Evidence map›Paper›PMID 36725186›Full record

ArticleJournal of advanced research2023

Integrated immunogenomic analysis of single-cell and bulk tissue transcriptome profiling unravels a macrophage activation paradigm associated with immunologically and clinically distinct behaviors in ovarian cancer.

Congcong Yan, Ke Li, Fanling Meng, Lu Chen, Jingting Zhao, Zicheng Zhang, Dandan Xu, Jie Sun, Meng Zhou

Open access · goldAbstract read
In one paragraph

Article in Journal of advanced research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
2.6field-weighted citation impact, top 9% of its field
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

30 citing papers in PubMed, 36 citations in OpenAlex.

  1. Identification of Epigenetic Regulator-Associated Genes in Keloid Disease Through Integrated Bulk and Single-Cell Transcriptomics With RT-qPCR Validation.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
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  13. Single-cell RNA sequencing and cell-cell communication analysis reveal tumor microenvironment associated with chemotherapy responsiveness in ovarian cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025
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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

9 authors at 3 institutions in 1 country.

Congcong YanSchool of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, P. R. China.
Ke LiSchool of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, P. R. China.
Fanling MengDepartment of Gynecology, Harbin Medical University Cancer Hospital, Harbin 150081, P. R. China.
Lu ChenSchool of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, P. R. China.
Jingting ZhaoSchool of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, P. R. China.
Zicheng ZhangSchool of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, P. R. China.
Dandan XuSchool of Basic Medical Sciences, Harbin Medical University, Harbin 150081, P. R. China. Electronic address: dandanxu@163.com.
Jie SunSchool of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, P. R. China. Electronic address: suncarajie@wmu.edu.cn.
Meng ZhouSchool of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325027, P. R. China. Electronic address: zhoumeng@wmu.edu.cn.
Wenzhou Medical University · CNHarbin Medical University · CNThird Affiliated Hospital of Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionIncreasing evidence demonstrates that the activation states and diverse spectrum of macrophage subtypes display dynamic heterogeneity in the tumor microenvironment, which plays a critical role in a variety of cancer types.

objectivesTo investigate the heterogeneity and the homeostasis of different macrophage subtypes, as well as their effect on biological and clinical manifestations of ovarian cancer (OV).

methodIntegrated immunogenomic analysis of single-cell and bulk tissuetranscriptome profiling was performed to systematically investigate the association between macrophage activation and prognostic and therapeutic efficacy. Consensus clustering analysis was used to define novel macrophage subtypes. An artificial neural network was used to simulate the dynamic activation of macrophages.

resultsThe pan-cohort results suggested that high relative infiltration abundance of M0 and M1 macrophages was associated with improved outcome and therapeutic efficacy. However, it was the opposite for M2 macrophages. Unsupervised consensus clustering analysis revealed two OV subgroups characterized by a balance between M0, M1 and M2 macrophages with distinct clinical and immunological behaviors. Finally, a macrophage polarization-derived artificial neural network model was proposed to serve as a robust prognostic factor and predictive biomarker for therapeutic efficacy, which was validated in different independent patient cohorts.

conclusionThe present study provides a new understanding of macrophage heterogeneity and its association with OV prognosis and underlines the future clinical potential of a macrophage activation model for tumor prevention and treatment.

Indexed as

Macrophage ActivationOvarian NeoplasmsFemaleGene Expression ProfilingHumansMacrophagesTranscriptomeTumor MicroenvironmentArtificial neural networkImmune microenvironmentMacrophagesSingle-cell

Identifiers

PMID36725186
PMCPMC9936412
OpenAlexW4224057206

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LicenceCC BY
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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.