Evidence map›Paper›PMID 41945233›Full record

ArticleDiscover oncology2026

Multidimensional single-cell analysis of the molecular characteristics and functional pathways of Regulatory T cells in the microenvironment of HR+ breast cancer.

Peiying Lu, Yiyan Zhai, Xiaodong Chen, Siyu Guo, Jiying Zhou, Meiling Guo, Huiling Lei, Peizhi Ye, Chunguo Wang, Jiarui Wu

Abstract read
In one paragraph

Article in Discover oncology, 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
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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

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

10 authors.

Peiying LuSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China.
Yiyan ZhaiSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China.
Xiaodong ChenSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China.
Siyu GuoAcademy of Chinese Medical Sciences, Henan University of Chinese Medicine, Zhengzhou, China.
Jiying ZhouSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China.
Meiling GuoSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China.
Huiling LeiSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China.
Peizhi YeNational Cancer Center/National Clinical Research Center for Cancer/Chinese Medicine Department of the Caner Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China. xiaokaimen@126.com.
Chunguo WangSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China. chunguowang@bucm.edu.cn.
Jiarui WuSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China. exogamy@163.com.

Funding

Beijing Natural Science Foundation 7242230
6 · The paper itself

Abstract

objectiveThe HR+/HER2- subtype represents the most prevalent form of breast cancer. T-cell heterogeneity and functional status within the breast cancer microenvironment significantly influence tumor progression and the efficacy of immunotherapy. Single-cell RNA sequencing(scRNA-seq) is a powerful tool that enables an in-depth analysis of diverse cell types and their molecular characteristics within tumor tissues.

methodsThis study analyzed scRNA-seq data from HR+/HER2- and ER+ breast cancer samples (GSE228499 and GSE176078) sourced from the GEO database. Initially, the Seurat package was employed for quality control and normalization of the data, followed by dimensionality reduction and clustering. Cell type identification was conducted using the SingleR and Garnett tools, with a focus on the extraction and annotation of T cells and their subsets. Subsequently, FindAllMarkers was utilized to screen for differentially expressed genes, in conjunction with Gene Set Enrichment Analysis (GSEA) pathway analysis using the GSEABase package. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) Enrichment Analysis using the Sangerbox platform. CellChat was employed to analyze intercellular communication, and Monocle was used for pseudo-time analysis to visualize T cell differentiation trajectories.

resultsThis study presents a multilayer analysis of scRNA-seq data derived from 29,540 HR+/HER2- cells and 41,103 ER+ cells, with a specific focus on T cells and Regulatory T (Treg) cells. By employing advanced single-cell sequencing techniques, we elucidate the distinct phenotypic and functional profiles of Treg cells, revealing their pivotal roles in tumor immune evasion and progression.

conclusionThis approach systematically elucidates the heterogeneity and functional characteristics of T cells and Treg cells within the HR+ breast cancer microenvironment, thereby providing a solid data foundation for advancing our understanding of the tumor immune microenvironment. KEYWORDS: Breast cancer, scRNA-seq, Tumor immune microenvironment, Treg

Identifiers

PMID41945233
PMCPMC13199528

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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.