Differential expression of selected histone modifier genes in human solid cancers

Abstract

Background: Post-translational modification of histones resulting in chromatin remodelling plays a key role in the regulation of gene expression. Here we report characteristic patterns of expression of 12 members of 3 classes of chromatin modifier genes in 6 different cancer types: histone acetyltransferases (HATs)- EP300, CREBBP, and PCAF; histone deacetylases (HDACs)- HDAC1, HDAC2, HDAC4, HDAC5, HDAC7A, and SIRT1; and histone methyltransferases (HMTs)- SUV39H1and SUV39H2. Expression of each gene in 225 samples (135 primary tumours, 47 cancer cell lines, and 43 normal tissues) was analysedby QRT-PCR, normalized with 8 housekeeping genes, and given as a ratio by comparison with a universal reference RNA.

Results: This involved a total of 13,000 PCR assays allowing for rigorous analysis by fitting a linear regression model to the data. Mutation analysis of HDAC1, HDAC2, SUV39H1, and SUV39H2 revealed only two out of 181 cancer samples (both cell lines) with significant coding-sequence alterations. Supervised analysis and Independent Component Analysis showed that expression of many of these genes was able to discriminate tumour samples from their normal counterparts. Clustering based on the normalized expression ratios of the 12 genes also showed that most samples were grouped according to tissue type. Using a linear discriminant classifier and internal cross-validation revealed that with as few as 5 of the 12 genes, SIRT1, CREBBP, HDAC7A, HDAC5 and PCAF, most samples were correctly assigned.

Conclusion: The expression patterns of HATs, HDACs, and HMTs suggest these genes are important in neoplastic transformation and have characteristic patterns of expression depending on tissue of origin, with implications for potential clinical application.

Figure 1

Schema of RT-PCR data analysis.

Figure 2

Normalized relative expression ratios of genes across all samples. Primary samples are on the left panels and grouped along the horizontal axis by tissue type according to the following colour codes: renal tumours (LIGHT BLUE), renal normals (DARK BLUE), colorectal tumours (DARK GREEN), colorectal normals (LIGHT GREEN), breast tumours (PINK), breast normals (RED), bladder tumours (YELLOW), bladder normals (ORANGE), glioblastomas (BLACK), ovarian tumours (GREY). Cell line samples are on the right panels and are also grouped by tissue type: ovarian (GREY), breast (PINK) and colorectal (DARK GREEN). The y axis shows the expression ratios on a log2 scale. The horizontal dashed lines represent an averaged one standard deviation (-0.4 to 0.4) gaussian noise level arising from unstable housekeeping gene expression across the whole sample set. The vertical distance between the two dashed lines represents therefore a zero centred 70% average confidence interval for all the expression values.

Figure 3

Cluster analysis of expression matrix of 12 genes across primary samples using the ensemble learning algorithm. Red denotes overexpression, green underexpression. See Figure 2 for detailed expression values.