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Updated: May 28, 2026

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Uncovering Latent Structure in Gliomas Using Multi-Omics Factor Analysis.

Catarina Gameiro Carvalho1, Alexandra M Carvalho2, Susana Vinga3,4

  • 1Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal.

Genes
|May 27, 2026
PubMed
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This study used multi-omics analysis to find molecular differences in glioma brain tumors. The findings reveal new biomarkers and gene expression patterns that could lead to personalized cancer treatments.

Area of Science:

  • Neuro-oncology
  • Genomics
  • Molecular Biology

Background:

  • Gliomas are common adult malignant brain tumors with poor prognosis.
  • Current World Health Organization (WHO) classification has limitations due to tumor heterogeneity.
  • Advancements in sequencing and The Cancer Genome Atlas (TCGA) enable multi-level molecular investigation.

Purpose of the Study:

  • To apply integrative multi-omics analysis to gliomas.
  • To explore the interplay between genomic, epigenomic, and transcriptomic data.
  • To identify molecular profiles and potential therapeutic targets in glioma.

Main Methods:

  • Integrative multi-omics analysis combining genomic, epigenomic (DNA methylation), and transcriptomic (mRNA, miRNA) data.
  • Utilized Multi-Omics Factor Analysis (MOFA), a Bayesian latent factor model.
Keywords:
latent factor modelmolecular subtypingmulti-omics integrationprognostic biomarkerssurvival analysis

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Last Updated: May 28, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

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Published on: September 20, 2024

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis
06:48

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

Published on: May 31, 2020

  • Analyzed data from The Cancer Genome Atlas (TCGA).

    • Main Results:

    • Distinct molecular profiles were identified across oligodendroglioma, astrocytoma, and glioblastoma.
    • Potential relationships between DNA methylation and gene expression were uncovered.
    • Novel candidate biomarkers associated with survival and a transcriptional profile linked to neural development were discovered.

    Conclusions:

    • Findings highlight distinct glioma molecular subtypes.
    • Identified potential biomarkers and gene expression patterns for survival.
    • Results may inform personalized therapeutic strategies for improved treatment and outcomes.