Cancer reduces transcriptome specialization

Abstract

A central goal of cancer biology is to understand how cells from this family of genetic diseases undergo specific morphological and physiological changes and regress to a de-regulated state of the cell cycle. The fact that tumors are unable to perform most of the specific functions of the original tissue led us to hypothesize that the degree of specialization of the transcriptome of cancerous tissues must be less than their normal counterparts. With the aid of information theory tools, we analyzed four datasets derived from transcriptomes of normal and tumor tissues to quantitatively test the hypothesis that cancer reduces transcriptome specialization. Here, we show that the transcriptional specialization of a tumor is significantly less than the corresponding normal tissue and comparable with the specialization of dedifferentiated embryonic stem cells. Furthermore, we demonstrate that the drop in specialization in cancerous tissues is largely due to a decrease in expression of genes that are highly specific to the normal organ. This approach gives us a better understanding of carcinogenesis and offers new tools for the identification of genes that are highly influential in cancer progression.

Figure 1. Scatter plot of H_j (Diversity) and δ_j (Specialization) in transcriptomes of normal tissues (blue), cancerous tissues (red), and stem cells (black).

Comparable data sets are linked by a discontinuous line. A - Human data from 53 libraries of 13 distinct tissues with a total of 671,197 tags for 28,087 genes; grouped analyses. B - Mouse data from 29 libraries of 5 distinct tissues and with a total of 541,453 expressed tags for 25,044 distinct genes; grouped analyses. Data for A and B are from the “Cancer Genome Anatomy Project” (http://cgap.nci.nih.gov/). Approximate 95% confidence intervals for diversity and specialization are plotted as continuous colored lines. See Supporting Text S1 as well as Figure S1, Figure S2, Figure S3, Figure S4, Figure S5 and Figure S6 that illustrate individual comparisons and details.

Figure 2. Scatter plot of H_j (Diversity) and δ_j (Specialization) in transcriptomes of normal (blue) and tumor (red) tissues in dataset C.

Human expression data are from the “Human Transcriptome Map” project (http://bioinfo.amc.uva.nl/HTMseq/controller), datasets “All tissues normal” and “All tissues tumor”. Data consist of 18,609,073 tags for a total of 62,916 loci by chromosome. See Figure S7, Figure S8, Figure S9 and Figure S10 that amplify the boxes of this figure presenting the 95% confidence intervals for the estimates.