title: Inferring probabilistic miRNA-mRNA interaction signatures in cancers: a role-switch approach.
abstract: Aberrant microRNA (miRNA) expression is implicated in tumorigenesis. The underlying mechanisms are unclear because the regulations of each miRNA on potentially hundreds of mRNAs are sample specific. We describe a novel approach to infer Probabilistic MiRNA-mRNA Interaction Signature ('ProMISe') from a single pair of miRNA-mRNA expression profile. Our model considers mRNA and miRNA competition as a probabilistic function of the expressed seeds (matches). To demonstrate ProMISe, we extensively exploited The Cancer Genome Atlas data. As a target predictor, ProMISe identifies more confidence/validated targets than other methods. Importantly, ProMISe confers higher cancer diagnostic power than using expression profiles alone. Gene set enrichment analysis on averaged ProMISe uniquely revealed respective target enrichments of oncomirs miR-21 and 145 in glioblastoma and ovarian cancers. Moreover, comparing matched breast (BRCA) and thyroid (THCA) tumor/normal samples uncovered thousands of tumor-related interactions. For example, ProMISe-BRCA network involves miR-155/183/21, which exhibits higher ProMISe coupled with coherently higher miRNA expression and lower target expression; oncomirs miR-221/222 in the ProMISe-THCA network engage with many downregulated target genes. Together, our probabilistic approach of integrating expression and sequence scores establishes a functional link between the aberrant miRNA and mRNA expression, which was previously under-appreciated due to the methodological differences.
authors.affiliation: Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4, Canada The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
authors.forename: Yue
authors.initials: Y
authors.lastname: Li
authors.affiliation: The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
authors.forename: Cheng
authors.initials: C
authors.lastname: Liang
authors.affiliation: Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4, Canada The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada.
authors.forename: Ka-Chun
authors.initials: KC
authors.lastname: Wong
authors.affiliation: The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A4, Canada.
authors.forename: Ke
authors.initials: K
authors.lastname: Jin
authors.affiliation: Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4, Canada The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A4, Canada Banting and Best Department of Medical Research, University of Toronto, Toronto, ON M5S 3E1, Canada zhaolei.zhang@utoronto.ca.
authors.forename: Zhaolei
authors.initials: Z
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism*; D045744:Cell Line, Tumor; D005260:Female; D015870:Gene Expression; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D057809:HEK293 Cells; D006801:Humans; D035683:MicroRNAs / Q000235:genetics / Q000378:metabolism*; D008957:Models, Genetic; D015233:Models, Statistical; D009857:Oncogenes; D034622:RNA Interference; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism*; D013964:Thyroid Neoplasms / Q000235:genetics / Q000378:metabolism*; D059467:Transcriptome
chemical_list: D014408:Biomarkers, Tumor; D035683:MicroRNAs; D012333:RNA, Messenger