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Identifying metabolism-related genes in liver cancer through weighted gene co-expression network analysis and machine learning

  • 0Faculty of Medicine, Macau University of Science and Technology, Taipa, China.
Frontiers in Genetics +

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October 10, 2025

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October 10, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study identified six metabolism-related genes (ACADS, ALDH8A1, COX4I2, CYP2C8, DBH, NDST3) as potential biomarkers for liver cancer prognosis. These genes may also serve as therapeutic targets for liver cancer treatment.

Area Of Science

  • Oncology
  • Metabolomics
  • Bioinformatics

Background

  • Liver cancer is a major cause of cancer mortality, often linked to metabolic dysregulation.
  • Identifying reliable prognostic biomarkers and therapeutic targets is crucial for improving patient outcomes.

Purpose Of The Study

  • To identify metabolism-related genes associated with liver cancer prognosis.
  • To discover potential therapeutic targets for liver cancer based on these biomarkers.

Main Methods

  • Transcriptomic data analysis using EdgeR and Weighted Gene Co-expression Network Analysis (WGCNA).
  • Machine learning algorithms (Random Forest, Support Vector Machine, LASSO) for marker gene selection.
  • Gene Set Enrichment Analysis (GSEA), single-sample GSEA (ssGSEA), and RT-PCR for validation and pathway analysis.
  • Drug discovery using the DGIdb database.

Main Results

  • 234 metabolism-related genes were identified; seven marker genes were selected using machine learning.
  • Six genes (ACADS, ALDH8A1, COX4I2, CYP2C8, DBH, NDST3) correlated with liver cancer patient survival and immune cell infiltration.
  • Gene expression patterns were validated in independent datasets (GSE54236) and clinical samples.
  • Candidate drugs targeting these biomarkers were identified, including PAZOPANIB and ETOPOSIDE.

Conclusions

  • Metabolism-related genes ACADS, ALDH8A1, COX4I2, CYP2C8, DBH, and NDST3 show significant potential as prognostic biomarkers for liver cancer.
  • These identified genes represent promising therapeutic targets for future liver cancer treatments.

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