Evidence map›Paper›PMID 40340848›Full record

ArticleJournal of translational medicine2025

Multi-dimensional characterization of cellular states reveals clinically relevant immunological subtypes and therapeutic vulnerabilities in ovarian cancer.

Can Zhang, Si Li, Jiyu Guo, Tao Pan, Ya Zhang, Yueying Gao, Jiwei Pan, Meng Liu, Qingyi Yang, Jinyang Yu and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

13 authors.

Can Zhang *College of Biomedical Information and Engineering, Hainan Medical University, Haikou, 571199, China.
Si Li *School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150081, China.
Jiyu Guo *School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150081, China.
Tao PanCollege of Biomedical Information and Engineering, Hainan Medical University, Haikou, 571199, China.
Ya ZhangCollege of Biomedical Information and Engineering, Hainan Medical University, Haikou, 571199, China.
Yueying GaoSchool of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150081, China.
Jiwei PanCollege of Biomedical Information and Engineering, Hainan Medical University, Haikou, 571199, China.
Meng LiuCollege of Biomedical Information and Engineering, Hainan Medical University, Haikou, 571199, China.
Qingyi YangSchool of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150081, China.
Jinyang YuCollege of Biomedical Information and Engineering, Hainan Medical University, Haikou, 571199, China.
Juan XuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang Province, China. xujuanbiocc@ems.hrbmu.edu.cn.
Yongsheng LiSchool of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, 150081, China. liyongsheng@ems.hrbmu.edu.cn.ORCID 0000-0003-1914-0727
Xia LiCollege of Biomedical Information and Engineering, Hainan Medical University, Haikou, 571199, China. lixia@hrbmu.edu.cn.

Funding

National Natural Science Foundation of China 32060152National Natural Science Foundation of China 32170676National Natural Science Foundation of China 32322020Natural Science Foundation of Hainan Province 823QN249Natural Science Foundation of Heilongjiang Province (Key Program) ZD2023C007
6 · The paper itself

Abstract

backgroundDiverse cell types and cellular states in the tumor microenvironment (TME) are drivers of biological and therapeutic heterogeneity in ovarian cancer (OV). Characterization of the diverse malignant and immunology cellular states that make up the TME and their associations with clinical outcomes are critical for cancer therapy. However, we are still lack of knowledge about the cellular states and their clinical relevance in OV.

methodsWe manually collected the comprehensive transcriptomes of OV samples and characterized the cellular states and ecotypes based on a machine-learning framework. The robustness of the cellular states was validated in independent cohorts and single-cell transcriptomes. The functions and regulators of cellular states were investigated. Meanwhile, we thoroughly examined the associations between cellular states and various clinical factors, including clinical prognosis and drug responses.

resultsWe depicted and characterized an immunophenotypic landscape of 3,099 OV samples and 80,044 cells based on a machine learning framework. We identified and validated 32 distinct transcriptionally defined cellular states from 12 cell types and three cellular communities or ecotypes, extending the current immunological subtypes in OV. Functional enrichment and upstream transcriptional regulator analyses revealed cancer hallmark-related pathways and potential immunological biomarkers. We further investigated the spatial patterns of identified cellular states by integrating the spatially resolved transcriptomes. Moreover, prognostic landscape and drug sensitivity analysis exhibited clinically relevant immunological subtypes and therapeutic vulnerabilities.

conclusionOur comprehensive analysis of TME helps leveraging various immunological subtypes to highlight new directions and targets for the treatment of cancer.

Indexed as

Ovarian NeoplasmsFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansImmunophenotypingMachine LearningPrognosisReproducibility of ResultsTranscriptomeTumor MicroenvironmentCellular StatesClinical prognosisDrug responseImmunotherapyTumor microenvironment

Identifiers

PMID40340848
PMCPMC12063340

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Registered trials

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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.