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Frequent Subgraph Mining of Functional Interaction Patterns Across Multiple Cancers
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 patt...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7958985/ https://www.ncbi.nlm.nih.gov/pubmed/33691023 |
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author | Durmaz, Arda Henderson, Tim A. D. Bebek, Gurkan |
author_facet | Durmaz, Arda Henderson, Tim A. D. Bebek, Gurkan |
author_sort | Durmaz, Arda |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7958985 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
spelling | pubmed-79589852021-03-15 Frequent Subgraph Mining of Functional Interaction Patterns Across Multiple Cancers Durmaz, Arda Henderson, Tim A. D. Bebek, Gurkan Pac Symp Biocomput Article 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. 2021 /pmc/articles/PMC7958985/ /pubmed/33691023 Text en Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC) 4.0 License. http://creativecommons.org/licenses/by-nc/4.0/ |
spellingShingle | Article Durmaz, Arda Henderson, Tim A. D. Bebek, Gurkan Frequent Subgraph Mining of Functional Interaction Patterns Across Multiple Cancers |
title | Frequent Subgraph Mining of Functional Interaction Patterns Across Multiple Cancers |
title_full | Frequent Subgraph Mining of Functional Interaction Patterns Across Multiple Cancers |
title_fullStr | Frequent Subgraph Mining of Functional Interaction Patterns Across Multiple Cancers |
title_full_unstemmed | Frequent Subgraph Mining of Functional Interaction Patterns Across Multiple Cancers |
title_short | Frequent Subgraph Mining of Functional Interaction Patterns Across Multiple Cancers |
title_sort | frequent subgraph mining of functional interaction patterns across multiple cancers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7958985/ https://www.ncbi.nlm.nih.gov/pubmed/33691023 |
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