Identifying Tissue- and Cohort-Specific RNA Regulatory Modules in Cancer Cells Using Multitask Learning

Milad Mokhtaridoost; Philipp G Maass; Mehmet Gönen

doi:10.3390/cancers14194939

Identifying Tissue- and Cohort-Specific RNA Regulatory Modules in Cancer Cells Using Multitask Learning

Cancers (Basel). 2022 Oct 9;14(19):4939. doi: 10.3390/cancers14194939.

Authors

Milad Mokhtaridoost^{1

2}, Philipp G Maass^{1

3}, Mehmet Gönen^{4

5}

Affiliations

¹ Genetics & Genome Biology Program, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada.
² Graduate School of Sciences and Engineering, Koç University, İstanbul 34450, Turkey.
³ Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.
⁴ Department of Industrial Engineering, College of Engineering, Koç University, İstanbul 34450, Turkey.
⁵ School of Medicine, Koç University, İstanbul 34450, Turkey.

Abstract

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.

Keywords: RNA regulation; cancer; mRNAs; machine learning; miRNAs; multitask learning.

Grants and funding

M.G. was supported by the Turkish Academy of Sciences (TÜBA-GEBİP; The Young Scientist Award Program) and the Science Academy of Turkey (BAGEP; The Young Scientist Award Program). P.G.M. holds a Canada Research Chair Tier 2 in Non-coding Disease Mechanisms.