The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
The Cancer Spliceome: Reprograming of Alternative Splicing in Cancer
Open accessAbstract
Alternative splicing allows for the expression of multiple RNA and protein isoforms from one gene, making it a major contributor to transcriptome and proteome diversification in eukaryotes. Advances in next generation sequencing technologies and genome-wide analyses have recently underscored the fact that the vast majority of multi-exon genes under normal physiology engage in alternative splicing in tissue-specific and developmental-specific manner. On the other hand, cancer cells exhibit remarkable transcriptome alterations partly by adopting cancer-specific splicing isoforms. These isoforms and their encoded proteins are not insignificant byproducts of the abnormal physiology of cancer cells, but either drivers of cancer progression or small but significant contributors to specific cancer hallmarks. Thus, it is paramount that the pathways that regulate alternative splicing in cancer, including the splicing factors that bind to pre-mRNAs and modulate spliceosome recruitment. In this review, we present a few distinct cases of alternative splicing in cancer, with an emphasis on their regulation as well as their contribution to cancer cell phenotype. Several categories of splicing aberrations are highlighted, including alterations in cancer-related genes that directly affect their pre-mRNA splicing, mutations in genes encoding splicing factors or core spliceosomal subunits, and the seemingly mutation-free disruptions in the balance of the expression of RNA-binding proteins, including components of both the major (U2-dependent) and minor (U12-dependent) spliceosomes. Given that the latter two classes cause global alterations in splicing that affect a wide range of genes, it remains a challenge to identify the ones that contribute to cancer progression. These challenges necessitate a systematic approach to decipher these aberrations and their impact on cancer. Ultimately, a sufficient understanding of splicing deregulation in cancer is predicted to pave the way for novel and innovative RNA-based therapies.
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Cites
Alternative isoform regulation in human tissue transcriptomes
Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B. (2008) · Nature · doi:10.1038/nature07509 · Cited by 5,181
Pan, Q., Shai, O., Lee, L. J., Frey, B. J., & Blencowe, B. J. (2008) · Nature Genetics · doi:10.1038/ng.259 · Cited by 3,951
Rac1b and reactive oxygen species mediate MMP-3-induced EMT and genomic instability
Radisky, D. C., Levy, D. D., Littlepage, L. E., Liu, H., Nelson, C. M., Fata, J. E., Leake, D., Godden, E. L., Albertson, D. G., Angela Nieto, M., et al. (2005) · Nature · doi:10.1038/nature03688 · Cited by 1,284
SRSF2 Mutations Contribute to Myelodysplasia by Mutant-Specific Effects on Exon Recognition
Kim, E., Ilagan, J. O., Liang, Y., Daubner, G. M., Lee, S. C.-W., Ramakrishnan, A., Li, Y., Chung, Y. R., Micol, J.-B., Murphy, M. E., et al. (2015) · Cancer Cell · doi:10.1016/j.ccell.2015.04.006 · Cited by 575
Rogozin, I. B., Wolf, Y. I., Sorokin, A. V., Mirkin, B. G., & Koonin, E. V. (2003) · Current Biology · doi:10.1016/s0960-9822(03)00558-x · Cited by 445
Tissue-specific alternative splicing remodels protein-protein interaction networks
Ellis, J. D., Barrios-Rodiles, M., Çolak, R., Irimia, M., Kim, T., Calarco, J. A., Wang, X., Pan, Q., O'Hanlon, D., Kim, P. M., et al. (2012) · Molecular Cell · doi:10.1016/j.molcel.2012.05.037 · Cited by 431
Caldenhoven, E., van Dijk, T. B., Solari, R., Armstrong, J., Raaijmakers, J. A. M., Lammers, J.-W. J., Koenderman, L., & de Groot, R. P. (1996) · Journal of Biological Chemistry · doi:10.1074/jbc.271.22.13221 · Cited by 373
van den Heuvel, A. P. J., Jing, J., Wooster, R. F., & Bachman, K. E. (2012) · Cancer Biology & Therapy · doi:10.4161/cbt.21348 · Cited by 209
Tang, Y., Horikawa, I., Ajiro, M., Robles, A. I., Fujita, K., Mondal, A. M., Stauffer, J. K., Zheng, Z.-M., & Harris, C. C. (2013) · Oncogene · doi:10.1038/onc.2012.288 · Cited by 165
The ribosome-engaged landscape of alternative splicing
Weatheritt, R. J., Sterne-Weiler, T., & Blencowe, B. J. (2016) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.3317 · Cited by 162 · cited 2×
“…is not always immediately interpretable or even recognized, a plethora of work indicates that alternatively spliced exons are translated and tend to encode important domains in the encoded polypeptide (Kalsotra and Cooper, 2011; Ellis et al., 2012; Weatheritt et al., 2016; Tapial et al., 2017).”
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MTOR signaling orchestrates stress-induced mutagenesis, facilitating adaptive evolution in cancer
Cipponi, A., Goode, D. L., Bedo, J., McCabe, M. J., Pajic, M., Croucher, D. R., Rajal, A. G., Junankar, S. R., Saunders, D. N., Lobachevsky, P., et al. (2020) · Science · doi:10.1126/science.aau8768 · Cited by 103
Identifying common transcriptome signatures of cancer by interpreting deep learning models
Jha, A., Quesnel-Vallières, M., Wang, D., Thomas-Tikhonenko, A., Lynch, K. W., & Barash, Y. (2022) · Genome Biology · doi:10.1186/s13059-022-02681-3 · Cited by 39
Metadata
- Authors
- El Marabti, E., & Younis, I.
- Venue
- Frontiers in Molecular Biosciences
- Publisher
- Frontiers Media SA
- Published
- 2018-09-07
- Volume
- 5
- Pages
- 80-80
- ISSN
- 2296-889X (ISSN-L)
- OpenAlex
- W2890052597
- Cited by
- 220
- References
- 11
- FWCI
- 9.65
- Type
- journal article
- Language
- en
- Open access
- gold
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