Pervasive transcription read-through promotes aberrant expression of oncogenes and RNA chimeras in renal carcinoma
Open accessAbstract
Aberrant expression of cancer genes and non-canonical RNA species is a hallmark of cancer. However, the mechanisms driving such atypical gene expression programs are incompletely understood. Here, our transcriptional profiling of a cohort of 50 primary clear cell renal cell carcinoma (ccRCC) samples from The Cancer Genome Atlas (TCGA) reveals that transcription read-through beyond the termination site is a source of transcriptome diversity in cancer cells. Amongst the genes most frequently mutated in ccRCC, we identified SETD2 inactivation as a potent enhancer of transcription read-through. We further show that invasion of neighbouring genes and generation of RNA chimeras are functional outcomes of transcription read-through. We identified the BCL2 oncogene as one of such invaded genes and detected a novel chimera, the CTSC-RAB38, in 20% of ccRCC samples. Collectively, our data highlight a novel link between transcription read-through and aberrant expression of oncogenes and chimeric transcripts that is prevalent in cancer.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Transcriptional read-through can give rise to a chimeric transcript which can be spliced and translated into an aberrant fusion protein, if it invades the downstream gene in a pair of tandem genes.
“Transcriptional read‐through can also give rise to a chimeric transcript which can be spliced and translated into an aberrant fusion protein, if it invades the downstream gene in a pair of tandem genes (Grosso et al, 2015; Hennig et al, 2018).”
From RNA polymerase II speed: a key player in controlling and adapting transcriptome composition (2021) · cites this paper as Grosso et al, 2015
The Results section, “Transcription read-through is a source of RNA chimeras in ccRCC” (page not provided), reports chimeric transcripts formed by intergenic splicing after read-through reaches a downstream tandem gene. The Discussion likewise describes invasion of the adjacent gene. Neither passage establishes that these transcripts are translated into aberrant fusion proteins. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCancer cells have probably exploited mechanisms of RNA Pol II speed regulation given the variety of circular and read-through RNAs whose expression is affected during cancer progression.
“Cancer cells have probably exploited such mechanisms given the variety of circular and read‐through RNAs whose expression is affected during cancer progression (Grosso et al, 2015; Smid et al, 2019).”
From RNA polymerase II speed: a key player in controlling and adapting transcriptome composition (2021) · cites this paper as Grosso et al, 2015
The Results sections on transcription read-through and RNA chimeras report altered read-through in clear cell renal carcinoma and link it to the production of RNA chimeras. They do not establish regulation of RNA polymerase II speed or address circular RNAs; the reported mechanism is impaired transcription termination. No page numbers are available in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIncreased transcriptional read-through is known to interfere with the expression of downstream genes in other contexts such as cancer or HSV-1 infection in human cells, as well as cold acclimation in plants.
“Increased transcriptional read‐through is known to interfere with the expression of downstream genes also in other contexts such as cancer or HSV‐1 infection in human cells (Grosso et al, 2015; Rutkowski et al, 2015; Hennig et al, 2018), as well as cold acclimation in plants (Kindgren et al, 2018).”
