Correlation signature of the macroscopic states of the gene regulatory network in cancer

Although cancer types differ substantially, many cancers share common gene expression signatures. Consistent with this observation, we find convergent and representative distributions and correlation vectors that are distinct in cancer and noncancer ensembles. These differences originate in many genes, but comparatively few genes account for the major differences. We identify genes with different combinatorial regulation in cancer and noncancer as indicated by significant differences in their correlation vectors. Among the identified genes are many established oncogenes and apoptotic genes (such as members of the Bcl-2, the MAPK, and the Ras families) and new candidate oncogenes. Our findings expand and complement the tumorigenic role of up and down regulation of these genes by emphasizing cancer-specific changes in their couplings and correlation patterns at genome-wide level that are independent from their mean levels of expression in cancer cells. Given the central role of these genes in defining the cancerous state it may be worth investigating them and the differences in their combinatorial regulation for developing wide-spectrum anticancer drugs.

[1]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[2]  Christian A. Rees,et al.  Systematic variation in gene expression patterns in human cancer cell lines , 2000, Nature Genetics.

[3]  E. Devilard,et al.  Gene expression profiles of poor-prognosis primary breast cancer correlate with survival. , 2002, Human molecular genetics.

[4]  Eytan Domany,et al.  Coupled Two-way Clustering Analysis of Breast Cancer and Colon Cancer Gene Expression Data , 2002, Bioinform..

[5]  Eytan Domany,et al.  Classification of human astrocytic gliomas on the basis of gene expression: a correlated group of genes with angiogenic activity emerges as a strong predictor of subtypes. , 2003, Cancer research.

[6]  E. Lander,et al.  A molecular signature of metastasis in primary solid tumors , 2003, Nature Genetics.

[7]  Ash A. Alizadeh,et al.  Gene Expression Signature of Fibroblast Serum Response Predicts Human Cancer Progression: Similarities between Tumors and Wounds , 2004, PLoS biology.

[8]  R. Tibshirani,et al.  Gene expression profiling identifies clinically relevant subtypes of prostate cancer. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Stefan Michiels,et al.  Prediction of cancer outcome with microarrays: a multiple random validation strategy , 2005, The Lancet.

[10]  A. Nobel,et al.  The molecular portraits of breast tumors are conserved across microarray platforms , 2006, BMC Genomics.

[11]  Donald E Ingber,et al.  A non-genetic basis for cancer progression and metastasis: self-organizing attractors in cell regulatory networks. , 2006, Breast disease.

[12]  L. Ein-Dor,et al.  Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Jeffrey T. Chang,et al.  Oncogenic pathway signatures in human cancers as a guide to targeted therapies , 2006, Nature.

[14]  Donald Geman,et al.  Large-scale integration of cancer microarray data identifies a robust common cancer signature , 2007, BMC Bioinformatics.

[15]  D. Sabatini,et al.  Cancer Cell Metabolism: Warburg and Beyond , 2008, Cell.