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Frequent Subgraph Mining of Functional Interaction Patterns Across Multiple Cancers.

Arda Durmaz1, Tim A D Henderson, Gurkan Bebek

  • 1Systems Biology and Bioinformatics Graduate Program, Case Western Reserve University, 10900 Euclid Ave., Cleveland OH 44106, USA5The Department of Translational Hematology and Oncology Research, Taussig Cancer Institute, Cleveland Clinic, 9500 Euclid Ave., Cleveland, OH 44195, USA, axd497@case.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|March 10, 2021
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This study introduces a novel algorithm to identify functional dysregulation patterns in cancer by analyzing molecular interactions. The findings reveal common and specific mechanisms driving cancer progression across various tumor types.

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Area of Science:

  • Computational biology
  • Genomics
  • Cancer research

Background:

  • Cancer development involves complex molecular interactions and dysregulation patterns.
  • Existing pan-cancer studies often focus on limited pathways or curated gene sets.
  • Identifying functional subnetworks is crucial for understanding tumor progression and developing therapeutics.

Purpose of the Study:

  • To develop and apply a frequent subgraph mining algorithm for uncovering functional dysregulation patterns across the cancer spectrum.
  • To integrate genomic alterations, gene expression, and protein-protein interaction networks.
  • To identify novel pathways and gene clusters associated with cancer progression and heterogeneity.

Main Methods:

  • Frequent subgraph mining coupled with biased random walks.
  • Integration of genomic alteration data, gene expression profiles, and protein-protein interaction networks.
  • Unsupervised learning approach, followed by gene clustering and pathway enrichment analysis.

Main Results:

  • Successfully recovered known cancer-related pathways.
  • Identified gene clusters highlighting cancer-specific and common dysregulation patterns.
  • Survival analysis demonstrated significant differences in patient outcomes based on identified clusters (p < 0.001).

Conclusions:

  • The algorithm effectively identifies biologically relevant interaction patterns underlying cancer progression.
  • The findings enhance understanding of cancer etiology and heterogeneity.
  • This approach aids in discovering potential therapeutic targets across diverse cancer types.