bioRxivGenomicsPreprint
Found in Transcription: Gene fusions arise through defects in RNA processing in the absence of chromosomal rearrangements.
Yue JiangiD, Michael J. ApostolidesiD, Mia HusićiD, Robert SiddawayiD, Man YuiD, Stephanie Mark, Arun K RamaniiD, Cynthia E HawkinsiD, Michael BrudnoiD
Abstract
Recent advancements in high throughput sequencing analysis have enabled the characterization of cancer-driving fusions, improving our understanding of cancer development. Most fusion calling methods, however, examine either RNA or DNA information alone and are limited to a rigid definition of what constitutes a fusion. For this study we developed a pipeline that incorporates several fusion calling methods and considers both RNA and DNA to capture a more complete representation of the tumour fusion landscape. Interestingly, most of the fusions we identified were specific to RNA, with no evidence of corresponding genomic restructuring. Further, while the average total number of fusions in tumour and normal brain tissue samples is comparable, their overall fusion profiles vary significantly. Tumours have an over-representation of fusions occurring between coding genes, whereas fusions involving intergenic or non-coding regions comprised the vast majority of those in normals. Tumours were also more abundant in unique, sample-specific fusions compared to normals, though several fusions exhibited strong recurrence in the tumour type examined (diffuse intrinsic pontine glioma; DIPG) and were absent from both normal tissues and other cancers. Intriguingly, tumours also show broad up- or down-regulation of spliceosomal gene expression, which significantly correlates with fusion number (p=0.007). Our results show that RNA-specific fusions are abundant in both tumour and normal tissue and are associated with spliceosomal gene dysregulation. RNA-specific fusions should be considered as a potential mechanism that may contribute to cancer formation initiation and maintenance alongside more traditional structural events.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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