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Dysregulated transcription across diverse cancer types reveals the importance of RNA-binding protein in carcinogenesis
© Wang et al.; licensee BioMed Central Ltd. 2015
- Published: 11 June 2015
It is well known that carcinogenesis is in part dictated by dysregulated transcription events and signal pathways. Large-scale transcriptional profiling studies in each cancer type have reported aberrant gene expression associated with cancer development. However, common and specific patterns altered across cancer types, especially the contribution of transcriptional and post-transcriptional regulators, are rarely explored.
Using transcriptional profiles from matched tumor and normal samples in the Cancer Genome Atlas pan-cancer dataset, we performed a comprehensive analysis on the altered expression across 9 cancer types, focusing on transcriptional and post-transcriptional regulators and cancer-related genes. As we expected, the transcription of cancer-related genes was significantly deregulated in tumor vs. normal across all cancer types. Surprisingly, the expression of RNA-binding proteins (RBPs), master regulators of post-transcriptional gene expression, was also significantly changed across most studied cancer types. Although the expression of RBPs was not as strongly deregulated as cancer-related genes, their direct interaction partners are enriched by cancer-related genes, suggesting the cascade regulation effect of RBPs. Integrating genetic and epigenetic profiles found that deregulated RBPs were frequently caused by genetic rather than epigenetic alterations. Furthermore, tissue-specific genes were under-expressed in tumor vs. normal across all cancer types except prostate cancer.
Dysregulated transcription across cancer types reveals the importance of RBPs in carcinogenesis. The aberrant expression of RBPs is caused by genetic alterations and spreads their effect to cancer-related genes. In addition, disruption of tissue-specific genes contributes to the corresponding cancer pathology.
- Dysregulated transcription
- Tissue-specific genes
- Cancer-related genes
Cancer development is characterized by uncontrolled cell proliferation, which is in part due to expression alteration of genes which regulate cell growth and differentiation, such as the improper over-expression of oncogenes, or the under-expression or disabling of tumor suppressor genes . Comparative analysis of expression alterations between tumor and matched normal samples in each individual cancer type has identified many transcriptional and post-transcriptional regulators associated with carcinogenesis [2–13]. For instance, compared to normal mucosa, transcription factor (TF) NRF2 was found over-expressed in head and neck squamous cell carcinoma . Using transcriptional data of 17 adenomas and paired samples of normal mucosa, the transcription-regulating network of colorectal adenomas is characterized by significantly altered expression of over 250 TF genes . Compared to TFs, expression alteration of RNA-binding proteins (RBPs), master regulators at the post-transcriptional level, was less studied but deregulated transcriptions of several RBPs also have been reported to play a critical role in human cancers [9–12]. For example, QKI was frequently down-regulated in lung cancer, and QKI-5 inhibited the proliferation and transformation of lung cancer cells . Transcription profiling analysis of RBPs uncovered their aberrant function associated with prostate adenocarcinoma, colon adenocarcinoma, and breast carcinoma as well [9, 12, 15]. Additionally, aberrant expression of microRNAs (miRNAs) and long non-coding RNAs' (lncRNAs') also led to cancer development [16–20]. However, common and specific patterns altered across different cancer types, especially the contribution of transcriptional and post-transcriptional regulators, are rarely known.
Large-scale genomics projects, such as the Cancer Genome Atlas (TCGA), provided various omics data for thousands of tumors with matched normal samples, including genetic, epigenetic, transcriptomics and proteomics data , which gave us a great opportunity to perform pan-cancer studies for understanding the common and specific profiles across multiple cancer types. Recently, landscapes of somatic mutation, copy number alterations and oncogenic signatures across major cancer types have been studied [22–24], as well as microRNA-target interaction and functional proteomics data analysis [25, 26]. However, as far as we know, comparative analysis of expression alterations of transcriptional and post-transcriptional regulators across cancer types has never been explored.
In this study, we characterized the expression perturbation of TFs, RBPs, lncRNAs, cancer related genes (allOnco) and other genes on 522 matched tumor and normal tissue pairs across 9 cancer types. We first analyzed the differential expression between matched tumor and normal for each type of gene sets across all studied cancer types, and compared their amplitude of alterations. Then we integrated genetic and epigenetic data and protein-protein interaction network (PPI) to explain the upstream cause and downstream effect of dysregulated transcription. Finally we compared expression changes of tissue-specific genes with non-specific ones and investigated the consistent pathway changes across different cancer types.
Expression alteration of RBPs contributes to cancer development
Number and significance of differently expressed genes.
