Somatic point mutations are enriched in long non-coding RNAs with possible regulatory function in breast cancer

Non-coding RNAs (ncRNAs) form a large portion of the mammalian genome however, their biological functions are poorly characterized in cancers. In this study, using a newly developed tool, SomaGene, we analyze de novo somatic point mutations from the International Cancer Genome Consortium (ICGC) whole-genome sequencing data of 1,855 breast cancers. We identify 929 candidates of ncRNAs that are significantly and explicitly mutated in breast cancer samples. By integrating data from the ENCODE regulatory features and FANTOM5 expression atlas, we show that the candidate ncRNAs in breast cancer samples significantly enrich for active chromatin histone marks (1.9 times), CTCF binding sites (2.45 times), DNase accessibility (1.76 times), HMM predicted enhancers (2.26 times) and eQTL polymorphisms (1.77 times). Importantly, we show that the 929 ncRNAs contain a much higher level (3.64 times) of breast cancer-associated genome-wide association (GWAS) single nucleotide polymorphisms (SNPs) than genome-wide expectation. Such enrichment has not been seen with GWAS SNPs from other diseases. Using breast tissue related Hi-C data we then show that 82% of our candidate ncRNAs (1.9 times) significantly interact with the promoter of protein-coding genes, including previously known cancer-associated genes, suggesting the critical role for candidate ncRNA genes in activation of essential regulators of development and differentiation in breast cancer. We provide an extensive web-based resource (https://www.ihealthe.unsw.edu.au/research), to communicate our results with the research community. Our list of breast cancer-specific ncRNA genes has the potential to provide a better understanding of the underlying genetic causes of breast cancer. Lastly, the tool developed in this study can be used in the analysis of somatic mutations in all cancers.

In silicoidentification of non-coding RNAs inHalobacterium salinarum NRC-1, a model archeon organism

BackgroundIn addition to the regulatory elements already known, for instance, transcription factors or post-translation modifications, there is growing interest in the regulatory role played by non-coding RNA molecules (ncRNA), whose functions are performed at a different level of biological information processing. Model organisms provide a convenient way of working in the laboratory, and different research groups use these models to conduct studies on the cellular mechanisms present in these organisms. Although some ncRNAs elements have been found in theHalobacterium salinarummodel organism, we believe that not enough is known about these genomic regions.MethodsTherefore, anin silicoanalysis for ncRNA identification was conducted onH. salinarumNRC-1. Considering a data integration perspective and some available methodologies, several machine learning models were built and used to designate candidate ncRNAs genome regions.ResultsA total of 42 new ncRNAs were identified. Combing analysis with other available tools, it had been observed that some suggested candidates also was found with different methodologies and thus, it highlights the proposed results.