Evidence map›Paper›PMID 40594370›Full record

ArticleScientific reports2025

Systematic screening of metabolic pathways to identify two breast cancer subtypes with divergent immune characteristics.

Xiangshu Cheng, Shuhao Zhang, Xin Meng, Rui Chen, Hao Tang, Jiacheng Wang, Yongshuai Jiang, Ruijie Zhang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

Who cites it

2 citing papers in PubMed.

  1. Review
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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

8 authors.

Xiangshu ChengCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongiiang Province, China.
Shuhao ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongiiang Province, China.
Xin MengCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongiiang Province, China.
Rui ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongiiang Province, China.
Hao TangCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongiiang Province, China.
Jiacheng WangCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongiiang Province, China.
Yongshuai JiangCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongiiang Province, China. jiangyongshuai@gmail.com.
Ruijie ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, 157 Baojian Road, Nangang District, Harbin, Heilongiiang Province, China. zhangrj@ems.hrbmu.edu.cn.

Funding

the Natural Science Foundation of Heilongjiang Province, China No. LH2019C043
6 · The paper itself

Abstract

Due to the high heterogeneity among breast cancer (BRCA) patients, most individuals show a limited response rate to one specific treatment. The metabolic plasticity of BRCA cells is one of the main causes of their heterogeneity, affecting not only their own growth and function but also their metabolites have an impact on the tumor immune microenvironment (TIME). However systematic evaluation of metabolic pathways in BRCA is lacking. We identified BRCA metabolic subtypes (BCMS) by consensus clustering 26 KEGG/Reactome pathways in the TCGA BRCA discovery cohort (n = 1094). Nine independent bulk transcriptome cohorts (total n > 4000), including METABRIC and GEO datasets, were used for validation via random forest classification. To characterize BCMS, we applied an analytical framework encompassing functional enrichment (GSEA), immune infiltration (Mcpcounter), clinical correlation, drug sensitivity (oncoPredict) on bulk transcriptome data, cell-cell communication analysis (CellChat) on single-cell RNA sequencing (scRNA-seq) data, and spatial co-localization analysis (CellTrek) on spatial RNA sequencing (spRNA-seq) data. We identified two distinct BCMS. BCMS-I exhibited upregulated lipid metabolism-related pathways, characterized by immune activation, a better prognosis, and higher infiltration of immune cells, including B cells, T cells, NK cells, macrophages, and neutrophils. Spatial co-localization analysis further revealed that BCMS-I demonstrated spatial co-localization with immune cells. In contrast, BCMS-II showed upregulation of amino acid and vitamin metabolism-related pathways, with tumor cell proliferation, a poorer prognosis, and a lack of immune cell infiltration. The immune activation in BCMS-I is marked by the significant activation of the MHC-I signaling pathway in interactions between tumor cells and T/NK cells, and of the MHC-II signaling pathway in interactions between tumor cells and dendritic cells/macrophages. In contrast, the proliferative characteristics of BCMS-II are associated with the co-activation of the GRN signaling pathway by myeloid immune cells and stromal cells within the tumor microenvironment. Drug sensitivity analysis revealed that BCMS-II was highly sensitive to Ganitumab, Carboplatin + ABT-888, and Pembrolizumab. This study established a novel Breast Cancer Metabolic Subtyping System (BCMSS) based on metabolic pathway analysis. Our findings highlight the heterogeneity of BRCA in terms of metabolic features, immune characteristics, clinical prognosis, and drug sensitivity. The novel classification system provides valuable insights for clinical diagnosis and treatment, serving as a foundation for precision diagnosis and personalized therapies in BRCA.

Indexed as

Breast NeoplasmsMetabolic Networks and PathwaysFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansTranscriptomeTumor MicroenvironmentBreast cancerMetabolic plasticityMetabolic subtypesTumor immune microenvironment

Identifiers

PMID40594370
PMCPMC12216186

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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.