A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene Signatures
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Authors
Shafi, Adib
Nguyen, Tin
Peyvandipour, Azam
Nguyen, Hung
Draghici, Sorin
Issue Date
2019
Type
Article
Language
Keywords
multi-cohort , multi-omics , meta-analysis , subnetwork identification , GBM , LGG
Alternative Title
Abstract
Although massive amounts of condition-specific molecular profiles are being accumulated in public repositories every day, meaningful interpretation of these data remains a major challenge. In an effort to identify the biomarkers that describe the key biological phenomena for a given condition, several approaches have been developed over the past few years. However, the majority of these approaches either (i) do not consider the known intermolecular interactions, or (ii) do not integrate molecular data of multiple types (e.g., genomics, transcriptomics, proteomics, epigenomics, etc.), and thus potentially fail to capture the true biological changes responsible for complex diseases (e.g., cancer). In addition, these approaches often ignore the heterogeneity and study bias present in independent molecular cohorts. In this manuscript, we propose a novel multi-cohort and multi-omics meta-analysis framework that overcomes all three limitations mentioned above in order to identify robust molecular subnetworks that capture the key dynamic nature of a given biological condition. Our framework integrates multiple independent gene expression studies, unmatched DNA methylation studies, and protein-protein interactions to identify methylation-driven subnetworks. We demonstrate the proposed framework by constructing subnetworks related to two complex diseases: glioblastoma and low-grade gliomas. We validate the identified subnetworks by showing their ability to predict patients' clinical outcome on multiple independent validation cohorts.
Description
Citation
Shafi, A., Nguyen, T., Peyvandipour, A., Nguyen, H., & Draghici, S. (2019). A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene Signatures. Frontiers in Genetics, 10. doi:10.3389/fgene.2019.0015
Publisher
License
Creative Commons Attribution 4.0 International
Journal
Volume
Issue
PubMed ID
ISSN
1664-8021