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

Abstract

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.

Fig. 1.

Distributions of pairwise correlations (ρ_ij) for subensembles from cancer, non-cancer and for fully randomized expression data (A) and convergence of the distributions (B).

Fig. 2.

Correlation vectors. Error bars correspond to the standard deviations from 10 subensembles. (A) Length (Norm). (B) Fractional difference in coupling, ΔC_i. (C) Projection on the body diagonal (mean). (D) Variance.

Fig. 3.

Comparisons of correlation vectors. (A and B) Distributions of angles/correlations (A) and Euclidean distances (B) between v_i calculated with all genes within and between ensembles. (C and D) Decrease in the angle (C) and distance (D) between v_i(n) and v_i(c) with the gradual and systematic removal of genes whose v_i are most distinct between cancer and noncancer.

Fig. 4.

Frequency of genes maximizing the separation between v_i(n) and v_i(c).

Fig. 5.

Clusters of correlation vectors. (A) Clustered correlation matrix for noncancer. (B) Clustered correlation matrix for cancer. (C) Overlap among clusters of correlation vectors between cancer and noncancer.