From RNA polymerase II speed: a key player in controlling and adapting transcriptome composition (2021) · cites this paper as Grosso et al, 2015
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437 · see citing works
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696 · see citing works
Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r36 · Cited by 12,668 · see citing works
Circos: An information aesthetic for comparative genomicsPreprint
Krzywinski, M. I., Schein, J. E., Birol, İ., Connors, J. M., Gascoyne, R. D., Horsman, D. E., Jones, S. J. M., & Marra, M. A. (2009) · Genome Research · doi:10.1101/gr.092759.109 · Cited by 10,838 · see citing works
Mutational landscape and significance across 12 major cancer types
Kandoth, C., McLellan, M. D., Vandin, F., Ye, K., Niu, B., Lu, C., Xie, M., Zhang, Q., McMichael, J. F., Wyczalkowski, M. A., et al. (2013) · Nature · doi:10.1038/nature12634 · Cited by 4,419 · see citing works
Comprehensive molecular characterization of clear cell renal cell carcinoma
The Cancer Genome Atlas Research Network (2013) · Nature · doi:10.1038/nature12222 · Cited by 3,485 · see citing works
Pan-cancer patterns of somatic copy number alteration
Zack, T. I., Schumacher, S. E., Carter, S. L., Cherniack, A. D., Saksena, G., Tabak, B., Lawrence, M. S., Zhang, C., Wala, J. A., Mermel, C. H., et al. (2013) · Nature Genetics · doi:10.1038/ng.2760 · Cited by 1,843 · see citing works
Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma
Varela, I., Tarpey, P. S., Raine, K. M., Huang, D., Ong, C. K., Stephens, P., Davies, H. R., Jones, D., Lin, M., Teague, J. W., et al. (2011) · Nature · doi:10.1038/nature09639 · Cited by 1,278 · see citing works
Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes
Dalgliesh, G. L., Furge, K., Greenman, C., Chen, L., Bignell, G., Butler, A., Davies, H., Edkins, S., Hardy, C., Latimer, C., et al. (2010) · Nature · doi:10.1038/nature08672 · Cited by 1,173 · see citing works
Regulation of Alternative Splicing by Histone Modifications
Luco, R. F., Pan, Q., Tominaga, K., Blencowe, B. J., Pereira‐Smith, O. M., & Misteli, T. (2010) · Science · doi:10.1126/science.1184208 · Cited by 1,099 · see citing works
TopHat-Fusion: an algorithm for discovery of novel fusion transcripts
Kim, D., & Salzberg, S. L. (2011) · Genome biology · doi:10.1186/gb-2011-12-8-r72 · Cited by 806 · see citing works
The UCSC Genome Browser database: 2014 update
Karolchik, D., Barber, G. P., Casper, J., Clawson, H., Cline, M. S., Diekhans, M., Dreszer, T. R., Fujita, P. A., Guruvadoo, L., Haeussler, M., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1168 · Cited by 670 · see citing works
α-Thalassaemia caused by a polyadenylation signal mutation
Higgs, D. R., Goodbourn, S. E. Y., Lamb, J., Clegg, J. B., Weatherall, D. J., & Proudfoot, N. J. (1983) · Nature · doi:10.1038/306398a0 · Cited by 386 · see citing works
Proudfoot, N. J. (1986) · Nature · doi:10.1038/322562a0 · Cited by 309 · see citing works
Unravelling the means to an end: RNA polymerase II transcription termination
Kuehner, J. N., Pearson, E. L., & Moore, C. L. (2011) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3098 · Cited by 299 · see citing works
SETD2 is required for DNA double-strand break repair and activation of the p53-mediated checkpoint
Carvalho, S., Vítor, A. C., Sridhara, S. R. C., Martins, F. B., Raposo, A. C., Desterro, J. M. P., Ferreira, J. A., & de Almeida, S. F. (2014) · eLife · doi:10.7554/elife.02482 · Cited by 266 · see citing works
Self-citationSplicing enhances recruitment of methyltransferase HYPB/Setd2 and methylation of histone H3 Lys36
de Almeida, S. F., Grosso, A. R., Koch, F., Fenouil, R., Carvalho, S., Andrade, J., Levezinho, H. I. D. S. P., Gut, M., Eick, D., Gut, I. G., et al. (2011) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2123 · Cited by 239 · see citing works
Self-citationDuns, G., van den Berg, E., van Duivenbode, I., Osinga, J., Hollema, H., Hofstra, R. M. W., & Kok, K. (2010) · Cancer Research · doi:10.1158/0008-5472.can-10-0120 · Cited by 236 · see citing works
Implications of chimaeric non-co-linear transcripts
Gingeras, T. (2009) · Nature · doi:10.1038/nature08452 · Cited by 226 · see citing works
Terminal Exon Definition Occurs Cotranscriptionally and Promotes Termination of RNA Polymerase II
Dye, M. J., & Proudfoot, N. J. (1999) · Molecular Cell · doi:10.1016/s1097-2765(00)80464-5 · Cited by 159 · see citing works
Are vertebrate exons scanned during splice-site selection?
