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Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight

  • 0Cancer Systems Biology, Section for Bioinformatics, Department of Health Technology, Technical University of Denmark, Lyngby, Denmark.
Plos Computational Biology +

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April 21, 2025

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April 21, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

We developed Moonlight2, a tool integrating DNA methylation and gene expression, to identify cancer driver genes. This approach reveals novel epigenetic drivers with prognostic and therapeutic potential in various cancer types.

Area Of Science

  • Genomics and Epigenetics
  • Cancer Biology
  • Systems Biology

Background

  • Cancer arises from genetic and epigenetic alterations in driver genes, crucial for understanding carcinogenesis, prognosis, and therapy.
  • Previous work established the Moonlight framework for predicting driver genes using multi-omics data.
  • Identifying driver genes is vital for advancing cancer research and treatment strategies.

Purpose Of The Study

  • To enhance the Moonlight framework by incorporating DNA methylation data for driver gene prediction.
  • To introduce Gene Methylation Analysis (GMA) for identifying epigenetically deregulated driver genes.
  • To provide epigenetic evidence for driver gene expression profiles in cancer.

Main Methods

  • Developed Moonlight2, integrating a DNA methylation analysis module (Gene Methylation Analysis - GMA).
  • Utilized the EpiMix tool to detect aberrant DNA methylation patterns and link them to gene expression changes.
  • Applied GMA to basal-like breast cancer, lung adenocarcinoma, and thyroid carcinoma datasets.

Main Results

  • Discovered 33, 190, and 263 epigenetically driven genes in basal-like breast cancer, lung adenocarcinoma, and thyroid carcinoma, respectively.
  • Identified a subset of driver genes with significant prognostic value, impacting patient survival.
  • Found that a portion of these driver genes represent potential therapeutic targets.

Conclusions

  • Moonlight2 provides a robust framework for exploring cancer's driving forces by integrating gene expression and methylation data.
  • The study offers novel insights into the epigenetic landscape of three distinct cancer subtypes.
  • The identified driver genes hold potential for improved cancer diagnostics, prognostics, and therapeutics.