Abstract
Alternative transcript cleavage and polyadenylation is linked to cancer cell transformation, proliferation and outcome. This has led researchers to develop methods to detect and bioinformatically analyse alternative polyadenylation as potential cancer biomarkers. If incorporated into standard prognostic measures such as gene expression and clinical parameters, these could advance cancer prognostic testing and possibly guide therapy. In this review, we focus on the existing methodologies, both experimental and computational, that have been applied to support the use of alternative polyadenylation as cancer biomarkers.
Highlights
Introduction70% of mammalian genes harbour multiple cleavage and polyadenylation sites i.e., poly(A) sites [7,8,9,10]
Since the discovery of APA in immunoglobulin M (IgM) and dihydrofolate reductase (DHFR) genes in 1980 [19,20], it has become clear that APA is the norm rather than the exception
APAatlas [27] provides a resource database of APA inferred from RNA-seq data in the Genotype-Tissue Expression (GTEx) project [102] using the Dynamic analysis of Alternative PolyAdenylation from RNA-Seq (DaPars) [25] bioinformatic approach
Summary
70% of mammalian genes harbour multiple cleavage and polyadenylation sites i.e., poly(A) sites [7,8,9,10] These sites can cause differential expression of mRNA transcripts by influencing their nuclear export, stability, subcellular localization, interaction with microRNAs, RNA binding proteins (RBPs), long non-coding RNAs (lncRNAs) and translation efficiency [11,12,13,14,15]. In the case of tandem APA, the poly(A) sites reside in the 30 UTRs resulting in transcript isoforms with invariant protein-coding sequence but 30 UTRs of different lengths. UTR length. (D) When a poly(A) signal is recognised in the intronic region, protein isoforms with distinct Carboxy-termini are generated in a process termed as CR-APA
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