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Individualized pathway activity algorithm identifies oncogenic pathways in pan-cancer analysis.

Xin Ke1, Hao Wu1, Yi-Xiao Chen2

  • 1Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China.

Ebiomedicine
|April 29, 2022
PubMed
Summary
This summary is machine-generated.

A new method, individualized pathway activity measurement (IPAM), precisely quantifies pathway activity in individual cancer samples. This approach aids in cancer classification, prognosis, and understanding pathological mechanisms for personalized medicine.

Keywords:
KEGG pathwaysOncogenic pathwaysPan-cancer analysisPathway activity algorithm

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Area of Science:

  • Oncology
  • Bioinformatics
  • Systems Biology

Background:

  • Biological pathway dysregulation is implicated in cancer initiation and progression.
  • Existing methods for pathway activity measurement have limitations in group comparisons and batch effect sensitivity.

Purpose of the Study:

  • To introduce a novel method for individualized pathway activity measurement (IPAM).
  • To identify characteristic dysregulated pathways across various cancer types using IPAM.
  • To assess IPAM's performance in cancer classification and prognosis prediction.

Main Methods:

  • Developed IPAM based on ranking gene expression levels in individual samples.
  • Calculated pathway activity for 318 KEGG pathways in 10,528 TCGA tumor/normal samples.
  • Compared IPAM performance against five widely used pathway analysis tools.

Main Results:

  • IPAM precisely quantified pathway activity and outperformed existing tools in pan-cancer analysis, classification, and prognosis.
  • A cancer diagnostic model using IPAM achieved an average ROC-AUC of 92.84% with tumor-educated platelets (TEPs).
  • Identified significantly deregulated pathways associated with survival, including tyrosine metabolism, fatty acid degradation, cell cycle, p53 signaling, and DNA replication.
  • Confirmed the prevalence of metabolic pathways in cancer and identified key regulatory factors (e.g., PPARA, NR1I2, NR1I3).

Conclusions:

  • IPAM offers a robust method for individual pathway activity assessment in cancer.
  • The findings provide insights into cancer's pathological mechanisms.
  • This work supports the development of personalized cancer medicine strategies.