Integrated Analysis of Copy Number Variation and Genome-Wide Expression Profiling in Colorectal Cancer Tissues

<div><p>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 <i>PTPRT</i>, <i>EMILIN3</i> and <i>CHD6</i>. 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 <i>CSMD1</i> and <i>DLC1</i>. 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.</p></div

Top 10 significant copy number gain regions.

<p>Top 10 significant copy number gain regions.</p

Cell cycle map from KEGG pathway.

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

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

<p>(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 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092553#pone-0092553-g002" target="_blank">Figure 2A</a>) and hierarchical clustering (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092553#pone-0092553-g002" target="_blank">Figure 2B</a>).</p

table_1_Integrated Characterization of MicroRNA and mRNA Transcriptome in Papillary Thyroid Carcinoma.xlsx

<p>The incidence rate of papillary thyroid carcinoma (PTC) has rapidly increased in the recent decades, and the microRNA (miRNA) is one of the potential biomarkers in this cancer. Despite good prognosis, certain features such as lymph node metastasis (LNM) and BRAF V600E mutation are associated with a poor outcome. More than 50% of PTC patients present with LNM and BRAF V600E is the most common mutation identified in this cancer. The molecular mechanisms underlying these features are yet to be elucidated. This study aims to elucidate miRNA–genes interaction networks in PTC with or without LNM and to determine the association of BRAF V600E mutation with miRNAs and genes expression profiles. Next generation sequencing was performed to characterize miRNA and gene expression profiles in 20 fresh frozen tumor and the normal adjacent tissues of PTC with LNM positive (PTC LNM-P) and PTC without LNM (PTC LNN). BRAF V600E was genotyped using Sanger sequencing. Bioinformatics integration and pathway analysis were performed to determine the regulatory networks involved. Based on network analysis, we then investigated the association between miRNA and gene biomarkers, and pathway enrichment analysis was performed to study the role of candidate biomarkers. We identified 138 and 43 significantly deregulated miRNAs (adjusted p value < 0.05; log2 fold change ≤ −1.0 or ≥1.0) in PTC LNM-P and PTC LNN compared to adjacent normal tissues, respectively. Ninety-six miRNAs had significant expression ratios of 3p-to-5p in PTC LNM-P as compared to PTC LNN. In addition, ribosomal RNA-reduced RNA sequencing analysis revealed 699 significantly deregulated genes in PTC LNM-P versus normal adjacent tissues, 1,362 genes in PTC LNN versus normal adjacent tissue, and 1,576 genes in PTC LNM-P versus PTC LNN. We provide the evidence of miRNA and gene interactions, which are involved in LNM of papillary thyroid cancer. These findings may lead to better understanding of carcinogenesis and metastasis processes. This study also complements the existing knowledge about deregulated miRNAs in papillary thyroid carcinoma development.</p

Distribution of clinicopathological features among 64 paired CRC sample.

<p>Distribution of clinicopathological features among 64 paired CRC sample.</p

Validation of copy number profiling by MLPA.

<p>Validation of copy number profiling by MLPA.</p

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

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

Categories of overlapping genes.

<p>Categories of overlapping genes.</p

Overlapped genes of integrated CRC datasets.

<p>(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.</p