Loss of connectivity in cancer co-expression networks

Abstract

Differential gene expression profiling studies have lead to the identification of several disease biomarkers. However, the oncogenic alterations in coding regions can modify the gene functions without affecting their own expression profiles. Moreover, post-translational modifications can modify the activity of the coded protein without altering the expression levels of the coding gene, but eliciting variations to the expression levels of the regulated genes. These considerations motivate the study of the rewiring of networks co-expressed genes as a consequence of the aforementioned alterations in order to complement the informative content of differential expression. We analyzed 339 mRNAomes of five distinct cancer types to find single genes that presented co-expression patterns strongly differentiated between normal and tumor phenotypes. Our analysis of differentially connected genes indicates the loss of connectivity as a common topological trait of cancer networks, and unveils novel candidate cancer genes. Moreover, our integrated approach that combines the differential expression together with the differential connectivity improves the classic enrichment pathway analysis providing novel insights on putative cancer gene biosystems not still fully investigated.

Figure 1. Schematic picture of connections in co-expression graphs.

Healthy condition on the left and disease-affected tissue on the right. Green links remain unchanged in the two phenotypes. Red connections are loss from healthy to cancer network. Blu edges are novel connections in the cancer tissue.

Figure 2. Cancer tissues are characterized by loss of connectivity.

(A-E) Cumulative distribution functions of the gene degree. (F) Boxplots of the gene degrees for the five tissues in the two conditions. Red color refers to the cancer phenotype. Blue color refers to the normal phenotype. The median degree in cancer is lower than in normal conditions.

Figure 3. Benjamini–Hochberg False Discovery Rate (FDR) as a function of the p-value.

Red color represents the differentially expressed genes (DE). Blue color represents the differentially connected genes (DC).

Figure 4. Comparison between differential expression and differential connectivity.

(A-E) (Upper panel) Gene differential connectivity p-value as a function of degree ratio . (Lower panel) Gene differential expression p-value as a function of degree ratio . Each point represents a gene and the trend line is the least-square line. (F) Correlations between the differential expression p-value and the gain of connections. P-values on the bars refer to right-tail tests for the positive correlations, and left-tail tests for the negative correlations. (G) Correlations between differential expression p-values and differential connection p-values.

Figure 5. Comparison between pathway enrichment studies for differential expression and differential connectivity.

(A-E) Benjamini–Hochberg False Discovery Rate (FDR) as a function of the p-value. Red color refers to the pathways enriched of differentially expressed genes (DE). Blu color refers to the pathways enriched of differentially expressed genes or differentially connected ones (DEC). (F) Reactome pathways of Immune System. Enrichment meta-analysis p-values across the tissues for ``Reactome Immune System'', its first and second sub-pathways. The histogram in the inset shows the tissue-specific enrichment p-values of ``Reactome Immune System''. (G-H) Core set pathway enrichment analysis. The numbers of core set pathways found as significant at 0.01 level (G) and in the top-ranked positions (H) are displayed on the bars. In the inset it is reported the p-value associated to the relative merit of DEC measure with respect to DE obtained by a permutation test.