| Autor: |
Mokhtaridoost M; Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada.; Graduate School of Sciences and Engineering, Koç University, İstanbul 34450, Turkey., Maass PG; Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada.; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada., Gönen M; Department of Industrial Engineering, College of Engineering, Koç University, İstanbul 34450, Turkey.; School of Medicine, Koç University, İstanbul 34450, Turkey. |
| Abstrakt: |
MicroRNA (miRNA) alterations significantly impact the formation and progression of human cancers. miRNAs interact with messenger RNAs (mRNAs) to facilitate degradation or translational repression. Thus, identifying miRNA-mRNA regulatory modules in cohorts of primary tumor tissues are fundamental for understanding the biology of tumor heterogeneity and precise diagnosis and treatment. We established a multitask learning sparse regularized factor regression (MSRFR) method to determine key tissue- and cohort-specific miRNA-mRNA regulatory modules from expression profiles of tumors. MSRFR simultaneously models the sparse relationship between miRNAs and mRNAs and extracts tissue- and cohort-specific miRNA-mRNA regulatory modules separately. We tested the model's ability to determine cohort-specific regulatory modules of multiple cancer cohorts from the same tissue and their underlying tissue-specific regulatory modules by extracting similarities between cancer cohorts (i.e., blood, kidney, and lung). We also detected tissue-specific and cohort-specific signatures in the corresponding regulatory modules by comparing our findings from various other tissues. We show that MSRFR effectively determines cancer-related miRNAs in cohort-specific regulatory modules, distinguishes tissue- and cohort-specific regulatory modules from each other, and extracts tissue-specific information from different cohorts of disease-related tissue. Our findings indicate that the MSRFR model can support current efforts in precision medicine to define tumor-specific miRNA-mRNA signatures. |
