Evidence map›Paper›PMID 41363115›Full record

ArticleBiology open2025

Methylation-based signature to distinguish indolent and aggressive prostate cancer.

Muheng Liao, Jace Webster, Amy Ly, Emily Rozycki, Christopher A Maher

Abstract read
In one paragraph

Article in Biology open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Muheng LiaoDepartment of Internal Medicine, Washington University School of Medicine, St Louis, MO 63110, USA.
Jace WebsterDepartment of Internal Medicine, Washington University School of Medicine, St Louis, MO 63110, USA.
Amy LyDepartment of Internal Medicine, Washington University School of Medicine, St Louis, MO 63110, USA.
Emily RozyckiDepartment of Internal Medicine, Washington University School of Medicine, St Louis, MO 63110, USA.
Christopher A MaherDepartment of Internal Medicine, Washington University School of Medicine, St Louis, MO 63110, USA.ORCID 0009-0005-1728-4614

Funding

PCRP Prostate Cancer Biorepository Network W81XWH-18-2-0019Washington University in St Louis School of Medicine
6 · The paper itself

Abstract

Prostate cancer management faces significant challenges in distinguishing indolent from aggressive disease, particularly since most patients are intermediate-risk and therefore hinders the ability to recommend standardized treatment recommendations. Moreover, current prognostic tools including Gleason scoring and tumor staging demonstrate limited accuracy for predicting disease progression and tumor recurrence. DNA methylation serves as a stable epigenetic modification that directly regulates gene expression, making it an ideal biomarker for cancer prognosis. Therefore, this study leveraged whole-genome enzymatic methylation sequencing on 120 patients to develop a novel prognostic signature for aggressive prostate cancer progression. We analyzed 20,849 differentially methylated regions (DMRs) and employed multiple machine learning approaches to identify optimal biomarkers. This revealed a 14-region DNA methylation signature that can serve as independent prognostic prediction factors outperforming traditional clinical indices. Further, when combined into a risk score it achieved a clinically meaningful odds ratio. This methylation-based approach provides actionable information for treatment decisions and surveillance strategies, representing a significant advancement toward precision medicine in prostate cancer management through biologically informed risk stratification.

Indexed as

Biomarkers, TumorDNA MethylationProstatic NeoplasmsComputational BiologyDisease ProgressionEpigenesis, GeneticGene Expression Regulation, NeoplasticHumansMachine LearningMaleNeoplasm GradingPrognosisBiomarkers, TumorClinical prognosisDNA methylationEpigenomeMachine learningProstate cancer

Identifiers

PMID41363115
PMCPMC12772133

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Registered trials

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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.