Dissecting the expression landscape of RNA-binding proteins in human cancers

Abstract

Background: RNA-binding proteins (RBPs) play important roles in cellular homeostasis by controlling gene expression at the post-transcriptional level.

Results: We explore the expression of more than 800 RBPs in sixteen healthy human tissues and their patterns of dysregulation in cancer genomes from The Cancer Genome Atlas project. We show that genes encoding RBPs are consistently and significantly highly expressed compared with other classes of genes, including those encoding regulatory components such as transcription factors, miRNAs and long non-coding RNAs. We also demonstrate that a set of RBPs, numbering approximately 30, are strongly upregulated (SUR) across at least two-thirds of the nine cancers profiled in this study. Analysis of the protein-protein interaction network properties for the SUR and non-SUR groups of RBPs suggests that path length distributions between SUR RBPs is significantly lower than those observed for non-SUR RBPs. We further find that the mean path lengths between SUR RBPs increases in proportion to their contribution to prognostic impact. We also note that RBPs exhibiting higher variability in the extent of dysregulation across breast cancer patients have a higher number of protein-protein interactions. We propose that fluctuating RBP levels might result in an increase in non-specific protein interactions, potentially leading to changes in the functional consequences of RBP binding. Finally, we show that the expression variation of a gene within a patient group is inversely correlated with prognostic impact.

Conclusions: Overall, our results provide a roadmap for understanding the impact of RBPs on cancer pathogenesis.

Figure 1

Flow chart showing the different steps in the analysis of expression levels of RNA-binding proteins for human cancers. The flow chart shows the acquisition and preparation of data (red), determination of patterns of dysregulation (green), network and interaction analysis (light blue), and survival analysis (dark blue). KM, Kaplan–Meier; lncRNA, long non-coding RNA; PPI, protein–protein interaction; RBP, RNA-binding protein; TCGA, the Cancer Genome Atlas; TF, transcription factor.

Figure 2

Comparison of expression levels of RNA-binding proteins and non-RNA-binding proteins for 16 tissues from 80 healthy individuals studied in the Human BodyMap project. Each of the 16 plots illustrates the significant differences in expression levels in RBPs (P < 2 × 10^-16, Wilcoxon test) across adipose, adrenal, brain, breast, colon, heart, kidney, liver, lung, lymph node, ovary, prostate, skeletal muscle, testes, thyroid, and white blood cell tissues. The x-axis is the category of the observed factor and the y-axis is the expression level. RBP, RNA-binding protein.

Figure 3

Log-ratio of expression for cancer to healthy expression for RNA-binding proteins in nine human cancers. The x-axis is an index of all the RNA-binding proteins that could be extracted from the expression data in the Cancer Genome Atlas. The y-axis is the ratio of the median expression level for each gene across patients versus the observed expression in the Human BodyMap data. Marked are the 31 strongly upregulated RBPs that have an expression ratio over nine across more than half of the studied cancers. Lung Ad. Ca., lung adenocarcinoma; Lung Sq. Ca., lung squamous carcinoma; RBP, RNA-binding protein.

Figure 4

Interaction profiles of RBPs. (A) Distribution of shortest path lengths between every pair of RBPs belonging to SUR and non-SUR RBP groups using the protein–protein interactions documented in the BioGRID database [66], shown as violin plots. The width of each plot is the frequency distribution and the diamond is the median value for the category. SUR RBPs were found to have significantly shorter path lengths amongst themselves in comparison to non-SUR RBPs (P < 2 × 10^-16, Wilcoxon test). (B) Box plot showing the number of interactions identified in BioGRID data for RBPs classified by variability levels defined by observed percentiles. The higher the variability for a RBP, the higher the observed number of protein interactions (P = 9.247 × 10^-16, low vs medium; P < 2.226 × 10^-16, low vs high; P = 6.6556 × 10^-16, medium vs high, KS test). RBP, RNA-binding protein; SUR, strongly upregulated; Kolmogorov–Smirnov test (KS test).

Figure 5

Survival of patients with breast cancer for different expression levels and path lengths for within and between expression groups of RNA-binding proteins. SUR (left) and non-SUR (right) survival for a sample of five RBPs classified by path length (shortest, median or longest). Curves in red are survival plots for patients with enhanced expression of the selected genes based on more than 1,800 patients’ expression profiles from the KM plot [68]. The within-group path ranking for SUR RBPs suggests that as the mean path lengths increase the contribution of the SUR RBPs in prognosis tends to increase. While between groups, RBPs having shorter path lengths to a SUR RPB contribute the most to prognosis. KM, Kaplan–Meier; RBP, RNA-binding protein; SUR, strongly upregulated; HR, Hazard Ratio.

Figure 6

Comparison and distribution of prognostic impact based on expression dysregulation and expression variability in breast tissue. RNA-binding proteins (A, C) and non-RNA-binding proteins (B, D) were categorized based on their level of dysregulation as healthy or cancer expression (SUR or non-SUR) and the variability of expression levels (high, medium or low) in patients with breast cancer. The statistical significances for the differences in the distributions of prognostic impact are discussed in the main text. KM, Kaplan–Meier; RBP, RNA-binding protein; Sig., significance; SUR, strongly upregulated.