Integrative multi-omics profiling and machine learning reveal enhancer RNA signatures for early detection and prognostic stratification in breast cancer.

Junhuan Xia, Jingwen Tian, Yuting Cao, Suhan Zhang, Weiye Qian, Yanan Qi, Hao Huang

Abstract read

In one paragraph

Article in NAR cancer, 2026. 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

7 authors.

Junhuan XiaState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, No. 2 SEU Road, Nanjing 211189, Jiangsu, China.ORCID https://orcid.org/0009-0009-8876-0154

Jingwen TianState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, No. 2 SEU Road, Nanjing 211189, Jiangsu, China.

Yuting CaoState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, No. 2 SEU Road, Nanjing 211189, Jiangsu, China.ORCID https://orcid.org/0009-0003-3607-7456

Suhan ZhangState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, No. 2 SEU Road, Nanjing 211189, Jiangsu, China.

Weiye QianState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, No. 2 SEU Road, Nanjing 211189, Jiangsu, China.

Yanan QiState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, No. 2 SEU Road, Nanjing 211189, Jiangsu, China.

Hao HuangState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, No. 2 SEU Road, Nanjing 211189, Jiangsu, China.ORCID https://orcid.org/0000-0002-5570-6145

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current breast cancer biomarkers rely predominantly on protein-coding transcriptomes, leaving the regulatory information encoded by enhancer RNAs (eRNAs) largely unexploited. Here, we profiled eRNA expression across 1073 The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) samples integrated with The Cancer eRNA Atlas annotations, using GTEx healthy breast tissue (

Indexed as

Biomarkers, TumorBreast NeoplasmsEnhancer RNAsMachine LearningEarly Detection of CancerFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMultiomicsPrognosisTranscriptomeBiomarkers, TumorEnhancer RNAs

Identifiers

PMID42798840

DOI10.1093/narcan/zcag026

PMCPMC13613065

What OpenQuestion holds

Textmetadata

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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.