Alternative Polyadenylation patterns for novel gene discovery and classification in cancer

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

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Department of Biology, Conference / Seminar