Expressogram: A Visualization of Cytogenetic Landscape in Cancer Samples Using Gene Expression Microarrays


In cancer genomes, there are frequent copy number aberration (CNA) events, some of which are believed to be tumori-genic. While copy numbers can be detected by a number of technologies, e.g., SNP arrays, their relations with gene expressions are not well clarified. Here, we describe an approach to visualize the global relations between copy numbers and gene expressions using expression microarrays. We mapped the gene expression signals detected by microar-ray probesets onto a reference human genome, the RefSeq, based on their annotated physical positions, resulting in a landscape that we called expressogram. To study the expressograms under various conditions and their relations with cytogenetic events, such as CNAs, we obtained three classes of array samples, namely samples of a cancer (e.g., liver cancer), normal samples in the same tissue, and normal samples of other tissues. We developed a Bayesian based algorithm to estimate a background signal from the latter two sources for the cancer samples. By subtracting the estimated background from the raw signals of the cancer samples, and subjecting the differences to a kernel-based smoothing scheme, we produced an expressogram that shows strong consistency with the copy numbers. This indicates that copy numbers are on average positively correlated with and have strong impacts on gene expressions. To further explore the applicability of these findings, we submit the expressograms to the significant CNA detection algorithm GISTIC. The results strongly indicate that expressogram can also be used to infer copy number events and significant regions of CNA affected dysregulation.

Share and Cite:

Chen, P. and Hung, Y. (2013) Expressogram: A Visualization of Cytogenetic Landscape in Cancer Samples Using Gene Expression Microarrays. Engineering, 5, 496-501. doi: 10.4236/eng.2013.510B102.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] M. Baudis, “Genomic Imbalances in 5918 Malignant Epithelial Tumors: An Explorative Meta-Analysis of Chromosomal CGH data,” BMC Cancer, Vol. 7, 2007, p. 226.
[2] J. Li, K. Wang, et al., “DNA Copy Number Aberrations in Breast Cancer by Array Comparative Genomic Hybridization,” Genomics Proteomics Bioinformatics, Vol. 7, No. 1-2, 2009, pp. 13-24.
[3] F. Rapaport and C. Leslie, “Determining Frequent Patterns of Copy Number Alterations in Cancer,” PLoS One, Vol. 5, No. 8, 2010, p. e12028.
[4] M. R. Stratton, P. J. Campbell and P. A. Futreal, “The Cancer Genome,” Nature, Vol. 458, No. 7239, 2009, pp. 719-724.
[5] R. Beroukhim, G. Getz, et al., “Assessing the Significance of Chromosomal Aberrations in Cancer: Methodology and Application to Glioma,” Proceedings of the National Academy of Sciences of the United States of America, Vol. 104, No. 50, 2007, pp. 20007-20012.
[6] C. N. Henrichsen, E. Chaignat and A. Reymond, “Copy Number Variants, Diseases and Gene Expression,” Human Molecular Genetics, Vol. 18, No. R1, 2009, pp. R1- R8.
[7] M. Kool, J. Koster, et al., “Integrated Genomics Identifies Five Medulloblastoma Subtypes with Distinct Genetic Profiles, Pathway Signatures and Clinicopathological Features,” PLoS One, Vol. 3, No. 8, 2008, p. e3088.
[8] P. Michalak, “Coexpression, Coregulation, and Cofunctionality of Neighboring Genes in Eukaryotic Genomes,” Genomics, Vol. 91, No. 3, 2008, pp. 243-248.
[9] S. Colella, C. Yau, et al., “QuantiSNP: An Objective Bayes Hidden-Markov Model to Detect and Accurately Map Copy Number Variation Using SNP Genotyping Data,” Nucleic Acids Research, Vol. 35, No. 6, 2007, pp. 2013- 2025.
[10] M. A. Sanders, R. G. Verhaak, et al., “SNPexpress: Integrated Visualization of Genome-Wide Genotypes, Copy Numbers and Gene Expression Levels,” BMC Genomics, Vol. 9, 2008, p. 41.
[11] S. Theodoridis and K. Koutroumbas, “Pattern Recognition,” 3rd Edition, Academic Press, San Diego, 2006.
[12] M. G. Schimek, “Smoothing and Regression: Approaches, Computation, and Application,” Wiley Series in Probability and Statistics Applied Probability and Statistics Section, Wiley, New York, 2000.
[13] D. Y. Chiang, A. Villanueva, et al., “Focal Gains of VEGFA and Molecular Classification of Hepatocellular Carcinoma,” Cancer Research, Vol. 68, No. 16, 2008, pp. 6779-6788.
[14] R. A. Irizarry, B. Hobbs, et al., “Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data,” Biostatistics, Vol. 4, No. 2, 2003, pp. 249-264.
[15] “CNAT4.0: Copy Numbers and Loss of Heterozygosity Estimation Algorithms for the Genechip Human Mapping 10/50/100/250/500k Array Set,” Affymetrix Inc., Tech. Rep., 2007.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.