Scientific ReportsBioinformaticsPeer-reviewed journal article
Human Transcription Factor and Protein Kinase Gene Fusions in Human Cancer
Kari SalokasiD, Rigbe G. WeldatsadikiD, Markku T VarjosaloiD
Abstract
Oncogenic gene fusions are estimated to account for up-to 20 % of cancer morbidity. Originally, oncofusions were identified in blood cancer, but recently multiple sequence-level studies of cancer genomes have established oncofusions throughout all tissue types. However, the functional implications of the identified oncofusions have often not been investigated. In this study, the identified oncofusions from a fusion detection approach (DEEPEST) were analyzed in more detail. In total, DEEPEST contains 28863 unique fusions. From sequence analysis, we found that almost 30% of them (8225) are expected to produce functional fusion proteins with features from both parent genes. Kinases and transcription factors were found to be the main gene families of the protein producing fusions. Considering their role as initiators, actors, and termination points of cellular signaling pathways, we focused our in-depth analyses on them. The domain architecture of the fusions, as well as of their expected interactors, suggests that abnormal molecular context of intact protein domains brought about by fusion events may unlock the oncogenic potential of the wild type counterparts of the fusion proteins. To understand overall effects of oncofusions on cellular signaling, we performed differential expression analysis using TCGA cancer project samples. Results indicated oncofusion-specific alterations in expression levels of individual genes, and overall lowering of the expression levels of key cellular pathways, such as signal transduction, proteolysis, microtubule cytoskeleton organization, and in particular regulation of transcription. The sum of our results suggests that kinase and transcription factor oncofusions globally deregulate cellular signaling, possibly via acquiring novel functions.
- organism: human
- assay: whole-genome sequencing
- research focus: differential expression
- approach: statistical model
- Citations
- 19
- FWCI
- 0.68
- Field percentile
- top 42%
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