Evidence map›Paper›PMID 40665339›Full record

ArticleBreast cancer research : BCR2025

Multi-transcriptomics predicts clinical outcome in systemically untreated breast cancer patients with extensive follow-up.

Thi T N Do, Ines Block, Mark Burton, Kristina P Sørensen, Martin J Larsen, Anne Marie Bak Jylling, Bent Ejlertsen, Anne-Vibeke Lænkholm, Qihua Tan, Torben A Kruse and 1 more

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Article in Breast cancer research : BCR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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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

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Thi T N DoDepartment of Clinical Genetics, Odense University Hospital, Odense, Denmark.
Ines BlockDepartment of Clinical Genetics, Odense University Hospital, Odense, Denmark.
Mark BurtonDepartment of Clinical Genetics, Odense University Hospital, Odense, Denmark.
Kristina P SørensenDepartment of Clinical Genetics, Odense University Hospital, Odense, Denmark.
Martin J LarsenDepartment of Clinical Genetics, Odense University Hospital, Odense, Denmark.
Anne Marie Bak JyllingDepartment of Pathology, Odense University Hospital. Institute of Clinical Research, University of Southern Denmark, Odense, Denmark.
Bent EjlertsenDanish Breast Cancer Cooperative Group & Department of Clinical Oncology Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
Anne-Vibeke LænkholmDepartment of Surgical Pathology, Zealand University Hospital, Slagelse, Denmark.
Qihua TanUnit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
Torben A KruseUnit of Human Genetics, Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
Mads ThomassenDepartment of Clinical Genetics, Odense University Hospital, Odense, Denmark. Mads.Thomassen@rsyd.dk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrognostic tools for determining patients with indolent breast cancers (BCs) are far from optimal, leading to extensive overtreatment. Several studies have demonstrated mRNAs, lncRNAs and miRNAs to have prognostic potential in BC. Because mRNAs, lncRNAs, and miRNAs capture distinct transcriptomic information, we hypothesized that combining them would improve classification performance.

methodsOur pair-matched design study included fresh frozen primary tumor samples from 160 lymph node negative and systemically untreated BC patients of which 80 developed recurrence while 80 remained recurrence-free (mean follow-up of 20.9 years). We integrated three classes of RNA and subsequently performed classification using seven machine learning methods followed by a voting scheme.

resultsUnder the criteria of ≥ 90% sensitivity, individual classifications resulted in specificities ranging from 74-91% for the integrated dataset and 56-66%, 58-71% and 69-86% for mRNAs, lncRNAs and miRNAs individually. The specificity level for the multi-transcriptomic dataset was 85% after voting while it was 38%, 48% and 82% for mRNAs, lncRNAs and miRNAs, respectively. In the clinical setting, very high sensitivity may be requested. In the most stringent clinical setting with a sensitivity of 99%, the integrated dataset also outperformed the others with a specificity of 41% compared to 0%, 9% and 28% for mRNAs, lncRNAs and miRNAs, respectively.

conclusionOur results strongly suggest an improvement of prognostic power for classification using an integrated dataset compared to individual classes of RNA and thus encourage researches to opt for an integration of datasets rather than analyzing them separately.

Indexed as

Biomarkers, TumorBreast NeoplasmsGene Expression ProfilingTranscriptomeAdultAgedFemaleFollow-Up StudiesGene Expression Regulation, NeoplasticHumansMicroRNAsMiddle AgedNeoplasm Recurrence, LocalPrognosisRNA, Long NoncodingRNA, MessengerBiomarkers, TumorMicroRNAsRNA, Long NoncodingRNA, MessengerBreast cancerMachine learning methodsMulti-RNA-based classifierMulti-transcriptomicsSystemically untreated patients

Identifiers

PMID40665339
PMCPMC12261547

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.