The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Dynamic analyses of alternative polyadenylation from RNA-seq reveal a 3′-UTR landscape across seven tumour types
Open accessAbstract
Alternative polyadenylation (APA) is a pervasive mechanism in the regulation of most human genes, and its implication in diseases including cancer is only beginning to be appreciated. Since conventional APA profiling has not been widely adopted, global cancer APA studies are very limited. Here we develop a novel bioinformatics algorithm (DaPars) for the de novo identification of dynamic APAs from standard RNA-seq. When applied to 358 TCGA Pan-Cancer tumour/normal pairs across seven tumour types, DaPars reveals 1,346 genes with recurrent and tumour-specific APAs. Most APA genes (91%) have shorter 3′-untranslated regions (3′ UTRs) in tumours that can avoid microRNA-mediated repression, including glutaminase (GLS), a key metabolic enzyme for tumour proliferation. Interestingly, selected APA events add strong prognostic power beyond common clinical and molecular variables, suggesting their potential as novel prognostic biomarkers. Finally, our results implicate CstF64, an essential polyadenylation factor, as a master regulator of 3′-UTR shortening across multiple tumour types. Alternative polyadenylation (APA) has been implicated in diverse physiological and pathological conditions including cancer. The authors present a new algorithm, DaPars, for APA analysis using available RNA-seq data and suggest CstF64 as a master regulator of 3′-UTR shortening across multiple tumour types.
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Cites
Hallmarks of Cancer: The Next Generation
Hanahan, D., & Weinberg, R. A. (2011) · Cell · doi:10.1016/j.cell.2011.02.013 · Cited by 66,590
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556
Vogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S., Diaz, L. A., & Kinzler, K. W. (2013) · Science · doi:10.1126/science.1235122 · Cited by 7,863
Cox’s regression model for counting processes: A large sample study
Andersen, P. K., & Gill, R. D. (1982) · The Annals of Statistics · doi:10.1214/aos/1176345976 · Cited by 4,419
Discovery and saturation analysis of cancer genes across 21 tumour types
Lawrence, M. S., Stojanov, P., Mermel, C. H., Robinson, J. T., Garraway, L. A., Golub, T. R., Meyerson, M., Gabriel, S. B., Lander, E. S., & Getz, G. (2014) · Nature · doi:10.1038/nature12912 · Cited by 3,096
Cancer cell metabolism: warburg and beyond
Hsu, P. P., & Sabatini, D. M. (2008) · Cell · doi:10.1016/j.cell.2008.08.021 · Cited by 2,452
MAQC Consortium, Shi, L., Shi, L., Reid, L. H., Jones, W. D., Shippy, R., Warrington, J. A., Baker, S. C., Collins, P. J., de Longueville, F., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1239 · Cited by 2,126
Gao, P., Tchernyshyov, I., Chang, T.-C., Lee, Y.-S., Kita, K., Ochi, T., Zeller, K. I., De Marzo, A. M., Van Eyk, J. E., Mendell, J. T., et al. (2009) · Nature · doi:10.1038/nature07823 · Cited by 2,124
Bullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-94 · Cited by 1,680
Mayr, C., & Bartel, D. P. (2009) · Cell · doi:10.1016/j.cell.2009.06.016 · Cited by 1,613
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From Krebs to clinic: glutamine metabolism to cancer therapy
Altman, B. J., Stine, Z. E., & Dang, C. V. (2016) · Nature Reviews Cancer · doi:10.1038/nrc.2016.71 · Cited by 2,119
Alternative polyadenylation of mRNA precursors
Tian, B., & Manley, J. L. (2017) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2016.116 · Cited by 1,179
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Li, Y. I., van de Geijn, B., Raj, A., Knowles, D. A., Petti, A. A., Golan, D., Gilad, Y., & Pritchard, J. K. (2016) · Science · doi:10.1126/science.aad9417 · Cited by 660
Sowd, G. A., Serrao, E., Wang, H., Wang, W., Fadel, H. J., Poeschla, E. M., & Engelman, A. N. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1524213113 · Cited by 249
Tang, W., Zhou, M., Dorsey, T. H., Prieto, D. A., Wang, X. W., Ruppin, E., Veenstra, T. D., & Ambs, S. (2018) · Genome Medicine · doi:10.1186/s13073-018-0602-x · Cited by 162
3′ UTR lengthening as a novel mechanism in regulating cellular senescence.
Chen, M., Lyu, G., Han, M., Nie, H., Shen, T., Chen, W., Niu, Y., Song, Y., Li, X., Li, H., et al. (2018) · Genome Research · doi:10.1101/gr.224451.117 · Cited by 111
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Aguet, F., Alasoo, K., Li, Y. I., Battle, A., Im, H. K., Montgomery, S. B., & Lappalainen, T. (2023) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00188-6 · Cited by 98
Post-transcriptional gene silencing mediated by microRNAs is controlled by nucleoplasmic Sfpq
Bottini, S., Hamouda-Tekaya, N., Mategot, R., Zaragosi, L.-E., Audebert, S., Pisano, S., Grandjean, V., Mauduit, C., Benahmed, M., Barbry, P., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01126-x · Cited by 89
Yang, S. W., Li, L., Connelly, J. P., Porter, S. N., Kodali, K., Gan, H., Park, J. M., Tacer, K. F., Tillman, H., Peng, J., et al. (2020) · Molecular Cell · doi:10.1016/j.molcel.2019.12.022 · Cited by 79
Self-citationHoerner, C. R., Chen, V. J., & Fan, A. C. (2019) · Kidney Cancer · doi:10.3233/kca-180043 · Cited by 68
Chia, M., Li, C., Marques, S., Pelechano, V., Luscombe, N. M., & van Werven, F. J. (2021) · Genome Biology · doi:10.1186/s13059-020-02245-3 · Cited by 47
Tamaddon, M., Shokri, G., Hosseini Rad, S. M. A., Rad, I., Emami Razavi, À., & Kouhkan, F. (2020) · Scientific Reports · doi:10.1038/s41598-020-68406-3 · Cited by 25
Comprehensive characterization of the transcriptional landscape in Alzheimer’s disease (AD) brains
Chen, C., Zhang, Z., Liu, Y., Hong, W., Karahan, H., Wang, J., Li, W., Diao, L., Yu, M., Saykin, A. J., et al. (2025) · Science Advances · doi:10.1126/sciadv.adn1927 · Cited by 6
“PDUIs of certain transcripts whose coverage of last exon 30 × were discarded as previously described ( 93 ).”
Self-citationPost-transcriptional control drives Aurora kinase A expression in human cancers
Cacioppo, R., Rad, D., Pagani, G., Gandellini, P., & Lindon, C. (2024) · PLOS ONE · doi:10.1371/journal.pone.0310625 · Cited by 3
Metadata
- Authors
- Xia, Z., Donehower, L. A., Cooper, T. A., Neilson, J. R., Wheeler, D. A., Wagner, E. J., & Li, W.
- Venue
- Nature Communications
- Published
- 2014-11-20
- Volume
- 5
- Issue
- 1
- Pages
- 5274-5274
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2036065307
- Cited by
- 576
- References
- 13
- FWCI
- 7.18
- Type
- journal article
- Language
- en
- Open access
- bronze
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