Evidence map›Paper›PMID 41163070›Full record

ArticleJournal of hematology & oncology2025

PSMA-targeted CAR-macrophages drive glycolytic reprogramming for enhanced prostate cancer immunotherapy.

Yangli Xu, Duoli Xie, Chunhao Cao, Zhuqian Wang, Yue Ju, Lili Guan, Xuelong Li, Shanshan Wu, Luo Zhang, Chao Liang and 1 more

Abstract readLetter
In one paragraph

Article in Journal of hematology & oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Leveraging Macrophage Metabolic Reprogramming for Enhanced Anti-Tumor Immunity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  6. CAR-macrophages: a new chapter in cancer immunotherapy.Acta biochimica et biophysica Sinica · 2026
    Article
  7. Review
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

11 authors.

Yangli Xu *Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, 518055, China.
Duoli Xie *Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, 518055, China.
Chunhao Cao *Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, 518055, China.
Zhuqian Wang *Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, 518055, China.
Yue JuRocRock Biotechnology Co., Ltd, Suzhou, 215000, China.
Lili GuanApplied Biology Laboratory, College of Environmental and Safety Engineering, Shenyang University of Chemical Technology, Shenyang, 110142, China.
Xuelong LiApplied Biology Laboratory, College of Environmental and Safety Engineering, Shenyang University of Chemical Technology, Shenyang, 110142, China.
Shanshan WuApplied Biology Laboratory, College of Environmental and Safety Engineering, Shenyang University of Chemical Technology, Shenyang, 110142, China.
Luo ZhangDepartment of Computational and Synthetic Biology, the Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, 100853, China. marbleluo@126.com.
Chao LiangDepartment of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, 518055, China. liangc@sustech.edu.cn.
Xiushan YinRocRock Biotechnology Co., Ltd, Suzhou, 215000, China. xiushanyin@me.com.

Funding

Department of Education of Guangdong Province 2021KTSCX104Ganzhou City Technology Innovation Center 2023ZXGX7956Guangdong Basic and Applied Basic Research Foundation 2022A1515012164National Center for Biotechnology Innovation in Cellular Therapy 2023XB02008National Key Research and Development Program of China 2024YFC3506200National Natural Science Foundation Council of China 32371490National Natural Science Foundation Council of China 82472394Science, Technology and Innovation Commission of Shenzhen Municipality SGDX20240115112400001
6 · The paper itself

Abstract

Although chimeric antigen receptor (CAR)-T cells have demonstrated remarkable efficacy against hematologic malignancies, their effectiveness in solid tumors is limited by poor tumor infiltration and severe cytokine release syndrome (CRS). CAR-macrophage (CAR-M) therapy has emerged as a promising alternative, leveraging the innate tumor-homing capacity of macrophages while enabling antigen-specific phagocytosis and immune activation without triggering CRS. Prostate-specific membrane antigen (PSMA) represents an ideal therapeutic target due to its high expression in prostate cancer cells. In this study, we engineered PSMA-specific CAR-M with potent anti-tumor activity against prostate cancer cells both in vitro and in vivo. PSMA-specific CAR-M exhibited strong antigen-dependent phagocytic capability and underwent polarization toward a pro-inflammatory, tumoricidal phenotype upon PSMA recognition. Mechanistically, interaction with PSMA-expressing prostate cancer cells induced metabolic reprogramming, characterized by enhanced glycolytic activity and suppressed oxidative phosphorylation, which reinforced the anti-tumor function of CAR-M. Our findings highlight PSMA-targeted CAR-M therapy as a promising immunotherapeutic approach for prostate cancer.

Indexed as

Antigens, SurfaceGlutamate Carboxypeptidase IIGlycolysisImmunotherapyImmunotherapy, AdoptiveMacrophagesProstatic NeoplasmsReceptors, Chimeric AntigenAnimalsCell Line, TumorHumansMaleMiceAntigens, SurfaceFOLH1 protein, humanGlutamate Carboxypeptidase IIReceptors, Chimeric AntigenCAR-MImmunotherapyMetabolic reprogramming.Prostate cancerPSMA

Identifiers

PMID41163070
PMCPMC12574140

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.