ApproachCancerMiner.org is a comprehensive resource for discovery and prioritization of functional miRNA-mRNA target interactions and relationships in human cancer using molecular datasets from The Cancer Genome Atlas.
Analysis in single cancer types. Our method first evaluates expression relationships of miRNAs and mRNAs in individual cancer types. For each miRNA-mRNA pair, we measure the association between miRNA and mRNA expression across the set of patient tumors using a multivariate linear model that also factors in variation (noise) in mRNA expression induced by changes in DNA copy number and promoter methylation at the mRNA gene locus.
Analysis across cancer types. To explore the hypothesis that individual miRNA-target relationships are active in multiple cancer types and may regulate common cancer traits, we use a rank-based statistical score, the REC score. The approach ranks miRNA-mRNA expression associations in the context of miRNA and cancer type and evaluates the null hypothesis that no association exists between the miRNA-mRNA pair in all cancer types. This rank-based approach ensures that individual cancer types are weighted equally, and limits bias from cancer data sets with large sample sizes or from strong associations measured in only a single cancer type. A strong negative REC score reflects that the miRNA-mRNA pair generally show anti-correlation across the studied cancer types, and this is evidence of a functional miRNA target relationship.
For details about the method, and for citing the resource, please refer to the paper:
- Anders Jacobsen, Joachim Silber, Girish Harinath, Jason T Huse, Nikolaus Schultz, Chris Sander. Analysis of microRNA-target interactions across diverse cancer types. Nature Structural & Molecular Biology (2013).