments, with implications for hypotheses generation and the stability of expression signatures used for predicting clinical outcomes.
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