{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Adam RS"],"funding":["New York Stem Cell Foundation","Cancer Research UK","European Research Council","ZonMw","KWF Kankerbestrijding"],"pagination":["2693-2709"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9297751"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["16(14)"],"pubmed_abstract":["Previously, colorectal cancer (CRC) has been classified into four distinct molecular subtypes based on transcriptome data. These consensus molecular subtypes (CMSs) have implications for our understanding of tumor heterogeneity and the prognosis of patients. So far, this classification has been based on the use of messenger RNAs (mRNAs), although microRNAs (miRNAs) have also been shown to play a role in tumor heterogeneity and biological differences between CMSs. In contrast to mRNAs, miRNAs have a smaller size and increased stability, facilitating their detection. Therefore, we built a miRNA-based CMS classifier by converting the existing mRNA-based CMS classification using machine learning (training dataset of n = 271). The performance of this miRNA-assigned CMS classifier (CMS-miRaCl) w"],"journal":["Molecular oncology"],"pubmed_title":["Development of a miRNA-based classifier for detection of colorectal cancer molecular subtypes."],"pmcid":["PMC9297751"],"funding_grant_id":["ERG‐StG 638193","Vidi 016.156.308","10529","C14303/A17197","ERC‐PoC 841112","841112","638193","A19274","UVA2014‐7245"],"pubmed_authors":["Vermeulen L","de Back TR","Markowetz F","Poel D","Ferreira Moreno L","Buffart TE","Torang A","Spronck JMA","Adam RS","Gomez Barila PM","Verheul HMW","Ten Hoorn S","Wang X"],"additional_accession":[]},"is_claimable":false,"name":"Development of a miRNA-based classifier for detection of colorectal cancer molecular subtypes.","description":"Previously, colorectal cancer (CRC) has been classified into four distinct molecular subtypes based on transcriptome data. These consensus molecular subtypes (CMSs) have implications for our understanding of tumor heterogeneity and the prognosis of patients. So far, this classification has been based on the use of messenger RNAs (mRNAs), although microRNAs (miRNAs) have also been shown to play a role in tumor heterogeneity and biological differences between CMSs. In contrast to mRNAs, miRNAs have a smaller size and increased stability, facilitating their detection. Therefore, we built a miRNA-based CMS classifier by converting the existing mRNA-based CMS classification using machine learning (training dataset of n = 271). The performance of this miRNA-assigned CMS classifier (CMS-miRaCl) w","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Jul","modification":"2026-05-27T20:31:13.018Z","creation":"2025-02-19T02:44:29.905Z"},"accession":"S-EPMC9297751","cross_references":{"pubmed":["35298091"],"doi":["10.1002/1878-0261.13210"]}}