Correlation analysis between single-nucleotide polymorphism and expression arrays in gliomas identifies potentially relevant target genes

Abstract

Primary brain tumors are a major cause of cancer mortality in the United States. Therapy for gliomas, the most common type of primary brain tumors, remains suboptimal. The development of improved therapeutics will require greater knowledge of the biology of gliomas at both the genomic and transcriptional levels. We have previously reported whole genome profiling of chromosome copy number alterations (CNA) in gliomas, and now present our findings on how those changes may affect transcription of genes that may be involved in tumor induction and progression. By calculating correlation values of mRNA expression versus DNA copy number average in a moving window around a given RNA probe set, biologically relevant information can be gained that is obscured by the analysis of a single data type. Correlation coefficients ranged from -0.6 to 0.7, highly significant when compared with previous studies. Most correlated genes are located on chromosomes 1, 7, 9, 10, 13, 14, 19, 20, and 22, chromosomes known to have genomic alterations in gliomas. Additionally, we were able to identify CNAs whose gene expression correlation suggests possible epigenetic regulation. This analysis revealed a number of interesting candidates such as CXCL12, PTER, and LRRN6C, among others. The results have been verified using real-time PCR and methylation sequencing assays. These data will further help differentiate genes involved in the induction and/or maintenance of the tumorigenic process from those that are mere passenger mutations, thereby enriching for a population of potentially new therapeutic molecular targets.

Figure 1

A. Schema of mapping expression and SNP probesets. Red rectangles represent windows around an expression probeset to calculate mean copy number for included SNPs. It is possible that some probesets have no SNPs in a window. B. Percentage of probesets with no SNPs in a window vs. window size for both Xba and Hind arrays. C. Distribution of expression probesets by number of SNPs in a window of 1 Mbp.

Figure 2

Scatterplot of several genes with both positive (top row) and negative (bottom row) correlation coefficients between expression and copy number. Genes with many samples having amplification are grouped on the two left columns; genes with many samples having deletions, on the two right columns.

Figure 3

Scatterplot of several genes selected as candidates for epigenetic regulations as determined by analysis of areas of LOH where mRNA expression levels are substantially lower than what would have been predicted based on the gene dosage leading to a lower, but positive, correlation coefficient. Red outline mark samples with Loss of Heterozygosity. Box-and-whisker diagram on the right of each plot shows expression distribution in non-tumor samples. Expression values are shown as a log2 of ratio to the median of non-tumor samples.

Figure 4

Validation of mRNA expression levels and promoter methylation for selected genes. A. Correlation between expression values from microarray experiment and real-time RT-PCR for 6 genes. Pearson correlation coefficients are shown in parenthesis on the top of each plot. B. Bisulphite sequencing of CXCL12 gene in 5 selected glioma samples (see text). Percentage of methylated colonies (more than 25% of CpG islands are methylated) was calculated from 10–16 individual colonies. Black dots indicate CpG islands methylated in more then 50% of colonies, white dots – less then 50% and grey dots – 50%. Control samples (unmethylated allele) are shown separately. Numbers below dots indicate genomic position of amplified fragment with respect to gene transcriptional start sites. Far right panel shows location of assayed samples in the context of CNA/expression correlation. Black dots represent samples with LOH and gray dots show all other samples. Black outline and label indicate samples used for methylation study. C. Bisulphite sequencing of PTER gene in 5 selected glioma samples. Control sample (unmethylated allele) is shown separately.