Evidence map›Paper›PMID 42194997›Full record

ArticleGenes2026

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

Catarina Gameiro Carvalho, Alexandra M Carvalho, Susana Vinga

Abstract read
In one paragraph

Article in Genes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Catarina Gameiro CarvalhoInstituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal.
Alexandra M CarvalhoInstituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal.ORCID 0000-0001-6607-7711
Susana VingaInstituto de Engenharia de Sistemas e Computadores-Investigação e Desenvolvimento (INESC-ID), Instituto Superior Técnico, Universidade de Lisboa, 1000-029 Lisbon, Portugal.ORCID 0000-0002-1954-5487

Funding

Fundação para a Ciência e Tecnologia 2023.17447.ICDTFundação para a Ciência e Tecnologia LISBOA2030-FEDER-0086820Fundação para a Ciência e Tecnologia UID/50021/2025Fundação para a Ciência e Tecnologia UIDB/50008/2020Fundação para a Ciência e Tecnologia UIDB/50022/2020Fundação para a Ciência e Tecnologia UID/PRR/50021/2025
6 · The paper itself

Abstract

backgroundGliomas are the most common malignant brain tumors in adults, characterized by a poor prognosis. Although the current World Health Organization (WHO) classification provides clear guidelines for classifying oligodendroglioma, astrocytoma, and glioblastoma patients, significant heterogeneity persists within each class, limiting the effectiveness of current treatment strategies. With the increasing availability of large-scale multi-omics datasets resulting from advancements in sequencing technologies and online repositories that provide them, such as The Cancer Genome Atlas (TCGA), it is now possible to investigate these tumors at multiple molecular levels.

methodsIn this work, we apply integrative multi-omics analysis to explore the interplay between genomic (mutations), epigenomic (DNA methylation), and transcriptomic (mRNA and miRNA) layers. Our approach relies on Multi-Omics Factor Analysis (MOFA), a Bayesian latent factor analysis model designed to capture sources of variation across different omics types.

resultsOur results highlight distinct molecular profiles across the three glioma types and identify potential relationships between methylation and genetic expression. In particular, we uncover novel candidate biomarkers associated with survival as well as a transcriptional profile associated with neural system development.

conclusionsThese findings may contribute to more personalized therapeutic strategies, potentially improving treatment effectiveness and survival outcomes in this disease.

Indexed as

Brain NeoplasmsGliomaBayes TheoremBiomarkers, TumorDNA MethylationFactor Analysis, StatisticalGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMicroRNAsMultiomicsPrognosisTranscriptomeBiomarkers, TumorMicroRNAslatent factor modelmolecular subtypingmulti-omics integrationprognostic biomarkerssurvival analysis

Identifiers

PMID42194997
PMCPMC13205169

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.