Evidence map›Paper›PMID 41578129›Full record

ArticleNPJ breast cancer2026

Spatial gene expression analysis reveals drivers of extremely early lymph node metastasis in breast cancer.

Satoi Nagasawa, Keiko Kajiya, Erina Ishikawa, Akinori Kanai, Ayako Suzuki, Ai Motoyoshi, Tsuguo Iwatani, Manabu Kubota, Masaru Nakamura, Tatsuya Onishi and 8 more

Abstract read
In one paragraph

Article in NPJ breast 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

18 authors.

Satoi Nagasawa *Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan. s3nagasawa@edu.k.u-tokyo.ac.jp.
Keiko Kajiya *Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
Erina IshikawaDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
Akinori KanaiDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
Ayako SuzukiDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
Ai MotoyoshiDivision of Breast and Endocrine Surgery, Department of Surgery, St. Marianna University School of Medicine, Kawasaki, Japan.
Tsuguo IwataniDivision of Breast and Endocrine Surgery, Department of Surgery, St. Marianna University School of Medicine, Kawasaki, Japan.
Manabu KubotaDepartment of Pathology, St. Marianna University School of Medicine, Kawasaki, Japan.
Masaru NakamuraDepartment of Pathology, St. Marianna University School of Medicine, Kawasaki, Japan.
Tatsuya OnishiDepartment of Breast Surgery, National Cancer Center Hospital East, Chiba, Japan.
Akiyoshi HoshinoDepartment of Diagnostic Pathology, Kitasato University Kitasato Institute Hospital, Minato-ku, Tokyo, Japan.
Ichiro MaedaDepartment of Diagnostic Pathology, Kitasato University Kitasato Institute Hospital, Minato-ku, Tokyo, Japan.
Akihiko MorozumiDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
Kenji TakatsukaInvestment Planning Department, Corporate Strategy, Nikon Corporation, Shinagawa-ku, Tokyo, Japan.
Junki KoikeDepartment of Pathology, St. Marianna University School of Medicine, Kawasaki, Japan.
Masahide SekiDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
Koichiro TsugawaDivision of Breast and Endocrine Surgery, Department of Surgery, St. Marianna University School of Medicine, Kawasaki, Japan.
Yutaka SuzukiDepartment of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan. ysuzuki@edu.k.u-tokyo.ac.jp.

Funding

the Japan Agency for Medical Research and Development (AMED) P-PROMOTE JP21ck0106700the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (KAKENHI Grants) JP24K11738
6 · The paper itself

Abstract

Lymph node metastasis correlates with breast cancer prognosis; however, the cellular mechanisms underlying the earliest metastatic events remain unclear. In spatial transcriptomic analysis of a patient with breast cancer at single-cell resolution, we identified 30 tumor cells representing the initial metastatic seeding in a lymph node. These cells originated from multiple epithelial-mesenchymal (EM) transition status and included six distinct subpopulations with biological significance. Only cells exhibiting a metabolic shift toward fatty acid metabolism successfully established lymph node colonies, implicating this shift in metastatic fitness. The tumor microenvironment surrounding these cells showed immunosuppressive and tumor-promoting features, supporting metastasis establishment. Cross-referencing these expression profiles with public datasets revealed that poor prognosis correlated not with fully mesenchymal or metastatic populations, but with hybrid EM cells exhibiting epithelial and mesenchymal traits. These findings highlight the metabolic and phenotypic plasticity of metastatic cells and serve as translational bridges between the spatial evolution of tumor cells in the extremely early stages of lymph node metastasis and clinical prognosis in breast cancer.

Identifiers

PMID41578129
PMCPMC12932634

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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 Socratic graph.