A hallmark genomic feature of human brain tumors is the presence of multiple complex structural and numerical chromosomal aberrations that result in altered gene dosages. These genetic alterations lead to widespread, genome-wide gene expression changes. Both gene expression as well as gene copy number profiles can be assessed on a large scale using microarray methodology. The integration of genetic data with gene expression data provides a particularly effective approach for cancer gene discovery. Utilizing an array of bioinformatics tools, we describe an analysis algorithm that allows for the integration of gene copy number and gene expression profiles as a first-pass means of identifying potential cancer gene targets in human (brain) tumors. This strategy combines circular binary segmentation for the identification of gene copy number alterations, and gene copy number and gene expression data integration with a modification of signal-to-noise ratio computation and random permutation testing. We have evaluated this approach and confirmed its efficacy in the human glioma genome.