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Alternative splicing: a new breakthrough for understanding tumorigenesis and potential clinical applications

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Abstract

Background

Alternative splicing (AS) is a post-transcriptional process that produces transcript variants, thus leading to transcriptome complexity. Recently, the scope of AS studies has been greatly expanded toward clinical applications owing to the abundance of RNA sequencing data.

Objective

This review consists of two parts. We first summarize bioinformatic resources that are useful for large-scale cancer-related AS studies. We then highlight the research efforts to utilize AS events for predicting clinical outcomes and planning therapeutic strategies.

Results

Computational approaches to interrogate AS events have been reviewed under three categories: (1) databases to provide functional and clinical annotation of AS events, (2) analytical tools to identify cancer-associated AS event, and (3) methods to identify splicing-related DNA variants and splicing-derived neoantigens. We also present the recent progress in exploring the clinical utility of AS under four categories: (1) identification of AS events for cancer prognosis, (2) utilization of AS events in molecular classification of various cancers, (3) regulatory mechanisms of AS underlying drug resistance, and (4) potential use of AS in cancer therapy.

Conclusion

This review will be helpful for understanding the biological implications of AS in cancer and facilitate the development of AS markers for cancer prognosis and treatment. We anticipate that future studies will lead to the application of genome-wide AS profiles in cancer precision medicine.

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Acknowledgements

This research was supported by the Basic Science Research Program through the National Research Foundation (NRF), funded by the Ministry of Education (2022R1I1A1A01068731 and RS-2022-00165497).

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Correspondence to Joonhyuck Park or Yeun-Jun Chung.

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Park, J., Park, J. & Chung, YJ. Alternative splicing: a new breakthrough for understanding tumorigenesis and potential clinical applications. Genes Genom 45, 393–400 (2023). https://doi.org/10.1007/s13258-023-01365-x

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