MethylMSI: Prediction of microsatellite instability based on DNA methylation profile and SVM model
Microsatellite instability (MSI) has gained increasing attention as a promising biomarker for cancer immunotherapy in diverse cancer types. As a result of DNA mismatch repair (MMR) deficiency, MSI tumors show a hypermutator phenotype. Based on this feature, many computational methods have been developed to predict MSI with DNA sequencing data or mRNA expression data. Using the methylation data from The Cancer Genome Atlas (TCGA) program, we developed MethylMSI, a support vector machine (SVM) model based tool for MSI classification in three cancer cohorts (colorectal, stomach and endometrial cancers). We demonstrated that MethylMSI has high MSI prediction accuracy in all three cohorts. Analysis of underlying features of the SVM model showed that hypermethylation of MLH1 is an important indicator for MSI in colorectal and endometrial cancers; while in stomach cancer, other MMR deficiency might be more important for MSI formation. We concluded that SVM had the potential to be the best model for predicting MSI status from DNA methylation data. In addition, an easy handling software is now freely available at https://github.com/Huanglab-ai/MethylMSI.
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Funding
Sun Yat-sen University Grant 76160\u201354990001Guangdong Provincial Natural Science Foundation Grant 2022A1515012286Guangdong Provincial Natural Science Foundation Grant 2025A1515012335Guangdong Provincial Natural Science Foundation Grant 2022A1515110268Shenzhen Science and Technology Innovation Program Grant JCYJ20250604174809012Shenzhen Science and Technology Innovation Program Grant JCYJ20220530145217040Shenzhen Science and Technology Innovation Program Grant JCYJ20220530145217039