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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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A graph-based multi-sample test for identifying pathways associated with cancer progression.

Qingyang Zhang1, Ghadeer Mahdi2, Jian Tinker1

  • 1Department of Mathematical Sciences, University of Arkansas, USA.

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|June 11, 2020
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Summary
This summary is machine-generated.

This study introduces a new computational method to analyze dynamic pathway changes during cancer progression. The approach identifies key pathways, like cell cycle and ERBB signaling, crucial for serous ovarian cancer development.

Keywords:
Edge-count testPathway analysisSerous ovarian cancerThe Cancer Genome AtlasTumorigenesis

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Area of Science:

  • Oncology
  • Computational Biology
  • Bioinformatics

Background:

  • Cancer arises from alterations in multiple genes and pathways, necessitating an understanding of oncogenic and tumor suppressor roles.
  • Existing computational models often rely on knowledge-based enrichment analyses, limiting flexibility for user-defined pathways.
  • Dynamic pathway alterations during cancer evolution require robust analytical methods.

Purpose of the Study:

  • To develop a flexible, data-driven computational approach for testing dynamic pathway changes during cancer progression.
  • To apply this method to analyze (epi)genetic alterations in KEGG pathways during serous ovarian cancer development.
  • To identify specific pathways driving cancer progression at different stages.

Main Methods:

  • Developed a nonparametric, data-driven approach based on pathway expansion and refinement.
  • Implemented a graph-based multivariate test for analyzing dynamic pathway changes.
  • Integrated gene expression, copy number, and DNA methylation data from the Cancer Genome Atlas (TCGA).

Main Results:

  • Identified nine pathways significantly associated with serous ovarian cancer progression.
  • Highlighted the involvement of cell cycle, ERBB, JAK-STAT, and p53 signaling pathways.
  • Determined that specific pathways contribute to distinct transition stages (e.g., cell cycle/ERBB in early stages, ECM/apoptosis in later stages).

Conclusions:

  • The proposed computational pipeline effectively detects stage-specific pathways driving cancer.
  • Provides novel insights into the molecular mechanisms of cancer initiation and progression.
  • Offers a powerful tool for analyzing user-defined gene sets and pathways in cancer research.