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MNMO: discover driver genes from a multi-omics data based-multi-layer network.

Zheng Deng1,2, Jingli Wu1,2,3, Xiaorong Chen4

  • 1Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin 541004, China.

Bioinformatics (Oxford, England)
|March 28, 2025
PubMed
Summary

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This summary is machine-generated.

We developed MNMO, a multi-layer network model using multi-omics data to identify cancer driver genes. MNMO outperforms existing methods in identifying genes crucial for cancer development and progression.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomic variations in cancer driver genes fuel cancer as a public health issue.
  • Identifying driver genes is essential for biomarker discovery and personalized cancer therapy.

Purpose of the Study:

  • To propose a novel prediction method, MNMO (multi-layer network model), for identifying cancer driver genes using multi-omics data.
  • To evaluate the performance of MNMO against existing state-of-the-art methods.

Main Methods:

  • Constructed a dynamically adjusted four-layer network integrating microRNAs (miRNAs) and three gene types.
  • Developed and calculated three scores (control capacity, mutation, network) using harmonic mean for an integrated gene score.
  • Validated the method on three real cancer datasets.

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Main Results:

  • MNMO demonstrated superior identification performance compared to six state-of-the-art methods across most scenarios.
  • Genes prioritized by MNMO showed better alignment with benchmark data and were strongly associated with cancer development and progression.
  • Extended versions of MNMO further improved performance, particularly for identifying tissue-specific genes.

Conclusions:

  • MNMO is an effective method for identifying cancer driver genes.
  • The model's ability to integrate multi-omics data provides a robust approach for cancer research.
  • Extended MNMO versions offer enhanced capabilities for tissue-specific gene identification.