Related Experiment Video
Updated: Nov 14, 2025
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
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.
Abstract:
Molecular mechanisms characterizing cancer development and progression are complex and process through thousands of interacting elements in the cell. Understanding the underlying structure of interactions requires the integration of cellular networks with extensive combinations of dysregulation patterns. Recent pan-cancer studies focused on identifying common dysregulation patterns in a confined set of pathways or targeting a manually curated set of genes. However, the complex nature of the disease presents a challenge for finding pathways that would constitute a basis for tumor progression and requires evaluation of subnetworks with functional interactions. Uncovering these relationships is critical for translational medicine and the identification of future therapeutics. We present a frequent subgraph mining algorithm to find functional dysregulation patterns across the cancer spectrum. We mined frequent subgraphs coupled with biased random walks utilizing genomic alterations, gene expression profiles, and protein-protein interaction networks. In this unsupervised approach, we have recovered expert-curated pathways previously reported for explaining the underlying biology of cancer progression in multiple cancer types. Furthermore, we have clustered the genes identified in the frequent subgraphs into highly connected networks using a greedy approach and evaluated biological significance through pathway enrichment analysis. Gene clusters further elaborated on the inherent heterogeneity of cancer samples by both suggesting specific mechanisms for cancer type and common dysregulation patterns across different cancer types. Survival analysis of sample level clusters also revealed significant differences among cancer types (p < 0.001). These results could extend the current understanding of disease etiology by identifying biologically relevant interactions.Supplementary Information: Supplementary methods, figures, tables and code are available at https://github.com/bebeklab/FSM_Pancancer.
Insights
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.
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.
More Related Videos
Related Concept Videos
02:26Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
01:21Cancer Survival Analysis
02:53Adaptive Mechanisms in Cancer Cells
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
02:04Protein-protein Interfaces
02:43Mouse Models of Cancer Study
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
01:05Cancer-Critical Genes II: Tumor Suppressor Genes
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...