SynthesisScientific reports2022
Tumor microenvironment-aware, single-transcriptome prediction of microsatellite instability in colorectal cancer using meta-analysis.
Synthesis in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- The Genetic Landscape of Colorectal Cancer: From Molecular Alterations to Therapeutic Decision Pathways.Cancers · 2026Review
- CLEC2B-KLRB1 axis acts as an immune checkpoint, governing the exhaustion of CD8Journal for immunotherapy of cancer · 2026Article
- Multi-omics evaluation of cell lines as models for metastatic prostate cancer.Communications biology · 2026Article
- Detecting microsatellite instability by length comparison of microsatellites in the 3' untranslated region with RNA-seq.Briefings in bioinformatics · 2024Article
- Genome-wide screening and functional validation of methylation barriers near promoters.Nucleic acids research · 2024Article
- Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics.NPJ precision oncology · 2024Article
- Article
- PCSK9 facilitates melanoma pathogenesis via a network regulating tumor immunity.Journal of experimental & clinical cancer research : CR · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Detecting microsatellite instability (MSI) in colorectal cancers (CRCs) is essential because it is the determinant of treatment strategies, including immunotherapy and chemotherapy. Yet, no attempt has been made to exploit transcriptomic profile and tumor microenvironment (TME) of it to unveil MSI status in CRC. Hence, we developed a novel TME-aware, single-transcriptome predictor of MSI for CRC, called MAP (Microsatellite instability Absolute single sample Predictor). MAP was developed utilizing recursive feature elimination-random forest with 466 CRC samples from The Cancer Genome Atlas, and its performance was validated in independent cohorts, including 1118 samples. MAP showed robustness and predictive power in predicting MSI status in CRC. Additional advantages for MAP were demonstrated through comparative analysis with existing MSI classifier and other cancer types. Our novel approach will provide access to untouched vast amounts of publicly available transcriptomic data and widen the door for MSI CRC research and be useful for gaining insights to help with translational medicine.
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Registered trials
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