Alternative Polyadenylation patterns for novel gene discovery and classification in cancer

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