Evidence map›Paper›PMID 38434763›Full record

ArticleMedComm2024

Unraveling the role of M1 macrophage and CXCL9 in predicting immune checkpoint inhibitor efficacy through multicohort analysis and single-cell RNA sequencing.

Yunfang Yu, Haizhu Chen, Wenhao Ouyang, Jin Zeng, Hong Huang, Luhui Mao, Xueyuan Jia, Taihua Guan, Zehua Wang, Ruichong Lin and 4 more

Open access · goldAbstract read
In one paragraph

Article in MedComm, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
6.0field-weighted citation impact, top 3% 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

25 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
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  8. Bacterial Outer Membrane Vesicles in Potentiating Cancer Vaccines: Progress and Prospects.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  9. Review
  10. Article
  11. Review
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  15. Article
  16. Review
  17. Article
  18. Article
  19. Claudin-9 (Cancer pathogenesis and therapy · 2025
    Article
  20. Article
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

14 authors at 8 institutions in 4 countries.

Yunfang YuFaculty of Medicine Macau University of Science and Technology Macao P. R. China.
Haizhu ChenGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Guangdong-Hong Kong Joint Laboratory for RNA Medicine Department of Medical Oncology Breast Tumor Centre Phase I Clinical Trial Centre Sun Yat-sen Memorial Hospital Sun Yat-sen University Guangzhou P. R. China.
Wenhao OuyangGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Guangdong-Hong Kong Joint Laboratory for RNA Medicine Department of Medical Oncology Breast Tumor Centre Phase I Clinical Trial Centre Sun Yat-sen Memorial Hospital Sun Yat-sen University Guangzhou P. R. China.
Jin ZengFaculty of Sustainable Development Macau University of Science and Technology Macau P. R. China.
Hong HuangSchool of Medicine Guilin Medical University Guilin P. R. China.
Luhui MaoGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Guangdong-Hong Kong Joint Laboratory for RNA Medicine Department of Medical Oncology Breast Tumor Centre Phase I Clinical Trial Centre Sun Yat-sen Memorial Hospital Sun Yat-sen University Guangzhou P. R. China.
Xueyuan JiaFaculty of Medicine Macau University of Science and Technology Macao P. R. China.
Taihua GuanGuangzhou National Laboratory Guangzhou P. R. China.
Zehua WangDivision of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai P. R. China.
Ruichong LinFaculty of Innovation Engineering Macau University of Science and Technology Macao P. R. China.
Zhenjun HuangGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Guangdong-Hong Kong Joint Laboratory for RNA Medicine Department of Medical Oncology Breast Tumor Centre Phase I Clinical Trial Centre Sun Yat-sen Memorial Hospital Sun Yat-sen University Guangzhou P. R. China.
Hanqi YinSouth China Institute of Biomedine Guangzhou China.
Herui YaoGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Guangdong-Hong Kong Joint Laboratory for RNA Medicine Department of Medical Oncology Breast Tumor Centre Phase I Clinical Trial Centre Sun Yat-sen Memorial Hospital Sun Yat-sen University Guangzhou P. R. China.
Kang ZhangFaculty of Medicine Macau University of Science and Technology Macao P. R. China.
Guangzhou Experimental Station · CNSun Yat-sen Memorial Hospital · CNMacau University of Science and Technology · MOSun Yat-sen University · CNGuangzhou Institutes of Biomedicine and Health · CNGuilin Medical University · CNHong Kong Baptist University · HKZen-Noh (Japan) · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The exact function of M1 macrophages and CXCL9 in forecasting the effectiveness of immune checkpoint inhibitors (ICIs) is still not thoroughly investigated. We investigated the potential of M1 macrophage and C-X-C Motif Chemokine Ligand 9 (CXCL9) as predictive markers for ICI efficacy, employing a comprehensive approach integrating multicohort analysis and single-cell RNA sequencing. A significant correlation between high M1 macrophage and improved overall survival (OS) and objective response rate (ORR) was found. M1 macrophage expression was most pronounced in the immune-inflamed phenotype, aligning with increased expression of immune checkpoints. Furthermore, CXCL9 was identified as a key marker gene that positively correlated with M1 macrophage and response to ICIs, while also exhibiting associations with immune-related pathways and immune cell infiltration. Additionally, through exploring RNA epigenetic modifications, we identified Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G (APOBEC3G) as linked to ICI response, with high expression correlating with improved OS and immune-related pathways. Moreover, a novel model based on M1 macrophage, CXCL9, and APOBEC3G-related genes was developed using multi-level attention graph neural network, which showed promising predictive ability for ORR. This study illuminates the pivotal contributions of M1 macrophages and CXCL9 in shaping an immune-active microenvironment, correlating with enhanced ICI efficacy. The combination of M1 macrophage, CXCL9, and APOBEC3G provides a novel model for predicting clinical outcomes of ICI therapy, facilitating personalized immunotherapy.

Indexed as

Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G (APOBEC3G)C‐X‐C Motif Chemokine Ligand 9 (CXCL9)immune checkpoint inhibitorsM1 macrophagemulti‐level attention graph neural networktumor immune microenvironment

Identifiers

PMID38434763
PMCPMC10906808
OpenAlexW4392387831

What OpenQuestion holds

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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.