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Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures
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Published on: December 26, 2016

Identification and Characterization of Cancer-Related Risk Metabolic Subpathways Reveal Their Functional Significance

Hongying Zhao1, Jinxing Yan1, Ming Wu1

  • 1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.

International Journal of Molecular Sciences
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

We identified cancer-specific metabolic pathways linked to disease progression. This approach reveals core metabolic modules and potential biomarkers for improved cancer patient stratification.

Keywords:
biomarkersmetabolismsubpathways

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Area of Science:

  • Metabolic pathways
  • Computational biology
  • Cancer research

Background:

  • Cancer progression involves significant metabolic changes.
  • Identifying these metabolic alterations is crucial for understanding cancer development.
  • Existing methods may not fully capture the complexity of cancer metabolism.

Purpose of the Study:

  • To develop a novel computational approach for identifying cancer-related metabolic subpathways (CMSubpathway).
  • To identify core metabolic modules and potential biomarkers for cancer.
  • To investigate the role of these metabolic modules in cancer progression and immune infiltration.

Main Methods:

  • Leveraged metabolic pathway gene network topology to identify subpathways.
  • Refined subpathways based on activity dysregulation, prognostic efficacy, and classification performance.
  • Utilized CRISPR knockout screening data and multi-omics data (transcriptome, proteome) for validation.

Main Results:

  • Identified 12 risk metabolic subpathways across six cancer types, forming a core metabolic module.
  • Validated the essential roles of ADH5, ALDH1B1, and ALDH7A1 in breast cancer.
  • Observed down-regulation of the core metabolic module in various cancer tissues and cells.
  • Linked core metabolic module activity to immune cell infiltration, particularly T cells, and identified abnormalities in CD8 T cell subtypes.

Conclusions:

  • Established a robust computational method for identifying cancer-related metabolic subpathways.
  • The identified core metabolic module and its genes serve as potential biomarkers for cancer.
  • This approach aids in discovering more precise biomarkers for cancer patient stratification and treatment.