MFDSMC: Accurate Identification of Cancer-Driver Synonymous Mutations Using Multiperspective Feature Representation Learning

Synonymous mutations do not change amino acid sequences, but they can drive cancer by influencing splicing, mRNA structure, translation efficiency, and other molecular mechanisms. Although driver synonymous mutations are significantly outnumbered by functionally neutral passenger mutations in cancer, their accurate discrimination is critical to understanding tumorigenesis. In this study, we developed multiperspective feature-based predictor for driver synonymous mutation in cancer (MFDSMC), a computational framework designed to improve the prediction of human cancer-driver synonymous mutations. First, we curated synonymous mutations from public cancer mutation databases to construct our data sets. For each mutation, we systematically characterized features across four biologically informed perspectives: sequence context, evolutionary conservation, epigenetic modifications, and regulatory/functional predictions. The optimal feature subset was identified through a feature importance ranking and sequential forward selection. After multiple machine learning classifiers were evaluated, XGBoost was selected to build the prediction model. Results revealed that the multiperspective fusion model outperformed models relying on single-perspective features or lacking any individual feature category. Notably, newly introduced epigenetic features derived from experimental sequencing data, combined with regulatory/functional prediction features, collectively enhanced the model's performance. When tested on two independent test sets and a curated data set of experimentally confirmed driver synonymous mutations, MFDSMC exhibited superior performance compared to existing state-of-the-art methods, providing a novel solution for precise prediction of cancer-driver synonymous mutations in genomic research and clinical applications. MFDSMC is available at https://github.com/xialab-ahu/MFDSMC.