Differential co-expression can signal a change in the activity of a pathway.

<p>Each arrow represents the level of expression of an enzymatic gene from a single sample (<i>e.g.</i> a patient, so that all arrows of the same color derive from the same sample). In normal tissue, the expression of genes encoding enzymes <i>E</i>₁ and <i>E</i>₂ are strongly correlated, and the expression of <i>E</i>₁ and <i>E</i>₃ are uncorrelated. In tumor tissue, the expression of genes encoding enzymes <i>E</i>₁ and <i>E</i>₃ are strongly correlated, and the expression of <i>E</i>₁ and <i>E</i>₂ are uncorrelated. If we assume that enzyme activity is correlated with expression, then we may hypothesize that the metabolic flux exiting from <i>E</i>₁ is coupled to flux in <i>E</i>₂ in normal tissue, and to flux in <i>E</i>₃ in tumor tissue. Note that the average expression of all enzymes remains constant between tumor and normal conditions, so that a differential expression analysis would be unlikely to identify the expression of these genes as anamolous.</p

Outline of the method to calculate differential co-expression.

<p>(<b>A</b>) Calculate the co-expression for each pair of metabolic genes across tumor (red) and normal (blue) samples, respectively. (<b>B</b>) For each pair of genes in a given tumor type (<i>e.g.</i> breast), compare the Spearman correlation coefficient in tumor and normal samples. Most pairs of genes show very similar co-expression in both tumor and normal samples (reflected in the high density of points in the center of the plot). More rarely, a pair of genes will show significantly different co-expression between normal and tumor samples (<i>e.g.</i> bottom right and top left corners). (<b>C</b>) Using the statistical methodology detailed in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004176#pcbi.1004176.e010" target="_blank">Eq 4</a>, filter out insiginificant differences in correlation coefficients. Retain the remaining (significant) differences in correlations in the matrix <b>D</b>. The filtered results can then be analyzed further to identify regions of metabolism enriched for differential co-expression.</p

The targets of HNF4 are enriched for differential co-expression in KIRC.

<p>(<b>A</b>) HNF4A is not differentially expressed between tumor and adjacent normal tissue samples in KIRC. Each dot corresponds to the expression of HNF4A in one sample of either primary KIRC tumor or normal kidney tissue. (<b>B</b>) Nevertheless, the metabolic gene targets of HNF4A show a distinct loss of co-expression with HNF4A in tumor samples. Several of these genes reside in central carbon metabolism. Genes outside the shaded area correspond to statistically significant instances of differential co-expression. (<b>C</b>) Heatmap of differential co-expression for the 20 metabolic gene targets of HNF4A containing the motif AARGTCCAN around the transcription start site. Value of each square indicates the difference in correlation coefficients between tumor and normal samples, with statistically insignificant differences set to zero. A strict p-value threshold of 1 × 10⁻⁴ was used to assign statistical significance. (<b>D</b>) Survival curves for patients showing low or high expression of HNF4A. Patients with low expression of HNF4A exhibited worse outcomes.</p

Transcription factors most enriched for differential co-expression targets in BRCA.

<p>Transcription factors most enriched for differential co-expression targets in BRCA.</p

Transcription factors most enriched for differential co-expression targets in KIRC.

<p>Transcription factors most enriched for differential co-expression targets in KIRC.</p

Top differentially co-expressed genes in KIRC.

<p>Top differentially co-expressed genes in KIRC.</p

Top differentially co-expressed genes in BRCA.

<p>Top differentially co-expressed genes in BRCA.</p

Differential expression and differential co-expression are only weakly correlated.

<p>Green dots, detailed in insets, indicate genes with high differential co-expression score </p><p></p><p></p><p>Si0</p><p></p><p></p> (> 2 standard deviations above the mean <p></p><p></p><p>Si0</p><p></p><p></p>) but very small absolute fold ratio (< 0.1). Black dots indicate a gene is differentially expressed with corrected p-value less than 0.01 and absolute fold log₂ fold ratio greater than 1. Transparent dots correspond to genes which are not differentially expressed.<p></p

PanCan analysis of differential co-expression.

<p>(<b>A</b>) All gene pairs which showed differential co-expression in at least 3 out of 4 different TCGA studies were identified. Approximately 50 unique metabolic genes participated in these recurrently differentially co-expressed pairs. The differential co-expression across all possible pairs of thes genes is depicted in the heatmap. A p-value threshold of 1 × 10⁻⁴ was used to assign statistical significance for differential co-expression. Special emphasis is placed on ATP5F1. (<b>B</b>) Co-expression of ATP5F1 and ATP5L, both members of mitochondrial Complex V, in four different TCGA studies (blue dots: normal tissue samples; red dots: tumor samples). Red line corresponds to perfect 1:1 correlation. Tumor samples exhibit substantially noisier co-expression of these two genes.</p

Extensive Decoupling of Metabolic Genes in Cancer - Fig 2

<p>(<b>A</b>) Principal components analysis (PCA) for the breast (green) and kidney (blue) differential co-expression data. Each dot represents one gene. Data from kidney tumors exhibits variation mostly along the first principal component, while data from breast tumors varies mostly along the second, suggesting that the dominant modes of variation in the two tumor types are distinct from each other. (<b>B</b>) Differential co-expression pathway analysis. Each axis denotes the enrichment score for a pathway in breast or kidney tumors, respectively. Red dots indicate significantly over- or under-enriched pathways. (<b>C</b>) A comparison of the score <b>S⁰</b> in breast and kidney tumors. Each dot is a single gene. A number of genes (blue and green dots and inset boxes) show extensive differential co-expression in one tumor type, but none in the other. Other genes (red dots and inset box) are highly differentially co-expressed in both.</p