MicroRNA (miRNA) abundance reflects a dynamic balance between biogenesis, target engagement and decay, yet differential expression (DE) analyses typically ignore changes in target‐site availability driven by alternative polyadenylation (APA). We introduce MIRNAPEX, an interpretable expression-stratification-based machine learning framework that quantifies the effect size of miRNA regulatory activity from RNA-seq by integrating target-gene expression with 3′UTR isoform usage to infer binding-site dosage. Using pan-cancer training sets, we fit regularized linear models to learn robust relationships between transcriptomic features and miRNA log-fold changes, with APA patterns adding clear predictive power beyond expression alone. When applied to knockdowns of core APA regulators, MIRNAPEX captured widespread 3′UTR shortening and correctly anticipated distinct, miRNA-specific shifts whose direction and magnitude mirrored the APA-driven change in site availability. Analysis of target-directed miRNA degradation interactions further showed that loss of distal decay-trigger sites coincides with higher miRNA abundance, consistent with a reduced degradation rate. Together these findings reveal that apparent DE of miRNAs can arise from dynamic changes in target-site landscapes rather than altered miRNA transcription, and that ignoring this aspect in conventional analysis workflows can lead to misestimation of the true effect size of gene-expression regulation.
Cihan et al. (Wed,) studied this question.