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Interactive gene identification for cancer subtyping based on multi-omics clustering.

Xiucai Ye1, Tianyi Shi2, Yaxuan Cui1

  • 1Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan.

Methods (San Diego, Calif.)
|February 21, 2023
PubMed
Summary

This study introduces a new framework to find interactive genes in cancer using multi-omics data. It identifies key genes within cancer subtypes, aiding in understanding tumor heterogeneity and improving patient outcomes.

Keywords:
Cancer subtypingGene co-expression networkInteractive genes identificationMulti-omics clustering

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Area of Science:

  • Computational biology
  • Genomics
  • Cancer research

Background:

  • Multi-omics databases enable exploration of complex cancer systems.
  • Existing methods often identify genes separately, overlooking crucial gene interactions in multigenic diseases.

Purpose of the Study:

  • To develop a novel learning framework for identifying interactive genes from multi-omics data.
  • To address the limitations of current methods by considering gene interactions in cancer development.

Main Methods:

  • Integration of diverse omics data based on similarity.
  • Application of spectral clustering for cancer subtype identification.
  • Construction of gene co-expression networks per subtype and detection of interactive genes using subgraph learning on eigenvector properties.

Main Results:

  • Identification of interactive genes specific to each cancer subtype from multi-omics data.
  • Gene ontology enrichment analysis revealed distinct biological processes and pathways for genes in different subtypes.
  • Detected genes demonstrate significant relationships with cancer development.

Conclusions:

  • The proposed framework effectively identifies interactive genes, offering insights into tumor heterogeneity.
  • Findings provide a basis for understanding subtype-specific cancer mechanisms.
  • This approach is expected to contribute to improved patient survival strategies.