MicroRNAs (miRNAs) comprise a gene-regulatory network through sequence complementarity with target mRNAs. Previous studies have shown that mammalian miRNAs decrease many target mRNA levels and reduce protein production predominantly by target mRNA destabilization. However, it has not yet been fully assessed whether this scheme is widely applicable to more realistic conditions with multiple miRNA fluctuations. By combining two analytical frameworks for detecting the enrichment of gene sets, Gene Set Enrichment Analysis (GSEA) and Functional Assignment of miRNAs via Enrichment (FAME), we developed GSEA–FAME analysis (GFA), which enables the prediction of miRNA activities from mRNA expression data using rank-based enrichment analysis and weighted evaluation of miRNA–mRNA interactions. This cooperative approach delineated a better widespread correlation between miRNA expression levels and predicted miRNA activities in cancer transcriptomes, thereby providing proof-of-concept of the mRNA-destabilization scenario. In an integrative analysis of The Cancer Genome Atlas (TCGA) multidimensional data including profiles of both mRNA and miRNA, we also showed that GFA-based inference of miRNA activity could be used for the selection of prognostic miRNAs in the development of cancer survival prediction models. This approach proposes a next-generation strategy for the interpretation of miRNA function and identification of target miRNAs as biomarkers and therapeutic targets.