ArticleBMC genomics2021
A novel method to identify gene interaction patterns.
Article in BMC genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- New insights into protein-protein interaction modulators in drug discovery and therapeutic advance.Signal transduction and targeted therapy · 2024Review
- SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks.BMC bioinformatics · 2022Article
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
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Authors and funding
6 authors.
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Abstract
backgroundGene interaction patterns, including modules and motifs, can be used to identify cancer specific biomarkers and to reveal the mechanism of tumorigenesis. Most of the existing module network inferencing methods focus on gene independent functional patterns, while the studies of overlapping characteristics between modules are lacking. The objective of this study was to reveal the functional overlapping patterns in gene modules, helping elucidate the regulatory relationship between overlapping genes and communities, as well as to explore cancer formation and progression.
resultsWe analyzed six cancer datasets from The Cancer Genome Atlas and obtained three kinds of gene functional modules for each cancer, including Independent-Community, Dependent-Community and Merged-Community. In the six cancers, 59(3.5%) Independent-Communities were identified, while 1631(96.5%) Dependent-Communities were acquired. Compared with Lemon-Tree and K-Means, the gene communities identified by our method were enriched in more known GO categories with lower p-values. Meanwhile, those identified distinguishing communities can significantly distinguish the survival prognostic of patients by Kaplan-Meier analysis. Furthermore, identified driver genes in the gene communities can be considered as biomarkers which can accurately distinguish the tumour or normal samples for each cancer type.
conclusionsIn all identified communities, Dependent-Communities are the majority. Our method is more effective than the other two methods which do not consider the overlapping characteristics of modules. This indicates that overlapping genes are located in different specific functional groups, and a communication bridge is established between the communities to construct a comprehensive carcinogenesis.
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