ed using Gitools. The scale ranges from the minimum value of 0, indicating low relative expression, to the mid-value of 1 indicating an average level of expression, to the maximum value of 10 indicating high relative expression. Columns represent different patient samples. Expression of the KDM5A target gene BRD8 provides a readout of KDM5A transcript level. Sample annotation and expression values are presented in its association with HDACs may underlie cell sensitivity to the HDAC inhibitors TSA and SAHA. The involvement of KDM5A in transcriptional activation may be similar to the active role played by HDACs at the transcribed regions. Thus, a combination of HDAC inhibitors and KDM5A inhibitors may be preferable for treatment of cancer cells with KDM5A overexpression. The homeotic gene BRD8, which reflects KDM5A activity, would be useful for testing the effects of KDM5A inhibitors. It is unclear if other KDM5 family members can be found at active genes. The KDM5B enables to catalyze demethylation of the same mark as KDM5A, however, it appears to have different expression pattern and therefore serve specific requirements in particular tissues. While our results are consistent with previous findings on upand downregulation of single enzymes, they represent an initial step to decipher enzyme combinations that can be co-regulated in cancer. Our data suggest that, in the absence of appropriate inhibitors to a causative HMT or HDM, pharmacological targeting of co-regulated HDM/HMT in the same gene expression signature could be applied to selectively target tumor cells. Interestingly, we used ChIP-on-chip, ChIPseq and gene expression data from non-matching cell types and still revealed correlations. This unexpected result suggests that it wouldn’t be too challenging a task to conduct a gene expression analysis of the cancer epigenome since genome-wide data from different studies can be used. However, we anticipate that the outcome of correlation analyses will depend on whether the samples are “1635054 from a more differentiated tissue or have stem cell 
properties. Because the likelihood of detecting a gene expression signature increases with the number of samples, creating profiles for a particular tumor type using large datasets will achieve more precision. Materials and Methods Normalized mRNA expression data from 73 normal human tissues were downloaded from the BioGPS database. Raw mRNA microarray expression data from various cancer cell lines were downloaded from the National Cancer Institute’s caBIG/caArray databases. Three replicates were available for each cell line. The mean expression was taken and intensity values were transformed to log2 values. When more than one probe was present for the same gene, the mean value was determined. However, in some cases, probes that did not “8496905 have a good correlation of expression with respect to other probes of the same gene, or probes that did not express across any of the samples, were not included. Log2-converted mean absolute expression values were represented in a color-coded heatmap using the program Matrix2Png . For the GeneAtlas and GSK data in 9 August 2011 | Volume 6 | Issue 8 | e24023 Co-Regulation of Histone-Modifying Enzymes Pearson’s correlation coefficient of HDM and HMT expression profiles in normal tissues and cancer cell lines was analyzed using Gitools. We used the TissueScan Cancer Survey Tissue qPCR panel 384-1, which 752187-80-7 manufacturer contains 381 human tumor tissues. Some of the samples are normal non-ma
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