Global changes in open reading frame dominance of RNAs during cancer initiation and progression
Suenaga, Y.; Kogashi, H.; Nakatani, K.; Lin, J.; Hasegawa, Y.; Kugou, K.; Kawashima, Y.; Furukawa, E.; Okumura, K.; Kita, E.; Wakabayashi, Y.; Kato, M.; Kawazu, M.; Hippo, Y.
Show abstract
Cancer cells express unique RNA transcripts; however, the factors determining their translation have remained unclear. We recently developed open reading frame (ORF) dominance as a measure that correlates with coding potential of RNAs. Upon calculating the ORF dominance of cancer-specific transcripts across 24 human tumor types, 14 exhibited significantly higher ORF dominance in cancer than in normal tissues. In organoid-based mouse genetic models, ORF dominance increased with carcinogenesis. Gene ontology analysis revealed that gene sets with increased ORF dominance were associated with cell proliferation, while those with decreased ORF dominance were linked to DNA damage response. Translatome analyses demonstrated that elevated ORF dominance during carcinogenesis resulted in higher translation frequencies of ribosome-bound RNAs. As cancer progressed, ORF dominance showed that the boundary between coding and noncoding transcripts became blurred prior to distant metastasis, indicating decreased proliferative cell populations and increased generation of RNA isoforms that potentially translate neoantigens before the development of metastatic tumors. These findings suggest that cancer evolution leads to dynamic changes in ORF dominance, resulting in global translational alterations in transcriptomes.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Long-read single-cell sequencing reveals expressions of hypermutation clusters of isoforms in human liver cancer cells 96%
- Systematic lncRNA mapping to genome-wide co-essential pathways uncovers cancer dependency on uncharacterized lncRNAs 95%
- circHIPK3 nucleates IGF2BP2 and functions as a competing endogenous RNA 95%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.