Niwa, M., MacDonald, C. C., & Berget, S. M. (1992) · Nature · doi:10.1038/360277a0 · Cited by 138 · see citing works
High-resolution profiling of histone h3 lysine 36 trimethylation in metastatic renal cell carcinoma
Ho, T. H., Park, I. Y., Zhao, H., Tong, P., Champion, M. D., Yan, H., Monzon, F. A., Hoang, A. G., Tamboli, P., Parker, A. S., et al. (2015) · Oncogene · doi:10.1038/onc.2015.221 · Cited by 84 · see citing works
Zhang, Y., Xie, S., Zhou, Y., Xie, Y., Liu, P., Sun, M., Xiao, H., Jin, Y., Sun, X., Zhu, C. H., et al. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.08.031 · Cited by 81 · see citing works
Uchida, Τ., Gao, J., Wang, C., et al. (2001) · Molecular Urology · doi:10.1089/109153601300177583 · Cited by 14 · see citing works
Comprehensive molecular characterization of clear cell renal cell carcinoma
Perou, C. M. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/me2t-4a07 · Cited by 7 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
RNA polymerase II speed: a key player in controlling and adapting transcriptome composition
Muniz, L., Nicolas, E., & Trouche, D. (2021) · The EMBO Journal · doi:10.15252/embj.2020105740 · Cited by 203 · see citing works
“Cancer cells have probably exploited such mechanisms given the variety of circular and read-through RNAs whose expression is affected during cancer progression (Grosso et al , 2015; Smid et al , 2019).”
Co-transcriptional splicing regulates 3′ end cleavage during mammalian erythropoiesis
Reimer, K. A., Mimoso, C. A., Adelman, K., & Neugebauer, K. M. (2021) · Molecular Cell · doi:10.1016/j.molcel.2020.12.018 · Cited by 180 · see citing works
Comparative analysis reveals genomic features of stress-induced transcriptional readthrough
Vilborg, A., Sabath, N., Wiesel, Y., Nathans, J., Levy-Adam, F., Yario, T. A., Steitz, J. A., & Shalgi, R. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1711120114 · Cited by 131 · see citing works
Hennig, T., Michalski, M., Rutkowski, A. J., Djakovic, L., Whisnant, A. W., Friedl, M.-S., Jha, B. A., Baptista, M. A. P., L’Hernault, A., Erhard, F., et al. (2018) · PLOS Pathogens · doi:10.1371/journal.ppat.1006954 · Cited by 95 · see citing works
Muniz, L., Deb, M. K., Aguirrebengoa, M., Lazorthes, S., Trouche, D., & Nicolas, E. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.11.006 · Cited by 56 · see citing works
- Authors
- Grosso, A. R., Leite, A. P., Carvalho, S., Matos, M. R., Martins, F. B., Vítor, A. C., Desterro, J. M., Carmo-Fonseca, M., & de Almeida, S. F.
- Affiliations
- University of Lisbon (PT)
- Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal
- ORCID
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2015-11-17
- Volume
- 4
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC4744188
- OpenAlex
- W2173001211
- Cited by
- 152
- References
- 26
- Citations by year
- 2026: 15 · 2025: 9 · 2024: 18 · 2023: 13 · 2022: 13 · 2021: 16 · 2020: 12 · 2019: 11 · 2018: 14 · 2017: 16 · 2016: 15
- FWCI
- 4.91
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- Funders
- Fundação para a Ciência e a Tecnologia (PTDC/BIM-ONC/0384-2012, SFRH/BD/92208/2013, SFRH/BD/52232/2013, IF/00510/2014)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- clear cell renal cell carcinoma, Bcl-2, transcriptome diversity
- MeSH terms
- Carcinoma, Renal Cell, Humans, Kidney Neoplasms, Histone-Lysine N-Methyltransferase, Recombinant Fusion Proteins, Recombination, Genetic, RNA, Messenger, Transcription, Genetic, Oncogene Proteins, Gene Expression, Proto-Oncogene Proteins c-bcl-2, Gene Expression Profiling, Cell Line, Tumor
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
