Abstract
Splicing factors (SFs) act in dynamic macromolecular complexes to modulate RNA processing. To understand the complex role of SFs in cancer progression, we performed a systemic analysis of the co-regulation of SFs using primary tumor RNA sequencing data. Co-regulated SFs were associated with aggressive breast cancer phenotypes and enhanced metastasis formation, resulting in the classification of Enhancer- (21 genes) and Suppressor-SFs (64 genes). High Enhancer-SF levels were related to distinct splicing patterns and expression of known oncogenic pathways such as respiratory electron transport, DNA damage and cell cycle regulation. Importantly, largely identical SF co-regulation was observed in almost all major cancer types, including lung, pancreas and prostate cancer. In conclusion, we identified cancer-associated co-regulated expression of SFs that are associated with aggressive phenotypes. This study increases the global understanding of the role of the spliceosome in cancer progression and also contributes to the development of strategies to cure cancer patients.
Highlights
In order to systematically evaluate the role of the complete spliceosome during breast cancer development and progression, we compared RNA expression levels of every single splicing factor (244 in total, derived from Hegele et al.3) between normal mammary gland tissue and matched primary breast tumor (Fig. 1A–D) and between primary breast tumor and metastatic tissue (Fig. 1E–H) using matched patient RNA sequencing data from 114 and 7 patients respectively, obtained from The Cancer Genome Atlas (TCGA)
Genes within the clusters derived from the analysis of the TCGA database showed strong overlap with clusters derived from the analysis of the BASIS database (Fig. 1N), providing high confidence in the actual co-regulation of these subsets of splicing factors in breast cancer
We took a bioinformatics approach to unravel splicing factor interactions at RNA expression levels in the context of cancer progression making advantage of the wealth of information provided by the TCGA and BASIS databases
Summary
Pearson Correlation coefficients (PCs) between RNA expression levels of splicing factors. (J) Hierarchical clustering (Euclidean distance, complete linkage) of the correlation of splicing factor expression levels in TCGA RNA sequencing data (red = high positive correlation, green = high negative correlation). (K) Same as J for BASIS RNA sequencing data (only expression data for 235 factors available). (L) Example of highly negatively correlating splicing factors in TCGA RNA sequencing data. N. Number of genes that overlap between BASIS and TCGA hierarchical clusters shown in (J,K) *p < 0.05, **p < 0.01, ***p < 0.001. We made advantage of large datasets of breast tumor-derived patient RNA sequencing-based gene expression (The Cancer Genome Atlas and BASIS11,13). High Enhancer-SF expression levels associate with a more aggressive tumor phenotype and higher risk of developing metastases. This study elicits an important role for splicing in cancer progression and might initiate the discovery of new biomarkers and treatments to combat this deadly disease
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