Evidence map›Paper›PMID 42351104›Full record

ReviewJournal of nanobiotechnology2026

Virus-like particles in cancer immunotherapy: bridging human and veterinary medicine through one health.

Yingqi Zhu, Zhuangli Bi, Zichen Zhang, Miao Zhang, Qingqing Du, Mingxing Hu, Ting Zhou, Yiming Fan, Shu Zhang, Guijun Wang and 1 more

Abstract readReview
In one paragraph

Review in Journal of nanobiotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Yingqi Zhu *Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Shanghai, 200241, China.
Zhuangli Bi *Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Shanghai, 200241, China.
Zichen ZhangShanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Shanghai, 200241, China.
Miao ZhangShanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Shanghai, 200241, China.
Qingqing DuShanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Shanghai, 200241, China.
Mingxing HuShanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Shanghai, 200241, China.
Ting ZhouShanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Shanghai, 200241, China.
Yiming FanShanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Shanghai, 200241, China.
Shu ZhangDepartment of Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. superdrzhang@yeah.net.
Guijun WangCollege of Animal Science and Technology, Anhui Agricultural University, Hefei, 230036, China. wgj2018@ahau.edu.cn.
Guangqing LiuShanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Shanghai, 200241, China. liugq@shvri.ac.cn.

Funding

the Central Public-interest Scientific Institution Basal Research Fund 2026JB09the China Postdoctoral Science Foundation 2024M753586the National Key Research and Development Program of China 2023YFD1800700the National Natural Science Foundation of China 32473001
6 · The paper itself

Abstract

Virus-like particles (VLPs) are engineered nanoplatforms that mimic viral structures, offering high immunogenicity, biocompatibility, and functional versatility for cancer immunotherapy. While widely explored in human oncology as nanovaccines and targeted delivery systems for chemo-/immuno-therapeutics and genetic payloads (e.g., mRNA, siRNA, and CRISPR/Cas systems), their potential in veterinary oncology remains underexploited. This review synthesizes recent advances in VLP design, including scaffold engineering, antigen display, cargo encapsulation, and surface functionalization, and discusses the mechanistic basis of VLP-induced antitumor immunity, encompassing dendritic cell activation, adaptive immune amplification, and tumor microenvironment remodeling. Importantly, we highlight the emerging role of companion animals with spontaneous tumors-such as lymphoma, melanoma, and mammary carcinoma-as immunocompetent translational models within the One Health framework. Comparative oncology reveals striking parallels in oncogenic pathways, immune landscapes, and therapeutic responses, supporting the use of canine and feline cancers as biologically relevant intermediates between murine studies and human clinical trials. We provide an evidence-based assessment of representative VLP platforms, evaluate their translational readiness, and examine cross-species opportunities for shared target development, biomarker discovery, and regulatory convergence, while also addressing species-specific biological and technical limitations. Finally, we propose a forward-looking roadmap that prioritizes manufacturing standardization, biomarker development, comparative validation, precision engineering, and emerging technologies such as AI-guided design and tumor-on-chip systems. Collectively, we position One Health as an operational strategy to accelerate the bidirectional translation of VLP-based immunotherapies for both human and veterinary cancer patients.

Indexed as

ImmunotherapyNeoplasmsVaccines, Virus-Like ParticleVeterinary MedicineAnimalsCancer VaccinesCatsDogsHumansNanovaccinesCancer VaccinesNanovaccinesVaccines, Virus-Like ParticleCancer immunotherapyCompanion animalsComparative oncologyDrug deliveryNanovaccineOne healthVirus-like particles (VLPs)

Identifiers

PMID42351104
PMCPMC13573491

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.