Publication:
Identifying tissue- and cohort-specific RNA regulatory modules in cancer cells using multitask learning

dc.contributor.coauthorMaass, Philipp G.
dc.contributor.departmentDepartment of Industrial Engineering
dc.contributor.departmentGraduate School of Sciences and Engineering
dc.contributor.departmentSchool of Medicine
dc.contributor.kuauthorGönen, Mehmet
dc.contributor.kuauthorMokhtaridoost, Milad
dc.contributor.schoolcollegeinstituteCollege of Engineering
dc.contributor.schoolcollegeinstituteGRADUATE SCHOOL OF SCIENCES AND ENGINEERING
dc.contributor.schoolcollegeinstituteSCHOOL OF MEDICINE
dc.date.accessioned2024-11-09T12:14:12Z
dc.date.issued2022
dc.description.abstractUnderstanding the underlying biological mechanisms of primary tumors is crucial for predicting how tumors respond to therapies and exploring accurate treatment strategies. miRNA-mRNA interactions have a major effect on many biological processes that are important in the formation and progression of cancer. In this study, we introduced a computational pipeline to extract tissue- and cohort-specific miRNA-mRNA regulatory modules of multiple cancer types from the same origin using miRNA and mRNA expression profiles of primary tumors. Our model identified regulatory modules of underlying cancer types (i.e., cohort-specific) and shared regulatory modules between cohorts (i.e., tissue-specific). 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.
dc.description.fulltextYES
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.indexedbyPubMed
dc.description.issue19
dc.description.openaccessYES
dc.description.publisherscopeInternational
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipM.G. was supported by the Turkish Academy of Sciences (TUBA-GEB.IP
dc.description.sponsorshipThe Young Scientist Award Program) and the Science Academy of Turkey (BAGEP
dc.description.sponsorshipThe Young Scientist Award Program). P.G.M. holds a Canada Research Chair Tier 2 in Non-coding Disease Mechanisms.
dc.description.versionPublisher version
dc.description.volume14
dc.identifier.doi10.3390/cancers14194939
dc.identifier.eissn2072-6694
dc.identifier.embargoNO
dc.identifier.filenameinventorynoIR04034
dc.identifier.quartileQ2
dc.identifier.scopus2-s2.0-85139852070
dc.identifier.urihttps://doi.org/10.3390/cancers14194939
dc.identifier.wos866714300001
dc.keywordsCancer
dc.keywordsMachine learning
dc.keywordsmiRNAs
dc.keywordsmRNAs
dc.keywordsMultitask learning
dc.keywordsRNA regulation
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.grantnoNA
dc.relation.ispartofCancers
dc.relation.urihttp://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/10916
dc.subjectOncology
dc.titleIdentifying tissue- and cohort-specific RNA regulatory modules in cancer cells using multitask learning
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorMokhtaridoost, Milad
local.contributor.kuauthorGönen, Mehmet
local.publication.orgunit1GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
local.publication.orgunit1College of Engineering
local.publication.orgunit1SCHOOL OF MEDICINE
local.publication.orgunit2Department of Industrial Engineering
local.publication.orgunit2School of Medicine
local.publication.orgunit2Graduate School of Sciences and Engineering
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