Alternative Polyadenylation patterns for novel gene discovery and classification in cancer

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Date

2017-06-03

Author

Beğik, Oğuzhan
Öyken, Merve
Can, Tolga
Erson Bensan, Ayşe Elif

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Certain aspects of diagnosis, prognosis and treatment of cancer patients are still important challenges to be addressed. Therefore, we propose a pipeline to uncover patterns of alternative polyadenylation (APA), a hidden complexity in cancer transcriptomes, to further accelerate efforts to discover novel cancer genes and pathways. Here, we analyzed expression data for 1,045 cancer patients and found a significant shift in usage of poly(A) signals in cancers. Using machine-learning techniques, we further defined subsets of APA events to classify cancer types. Furthermore, detected APA patterns were associated with altered protein levels in patients, revealed by antibody-based profiling data. Overall, our study offers a computational approach for the use of APA in novel gene discovery and classification in cancers, with important implications in basic research, biomarker discovery and precision medicine approaches.

URI

https://hdl.handle.net/11511/82854

DOI

https://doi.org/10.1016/j.neo.2017.04.008

Conference Name

Annual Meeting of the RNA Society ( 30 Mayıs - 03 Haziran 2017)

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Department of Biology, Conference / Seminar

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Citation Formats

O. Beğik, M. Öyken, T. Can, and A. E. Erson Bensan, “Alternative Polyadenylation patterns for novel gene discovery and classification in cancer,” presented at the Annual Meeting of the RNA Society ( 30 Mayıs - 03 Haziran 2017), 2017, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/82854.