ArticleMolecular oncology2022
Development of a miRNA-based classifier for detection of colorectal cancer molecular subtypes.
Article in Molecular oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Multidimensional immune ecological subtyping identifies RUNX1 as a prognostic factor in uveal melanoma.Discover oncology · 2026Article
- The emerging role of machine learning-based methods in cancer classification using microRNA.Biochemistry and biophysics reports · 2026Review
- Tumor microenvironment-driven microRNA dysregulation: Key interactions in colorectal cancer progression.World journal of gastrointestinal oncology · 2026Review
- Multi-omic characterization of consensus molecular subtype 1 (CMS1) colorectal cancer with dampened immune response improves precision medicine.Molecular oncology · 2025Article
- NanoCMSer: a consensus molecular subtype stratification tool for fresh-frozen and paraffin-embedded colorectal cancer samples.Molecular oncology · 2025Article
- Circulating miRNA Signature Predicts Cancer Incidence in Lynch Syndrome-A Pilot Study.Cancer prevention research (Philadelphia, Pa.) · 2024Article
- A Novel Molecular Analysis Approach in Colorectal Cancer Suggests New Treatment Opportunities.Cancers · 2023Article
- Circular RNA ATP2C1 (has_circ_0005797) sponges miR-432/miR-335 to promote breast cancer progression through regulating CCND1 expression.American journal of cancer research · 2023Article
- Article
- Circ-TRIO promotes TNBC progression by regulating the miR-432-5p/CCDC58 axis.Cell death & disease · 2022Article
- Characterizing the molecular heterogeneity of clear cell renal cell carcinoma subgroups classified by miRNA expression profile.Frontiers in molecular biosciences · 2022Article
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Authors and funding
13 authors.
Funding
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) 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.
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