Integrated analysis of copy number variation and genome-wide expression profiling in colorectal cancer tissues

Abstract

Integrative analyses of multiple genomic datasets for selected samples can provide better insight into the overall data and can enhance our knowledge of cancer. The objective of this study was to elucidate the association between copy number variation (CNV) and gene expression in colorectal cancer (CRC) samples and their corresponding non-cancerous tissues. Sixty-four paired CRC samples from the same patients were subjected to CNV profiling using the Illumina HumanOmni1-Quad assay, and validation was performed using multiplex ligation probe amplification method. Genome-wide expression profiling was performed on 15 paired samples from the same group of patients using the Affymetrix Human Gene 1.0 ST array. Significant genes obtained from both array results were then overlapped. To identify molecular pathways, the data were mapped to the KEGG database. Whole genome CNV analysis that compared primary tumor and non-cancerous epithelium revealed gains in 1638 genes and losses in 36 genes. Significant gains were mostly found in chromosome 20 at position 20q12 with a frequency of 45.31% in tumor samples. Examples of genes that were associated at this cytoband were PTPRT, EMILIN3 and CHD6. The highest number of losses was detected at chromosome 8, position 8p23.2 with 17.19% occurrence in all tumor samples. Among the genes found at this cytoband were CSMD1 and DLC1. Genome-wide expression profiling showed 709 genes to be up-regulated and 699 genes to be down-regulated in CRC compared to non-cancerous samples. Integration of these two datasets identified 56 overlapping genes, which were located in chromosomes 8, 20 and 22. MLPA confirmed that the CRC samples had the highest gains in chromosome 20 compared to the reference samples. Interpretation of the CNV data in the context of the transcriptome via integrative analyses may provide more in-depth knowledge of the genomic landscape of CRC.

Figure 1. Karyogram view of detected gains and losses regions across autosomes.

Gains are shown in green and losses are shown in red. The length of the horizontal bar corresponds to number of samples involved at the respective cytoband. Most of the gains were found at the long arm of chromosome 20 and losses were mainly observed at the short arm of chromosome 8.

Figure 2. Plots of principal components analysis (PCA) and hierarchical clustering of gene expression datasets.

(A) PCA scatter plot of CRC data. Each point represents sample. Points are colored by group status with blue representing non-cancerous epithelium and red representing tumor tissue. (B) Hierarchical clustering of mRNA profiles. Samples are indicated along the horizontal axis and grouped by the color bar between the dendogram and the heat map. Blue represents non-cancerous epithelium and red represents tumor tissue. Overall, there was a clear separation between non-cancerous epithelium and tumor tissue group when examined by both PCA (Figure 2A) and hierarchical clustering (Figure 2B).

Figure 3. Overlapped genes of integrated CRC datasets.

(A) Venn-diagram representing the common genes in CNV and gene expression datasets revealed 56 overlapping genes. (B) Circular map showing overview of CNVs and gene expression data. Chromosomes are shown in the color coded of the outer most ring. The second ring shows the distribution of gene expression profile. (red indicates up-regulated genes and green indicates down-regulated genes). The inner ring represents CN changes (red denotes gain in CN and green denotes loss in CN). The innermost ring shows the distribution of the two overlapping datasets.

Figure 4. Correlation of gene expression and CNV datasets in 15 paired subsets of CRC patients.

Scatter plots of gene expression (y-axis) correlating to copy number (x-axis) with differential expression & CN change in CRC for ARGLU1 (Figure 4A) and UGGT2 (Figure 4B) genes. Each dot represents one sample.

Figure 5. Cell cycle map from KEGG pathway.

Cell cycle was found to be the most significant enriched pathway (p<0.05). Genes involved (CDC25B, PCNA and p107/RBL1) shown in red color box indicates the genes to have gain in CN and increased level of expression.