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

Lihua Wang1,2, Chen Ye1, Na Cheng3

  • 1Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China.

Journal of Chemical Information and Modeling
|June 6, 2025
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Summary
This summary is machine-generated.

We developed a computational tool, MFDSMC, to accurately identify cancer-driving synonymous mutations. This predictor integrates multiple biological features to distinguish driver mutations from neutral ones, aiding cancer research.

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Synonymous mutations, despite not altering amino acid sequences, can drive cancer through various molecular mechanisms.
  • Distinguishing driver synonymous mutations from passenger mutations is crucial for understanding tumorigenesis.

Purpose of the Study:

  • To develop a computational framework, MFDSMC, for improved prediction of human cancer-driver synonymous mutations.
  • To integrate diverse biological features for enhanced predictive accuracy.

Main Methods:

  • Curated synonymous mutations from public databases.
  • Characterized features across sequence context, evolutionary conservation, epigenetic modifications, and regulatory/functional predictions.
  • Utilized machine learning, specifically XGBoost, after feature selection and evaluation.

Main Results:

  • The multiperspective fusion model significantly outperformed single-perspective models.
  • Epigenetic and regulatory/functional features notably improved prediction performance.
  • MFDSMC demonstrated superior performance on independent test sets compared to existing methods.

Conclusions:

  • MFDSMC offers a novel and precise solution for predicting cancer-driver synonymous mutations.
  • The framework has potential applications in genomic research and clinical settings.
  • Accurate identification of driver synonymous mutations is critical for cancer studies.