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HisCoM-PAGE: Hierarchical Structural Component Models for Pathway Analysis of Gene Expression Data.

Lydia Mok1, Yongkang Kim2, Sungyoung Lee3

  • 1Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.

Genes
|November 20, 2019
PubMed
Summary

This study introduces HisCoM-PAGE, a novel model for analyzing gene expression data to identify cancer pathways. It effectively identifies pathways linked to pancreatic cancer survival, outperforming existing methods in simulations.

Keywords:
Hierarchical structured component modelPathway analysisSurvival phenotype

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Cancer Genomics

Background:

  • Existing gene expression analyses often overlook pathway correlations, limiting their scope.
  • Identifying cancer-associated pathways is crucial for understanding disease mechanisms and prognosis.

Purpose of the Study:

  • To develop a novel pathway analysis method, HisCoM-PAGE, that considers gene and pathway hierarchy and correlations.
  • To apply HisCoM-PAGE to identify pathways associated with cancer survival phenotypes, specifically pancreatic cancer.

Main Methods:

  • Proposed a hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE).
  • Focused on survival phenotype analysis.
  • Compared HisCoM-PAGE performance against Gene Set Enrichment Analysis (GSEA), Global Test, and Wald-type Test using simulations and real pancreatic cancer data.

Main Results:

  • HisCoM-PAGE successfully identified pathways associated with pancreatic cancer prognosis in real data analysis.
  • Simulation studies indicated HisCoM-PAGE possesses higher power in detecting causal pathways compared to competing methods.
  • The model effectively accounts for hierarchical gene structures and inter-pathway correlations.

Conclusions:

  • HisCoM-PAGE offers a robust framework for pathway analysis in gene expression data, improving upon single-pathway approaches.
  • The method demonstrates significant potential for identifying prognostic biomarkers and understanding cancer biology.
  • HisCoM-PAGE provides a powerful tool for cancer survival phenotype analysis.