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

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. Consensus molecular subtypes (CMSs) capture heterogeneity of colorectal cancer based on mRNA expression. To retrieve CMS classification from miRNAs, we present a miRNA‐assigned CMS classifier (CMS‐miRaCl), which we validated using several datasets. Important features of this random forest model were linked to microsatellite instability or Wnt signaling. CMS‐miRaCl potentially facilitates CMS classification using miRNA data in clinical workflows and research settings.