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MicroRNAs (miRNAs) play a crucial role in tumorigenesis and development through their effects on target genes. The characterization of miRNA-gene interactions will lead to a better understanding of cancer mechanisms. Many computational methods have been developed to infer miRNA targets with/without expression data. Because expression datasets are in general limited in size, most existing methods concatenate datasets from multiple studies to form one aggregated dataset to increase sample size and power. However, such simple aggregation analysis results in identifying miRNA-gene interactions that are mostly common across datasets, whereas specific interactions may be missed by these methods. Recent releases of The Cancer Genome Atlas data provide paired expression profiling of miRNAs and genes in multiple tumors with sufficiently large sample size. To study both common and cancer-specific interactions, it is desirable to develop a method that can jointly analyze multiple cancers to study miRNA-gene interactions without combining all the data into one single dataset.

Joint analysis of expression profiles from multiple cancers improves the identification of microRNA–gene interactions