Evidence map›Paper›PMID 38725856›Full record

ReviewInternational journal of biological sciences2024

Glycosylation Targeting: A Paradigm Shift in Cancer Immunotherapy.

Xueting Ren, Shuai Lin, Feng Guan, Huafeng Kang

Abstract readReview
In one paragraph

Review in International journal of biological sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

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  6. Pharmaceuticals (Basel, Switzerland) · 2026
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  13. Multi-Omics Integration for Advancing Glioma Precision Medicine.Annals of clinical and translational neurology · 2026
    Review
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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

4 authors.

Xueting RenDepartment of Oncology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Shuai LinDepartment of Oncology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Feng GuanKey Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, College of Life Sciences, Northwest University, Xi'an, Shaanxi, China.
Huafeng KangDepartment of Oncology, the Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immunotherapy has shown great potential in cancer treatment. However, even with the intervention of techniques such as immune checkpoint inhibitor therapy, tumors can still achieve immune escape, leading to a low response rate. Abnormal glycosylation is a widely recognized hallmark of cancer. The development of a complex "glyco-code" on the surface of tumor cells can potentially influence the immune system's ability to monitor tumors and can impact the anti-tumor immune response. Therefore, abnormal glycosylation has emerged as a promising target for immunotherapy. Many recent studies have shown that targeted glycosylation can reshape the tumor microenvironment (TME) and promote the immune response, thereby improving the response to immunotherapy. This review summarizes how glycosylation affects anti-tumor immune function in the TME and synthesizes the latest research progress on targeted glycosylation in immunotherapy. It is hoped that by elucidating the basic laws and biological connotations of glycosylation, this review will enable researcher to thoroughly analyze the mechanism of its influence on the immune metabolic regulation network, which will provide a theoretical tool for promoting the clinical application of glycosylation codes.

Indexed as

ImmunotherapyNeoplasmsTumor MicroenvironmentAnimalsGlycosylationHumans

Identifiers

PMID38725856
PMCPMC11077373

What OpenQuestion holds

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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.