Surprisingly, RBPs were significantly changed in 6 of the 9 cancer types. Marginal significance was observed in PRAD (p-value = 0.01) and HNSC (p-value = 0.04), while highly statistical significance was detected in COADREAD, LUAD and LUSC (p-value = 3.82e-13, 1e-15 and 4.04e-16 respectively) (Figure 1). Consistent expression alterations of RBPs across different cancer types suggested that they play an important role in carcinogenesis. Compared to RBPs, TFs only showed marginally significant enrichment in HNSC, possibly due to the fact that the activity changes of TF are at the protein level which cannot be reflected at the transcription level (Figure 1). lncRNAs were significantly depleted across all cancer types (Figure 1), which are possibly biased because only 264 of 9227 lncRNAs were included in the standardized mRNA-Seq data in Firehose (see Materials and Methods). Additionally the expression level of lncRNAs is especially low compared to other regulation factors .
Tissue-specific genes lost function in tumor
Functional similarity across different cancer types
Dysregulated transcription of RBPs plays an important role in cancer development. The aberrant expression of RBPs is caused by genetic alterations and spreads their effect to cancer-related genes. In addition, disruption of tissue-specific genes contributes to the corresponding cancer pathology.
Genetic, epigenetic and transcriptomics data for 9 cancer types
The mRNA-Seq data of 522 matched tumor and adjacent normal samples for 9 cancer types, the copy number alterations, and the DNA methylation data were downloaded from Firehose developed by the Broad GDAC (https://confluence.broadinstitute.org/display/GDAC/Dashboard-Stddata). The nine cancer types are BRCA (Breast cancer carcinoma), COADREAD (colon/ rectum adenocarcinoma), HNSC (head and neck squamous cell carcinoma), LUAD (lung adenocarcinoma), KIRC (kidney renal clear cell carcinoma), LIHC (liver hepatocellular carcinoma), LUSC (lung squamous cell carcinoma), THCA (thyroid carcinoma) and PRAD (prostate adenocarcinoma). There are 111 paired samples for BRCA, 32 for COADREAD, 41 for HNSC, 72 for KIRC, 57 for LUAD, 59 pairs for THCA and 50 for each type of LUSC, LIHC and PRAD, respectively.
Different gene sets
1889 TFs were collected from TRANSFAC , and 799 experimentally characterized RBPs were obtained from a recent publication dissecting transcriptional profiles of RNA-binding protein in cancer . Over 9,000 lncRNAs were downloaded from Genecode [58, 59], but only 264 of these were included in mRNA-Seq data from the Broad GDAC standardized data packages. A comprehensive list of 2102 cancer related genes (allOnco), which is a non-redundant union of 8 studies [33, 60–64], was downloaded from Bushman Lab (http://www.bushmanlab.org/links/genelists). About 2570 tissue-specific genes were collected from PaGenBase, which defines genes to be tissue-specific if they are dominantly expressed in one tissue. There are 145 breast-specific, 364 colon-specific, 480 kidney-specific, 628 liver-specific, 643 lung-specific, 263 prostate-specific and 227 thyroid-specific genes, respectively . Different types of somatic mutations, including frameshift mutations, germline mutations, missense mutations, large deletions, splicing mutations and translocations were collected from COSMIC .
Statistical evaluation of differential expression
Paired t-test was used to detect differentially expressed genes between matched tumor and normal tissue pairs. Bonferroni method was used to adjust p-values for multiple testing. Hypergeometric test was used to evaluate the enrichment of different types of genes in the set of differentially expressed genes. All statistical tests in this study were implemented in R (version 3.0.3) .
Pairwise Spearman correlations were calculated between the copy number alterations/DNA methylation alterations and gene expression changes for differentially and non-differentially expressed RBPs. The statistical difference of the correlation coefficients were assessed by Wilcoxon Rank Sum test .
Clustering by biological pathways
KEGG pathways are wiring diagrams of molecular interactions, reactions, and relations, and mainly used for biological interpretation of higher-level systemic functions. Different cancers may have consistent changes in some cancer related pathways. To find those pathways similarly altered across different cancers, we performed hierarchal clustering under some specific pathways, including cell cycle, cell proliferation, pathways in cancer and etc. The distance matrix was calculated by Spearman correlation coefficient of expression alteration between different cancer types.
This work was supported by grants CCSG (P30 CA068485), BETRNet (U01 CA163056), Breast (P50 CA098131), and GI (P50 CA095103).
The publication charges for this article have been funded by the corresponding authors.
This article has been published as part of BMC Genomics Volume 16 Supplement 7, 2015: Selected articles from The International Conference on Intelligent Biology and Medicine (ICIBM) 2014: Genomics. The full contents of the supplement are available online at http://0-www.biomedcentral.com.brum.beds.ac.uk/bmcgenomics/supplements/16/S7.
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