Target-site dynamics explain a large share of apparent microRNA differential expression.

MicroRNA (miRNA) abundance reflects a dynamic balance between biogenesis, target engagement, and decay, yet differential expression analyses typically ignore changes in target-site availability driven by alternative polyadenylation (APA). We introduce MIRNAPEX, an expression-stratification-based machine learning framework that quantifies miRNA regulatory effect sizes from RNA-seq data by integrating target-gene expression with 3'UTR isoform usage to infer effective binding-site dosage. Using pan-cancer training sets, we train models that learn relationships between transcriptomic features and miRNA log fold changes, with APA patterns providing context-dependent complementary information alongside gene expression. When applied to knockdowns of core APA regulators, MIRNAPEX captured widespread 3'UTR shortening and predicted miRNA-specific shifts whose direction was consistent with changes in the APA-associated 3'UTR landscapes of target genes. Analysis of target-directed miRNA degradation interactions further showed that loss of distal decay-trigger sites coincides with increased miRNA abundance, consistent with reduced target-directed miRNA degradation. Together, these findings suggest that apparent miRNA differential expression can be associated with dynamic target-site landscapes in addition to altered miRNA transcription, and that neglecting this dimension can lead to misestimation of regulatory effect sizes.