Evidence map›Paper›PMID 42582384›Full record

ArticleFrontiers in immunology2026

Risk stratification and multi-omics reveal RAD51 inhibition as a radiosensitizing strategy in triple-negative breast cancer.

Ran An, Jianming Li, Tianhui Liu, Cunyu Tang, Hong Lu

Abstract read
In one paragraph

Article in Frontiers in immunology, 2026. 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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0citing papers 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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Ran An *Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
Jianming Li *Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
Tianhui LiuDepartment of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
Cunyu TangTianjin Medical University Cancer Institute & Hospital, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
Hong LuDepartment of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Triple-negative breast cancer (TNBC) is a highly aggressive subtype characterized by significant inter- and intra-tumoral heterogeneity. Its varied response to radiotherapy limits clinical outcomes, yet the underlying molecular determinants remain poorly understood. Methods: By integrating single-cell and bulk transcriptomic data, we profiled cell-type-specific radiosensitivity and defined distinct molecular subtypes via consensus clustering. A random survival forest (RSF) prognostic model was then constructed using 34 survival-associated genes and interpreted via SHAP analysis, and its performance was validated in multiple independent external cohorts. RAD51 was prioritized via Dependency Map (DepMap) analysis, and its functional role was validated Results: Single-cell analysis revealed that fibroblasts, macrophages, and proliferative cells harbored elevated radioresistance signatures compared to lymphoid lineages. Bulk profiling identified two distinct subtypes: Cluster A (immunosuppressive, radioresistant, poor prognosis) and Cluster B (immune-active, favorable survival). The RSF model robustly stratified patient risk, with high-risk tumors tightly linked to an immune-desert microenvironment. DepMap analysis nominated RAD51 as a core biological dependency. Importantly, RAD51 knockdown sensitized TNBC cells to irradiation by exacerbating DNA damage, triggering G2/M phase arrest, and driving apoptosis. Consistently, Conclusion: This integrative multi-omics study delineates the cellular and molecular landscape of TNBC radioresistance. The RSF model provides a robust tool for risk stratification, while identifying RAD51 as a potential therapeutic target to restore radiosensitivity and reverse immunosuppression, particularly in high-risk, immune-cold tumors.

Indexed as

Rad51 RecombinaseRadiation ToleranceTriple Negative Breast NeoplasmsAnimalsCell Line, TumorFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMiceMultiomicsPrognosisRisk AssessmentRAD51 protein, humanRad51 Recombinasemachine learningRAD51radioresistancesingle-cell RNA sequencingtriple-negative breast cancer

Identifiers

PMID42582384
PMCPMC13457375

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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.