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A feature extraction framework for discovering pan-cancer driver genes based on multi-omics data.

Xiaomeng Xue1, Feng Li1, Junliang Shang1

  • 1School of Computer Science Qufu Normal University Rizhao China.

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|February 12, 2026
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Summary

This study introduces a novel framework to identify cancer driver genes using multi-omics data and protein-protein interaction networks. The method effectively predicts potential cancer genes, aiding precision oncology and tumor treatment.

Keywords:
cancer driver genesfeature extractionmulti‐omics datanetwork propagationpan‐cancer

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Identifying tumor driver genes is crucial for precision oncology.
  • Distinguishing driver genes from large genomic datasets is challenging.
  • Multi-omics data and protein-protein interaction (PPI) networks offer rich information.

Purpose of the Study:

  • To develop a feature extraction framework for discovering pan-cancer driver genes.
  • To integrate multi-omics data with PPI networks for enhanced gene prediction.
  • To improve the accuracy of identifying genes critical for cancer development.

Main Methods:

  • Constructed a framework using multi-omics data (mutations, gene expression, copy number variants, DNA methylation) and PPI networks.
  • Employed network propagation algorithms to mine functional information within PPI networks.
  • Extracted various features including distribution, TOPSIS, and SetExpan features.
  • Utilized the lightGBM classification algorithm for gene prediction.

Main Results:

  • The proposed framework outperforms existing methods in predicting pan-cancer driver genes.
  • Achieved superior performance based on the area under the check precision-recall curve (AUPRC).
  • Demonstrated robust effectiveness across different PPI networks.

Conclusions:

  • The framework is effective in predicting potential cancer genes.
  • Provides valuable insights for tumor diagnosis and treatment strategies.
  • Highlights the utility of integrating multi-omics data and network analysis for cancer research.