Development of a miRNA-based classifier for detection of colorectal cancer molecular subtypes

Ronja S. Adam, Dennis Poel, Leandro Ferreira Moreno, Joey M. A. Spronck, Tim R. de Back, Arezo Torang, Patricia M. Gomez Barila, Sanne ten Hoorn, Florian Markowetz, Xin Wang, Henk M. W. Verheul, Tineke E. Buffart, Louis Vermeulen

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)


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) was evaluated in several datasets, achieving an overall accuracy of ~ 0.72 (0.6329–0.7987) in the largest dataset (n = 158). To gain insight into the biological relevance of CMS-miRaCl, we evaluated the most important features in the classifier. We found that miRNAs previously reported to be relevant in microsatellite-instable CRCs or Wnt signaling were important features for CMS-miRaCl. Following further studies to validate its robustness, this miRNA-based alternative might simplify the implementation of CMS classification in clinical workflows.
Original languageEnglish
Pages (from-to)2693-2709
Number of pages17
JournalMolecular Oncology
Issue number14
Early online date2022
Publication statusPublished - Jul 2022


  • colorectal cancer
  • consensus molecular subtypes
  • miRNA
  • microRNA

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