Evidence map›Paper›PMID 41275425›Full record

ArticleClinical and translational medicine2025

Single-cell landscape of the tumour immune microenvironment in human gynaecologic malignancies.

Simin Yin, Sen Li, Mengyan Tu, Junfen Xu

Abstract read
In one paragraph

Article in Clinical and translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Simin YinZhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Sen LiZhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Mengyan TuDepartment of Gynecologic Oncology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Junfen XuZhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.ORCID 0000-0002-2377-0775

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe immune microenvironment of the three most common gynaecological malignancies-tubo-ovarian cancer, endometrial cancer and cervical cancer-has not been systematically studied, limiting clinical application.

methodsThis study analyses 272 389 CD45+ immune cells by integrating publicly available single-cell RNA sequencing (scRNA-seq) data from 111 tumour and non-malignant tissue samples. We identified distinct subsets within immune cells: 11 for monocytes/macrophages, six for CD4 T cells, eight for CD8 T cells and five for B cells, detailing their distribution, prevalence and distinct functions.

resultsA pro-angiogenic macrophage subset linked to NF-κB signalling was associated with worse clinical outcomes and an interferon-primed macrophage subset correlated with improved survival by recruiting T cells through CXCL9/10/11 secretion, as confirmed by multi-colour immunohistochemistry. T cells exhibited dynamic roles in tubo-ovarian cancer, with CD8 Tex cells contributing to immune dysfunction and poor prognosis, while CD8 Trm cells in early-stage tumours supported immune surveillance. Additionally, we identified co-stimulatory and co-inhibitory receptor interactions and classified distinct B cell subsets with varying prognostic implications.

conclusionsThis comprehensive analysis of the tumour immune microenvironment in gynaecological malignancies provides new insights into immune cell composition and function offering potential for optimising immunotherapies and improving clinical outcomes in these cancers.

Indexed as

Genital Neoplasms, FemaleSingle-Cell AnalysisTumor MicroenvironmentFemaleHumanscervical cancerendometrial cancersingle‐cell RNA sequencingtubo‐ovarian cancertumour immune microenvironment

Identifiers

PMID41275425
PMCPMC12640613

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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.