Dual Immunological Prognostic Models for Risk Stratification and Treatment Insights in Triple-Negative Breast Cancer.

Shihua Lin, Hongjiu Wang, Zhenzhen Wang, Yuxuan Xiao, Menoudji Djetoyom Patrice, Li Wang, Xia Li, Yunpeng Zhang

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In one paragraph

Article in International journal of molecular sciences, 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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Authors and funding

8 authors.

Shihua LinCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.

Hongjiu WangCollege of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, China.

Zhenzhen WangCollege of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, China.

Yuxuan XiaoCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.

Menoudji Djetoyom PatriceCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.ORCID 0009-0002-8195-1993

Li WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.

Xia LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.

Yunpeng ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.ORCID 0000-0002-3709-3656

Funding

Key Research and Development Program of Heilongjiang Province 2024ZX12C27National Natural Science Foundation of China 62472131, 62372144, 62573169 62172131, U23A20166, 32570792National Science and Technology Major Program 2024ZD0530500Outstanding Youth Foundation of Heilongjiang Province of China YQ2023F004

6 · The paper itself

Abstract

Triple-negative breast cancer (TNBC) represents the most aggressive breast cancer subtype, with its highly heterogeneous tumor microenvironment posing substantial challenges for precision diagnosis and therapy. To address this, we aim to construct a novel prognostic framework based on tumor-immune interactions. Through integrative analysis of single-cell RNA sequencing data from 30 TNBC samples (106,132 cells), we identify key tumor expression metaprograms and uncover their interaction with an immunosuppressive dendritic-cell subset, a process associated with the NECTIN1-NECTIN4 axis. Leveraging these interactions, we developed and validated two immunological prognostic models using multi-cohort transcriptomic data, including the stress response tumor cell and pDC_CLEC4C prognostic model (SPSM) and the immune response tumor cell and pDC_CLEC4C prognostic model (IPSM). These models effectively stratified TNBC patients into distinct risk groups, with the low-risk group characterized by an immunologically active microenvironment and elevated expression of immune checkpoint genes, suggesting a potential responsiveness to immunotherapy. Furthermore, we identified several potential therapeutic agents, including imatinib and bortezomib. Collectively, our dual-model framework provides a tool for risk stratification, offers translational insights for precision treatment, and presents new directions for understanding TNBC heterogeneity and therapeutic development.

Indexed as

Models, ImmunologicalTriple Negative Breast NeoplasmsBiomarkers, TumorFemaleGene Expression Regulation, NeoplasticHumansNectinsPrognosisTumor MicroenvironmentBiomarkers, TumorNectinsdrug predictionprognostic modelsingle-cellTNBCtumor immune microenvironment

Identifiers

PMID41683914

DOI10.3390/ijms27031494

PMCPMC12898472

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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.