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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Dissecting Pathway Disturbances Using Network Topology and Multi-platform Genomics Data.

Yuping Zhang1, M Henry Linder2, Ali Shojaie3

  • 1Department of Statistics, Institute for Systems Genomics, Center for Quantitative Medicine, Institute for Collaboration on Health, Intervention, and Policy, The Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT 06269, USA.

Statistics in Biosciences
|June 30, 2023
PubMed
Summary
This summary is machine-generated.

Integrating multi-omics data, including copy number variants and gene expression, enhances the discovery of cancer pathway disturbances. Analyzing the BRAF pathway across 11 cancer types reveals tumor-specific aberrations and commonalities.

Keywords:
data integrationmulti-platform genomicsnetwork topologypathway analysis

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Area of Science:

  • Oncology
  • Genomics
  • Systems Biology

Background:

  • Complex diseases like cancer arise from cumulative pathway disturbances, not just single gene mutations.
  • Single-platform analyses may miss crucial insights into disease mechanisms.
  • Multi-platform 'omics' data offer a more comprehensive view of biological systems.

Purpose of the Study:

  • To evaluate the benefit of pathway-based analysis using integrated multi-omics data.
  • To investigate the BRAF pathway disturbances across 11 cancer types using genomics, epigenomics, and transcriptomics.
  • To assess the impact of different molecular regulatory elements and network topology on discovering tumor-specific aberrations.

Main Methods:

  • Utilized multi-platform 'omics' data (genomics, epigenomics, transcriptomics) from The Cancer Genome Atlas (TCGA) for 11 cancer types.
  • Focused on the BRAF oncogenetic pathway, analyzing copy number variants (CNVs), methylation, and gene expression.
  • Conducted simulation studies to assess the effects of network topology and multi-omics integration.

Main Results:

  • Integrating CNVs and/or methylation with mRNA expression improved the detection of tumor aberrations.
  • Incorporating CNVs with mRNA expression was more effective than using methylation data.
  • Network topology analysis enhanced the discovery of tumor aberrations.
  • Identified similarities and differences in BRAF pathway disturbances across various cancer types.

Conclusions:

  • Multi-omics data integration, particularly with CNVs and gene expression, significantly enhances the understanding of cancer pathway disturbances.
  • Pathway-based analysis using integrated 'omics' data provides valuable insights into tumor-specific aberrations and cross-cancer commonalities.
  • Network topology plays a crucial role in dissecting complex pathway dysregulations in cancer.