Gr-rnf43 Regulation in Colorectal Cancer - Gr-rnf43 Regulation in Colorectal Cancer - Open Access Pub

In contrast to approaches that compare pair-wise control (i.e. normal) to treated (i.e. disease) samples, we compared colorectal cancer samples not only to a set of control samples but also against a wide range of samples and conditions to collect the differentially expressed genes and identify target genes. We identified specific genes for colorectal cancer and showed that they are significantly associated with colorectal cancer in the literature. Analysis of independent datasets revealed a significantly distinct expression pattern for glucocorticoid receptor (GR) and ring finger protein 43 (RNF43) in colorectal cancer samples. GR was downregulated whereas RNF43 was upregulated in colorectal cancer with respect to various conditions in different datasets. In HCT116 colorectal cancer cell line, knock-down of GR levels with siRNA resulted in increased RNF43 levels, suggesting that GR might be a negative regulator of RNF43. Our study suggests that the downregulation of GR might be involved in the upregulation of RNF43 in colorectal cancer. DOI10.14302/issn.2326-0793.jpgr-16-941 Analyses of gene expression levels generally focus on differential expression between two conditions. In cancer studies the comparison would be tumor vs. normal samples, long vs. poor surviving samples, or metastatic vs. non-metastatic samples 1, 2. However, the analysis of a multi-conditional large-scale gene expression dataset has provided useful information, such as identifying genes with switch-like behavior, which are not readily recovered using pair-wise data analysis 3. Analyzing the expression levels of a gene across a diverse condition space provides a better measure of the specificity of the gene for a particular condition 4. To determine whether a gene of interest for a condition is specific to that condition and differentially expressed, we need to have an understanding of the distribution of “normal” expression levels of that gene. This set of possible expression levels for a gene can be more readily obtained in a large-scale multi-condition dataset, which can be used to determine the “normal” expression distribution of a gene, and in turn help better define the gene expression levels that are “abnormal”. For example p53, a well-known gene mutated commonly in many cancer types, does not appear to be differentially expressed in a small dataset that compares only two conditions, i.e. control vs. γ-irradiation samples (wherein the DNA-damage response gene p53 is known to be activated), but was identified in a large dataset comprising of multiple conditions that was not