Rewiring of transcriptional regulatory networks has been implicated in many biological and pathological processes. However, most current methods for detecting rewiring events (differential network connectivity) are not optimized for miRNA-mediated gene regulation and fail to systematically examine predicted target genes in study designs with multiple experimental or phenotypic groups. We developed a novel method to address these shortcomings. The method first estimates miRNA-gene expression correlations with Spatial Quantile Normalization to remove the mean-correlation relationship. Then, for each miRNA, genes are ranked by their correlation strength per experimental group. Enrichment patterns of predicted target genes are compared using the Anderson-Darling test and significance levels are estimated via permutation. Finally, context-specific target genes for each miRNA are identified with target prioritization based on the correlation strength between miRNA and predicted target genes within each group. In miR-155 KO RNA-seq data from four mice immune cell types, our method captures the known cell-specific regulatory differences of miR-155, and prioritized targets are involved in functional pathways with cell-type specificity. Moreover, in TCGA BRCA data, our method identified subtype-specific targets that were uniquely altered by miRNA perturbations in cell lines of the same subtype. Our work provides a new approach to characterize miRNA-mediated gene regulatory network rewiring across multiple groups from transcriptomic profiles. The method may offer novel insights into cell-type and cancer subtype-specific miRNA regulatory roles.