Evidence map›Paper›PMID 39920693›Full record

ArticleBMC cancer2025

Site-specific immunoglobulin G N-glycosylation is associated with gastric cancer progression.

Tingting Xu, Jianmin Huang, Jiajing Lin, Yuanyuan Liu, Yi Wang, Wenkang Shen, Jianjie He, Shuyun Chen, Xi Zhu, Yuqin Que and 6 more

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors.

Tingting Xu *Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China.
Jianmin Huang *Digestive Endoscopy Center, Fuzhou University Affiliated Provincial Hospital, Fuzhou, 350000, China.
Jiajing LinThe Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
Yuanyuan LiuThe Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
Yi WangDepartment of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430074, China.
Wenkang ShenThe Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
Jianjie HeThe Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
Shuyun ChenDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China.
Xi ZhuDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China.
Yuqin QueDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China.
Mengting HuDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China.
Yu ChenDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China.
Liming ChengDepartment of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430074, China.
Honghao HeSino-US Telemed (Wuhan) Co., Ltd, Wuhan, 430074, China.
Xin LiuThe Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China. xliu@mail.hust.edu.cn.
Si LiuDepartment of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350122, China. siliu@fjmu.edu.cn.

Funding

High-Level Talents Research Start-Up Project of Fujian Medical University XRCZX2023015Initiative for University Students in the Training Program for Innovation and Entrepreneurial Development xy202410016Key Project of Education and Scientific Research for Young and Middle-aged People of Fujian Province JZ230019National Key Research and Development Program of China 2022YFC3400800National Key Research and Development Program of China 2022YFF1203304Natural Science Foundation of Fujian Province 2024J01577
6 · The paper itself

Abstract

backgroundThe relationship between cancer development and alterations in IgG N-glycosylation has been well-established. However, comprehensive profiling of the N-glycome and N-glycoproteome in gastric cancer (GC) remains limited. Furthermore, the prognostic potential of IgG N-glycan patterns in identifying precursors to GC has yet to be fully elucidated.

methodsThe IgG N-glycome in GC was characterized using a custom high-throughput orthogonal mass spectrometry approach. Multivariate analysis was employed to identify and assess glycomic alterations. A comprehensive bioinformatics analysis was also conducted to investigate the differential expression of N-glycosylation-related genes and their potential roles in GC pathogenesis. Additionally, interleukin-11 (IL-11) levels were quantified using a standardized enzyme-linked immunosorbent assay (ELISA).

resultsGalactosylation and sialylation of IgG decreased mainly in the IgG1 and IgG2 subclasses in GC, with subclass-specific changes in IgG3 and IgG4 galactosylation. These glycan modifications were represented by unique glycopeptides (IgG1_H5N5, IgG2_H4N3F1, IgG2_H4N4, IgG2_H4N4F1S1, IgG3/4_H4N4F1, IgG3/4_H4N4F1S1), which outperformed CA72-4 for GC diagnosis. Analysis of key glycogenes revealed differential expression patterns, implicating a functional role for IgG N-glycosylation in GC. Notably, the abundance of specific IgG glycosylation exhibited a significant correlation with serum level of IL-11.

conclusionsAlterations in subclass-specific IgG N-glycosylation represent promising biomarkers for the detection and monitoring of GC progression, potentially influenced by cytokine-driven inflammation. Understanding these changes could improve our knowledge of molecular mechanisms, aiding in diagnostic improvements and therapeutic development.

Indexed as

Immunoglobulin GStomach NeoplasmsAdultAgedBiomarkers, TumorDisease ProgressionFemaleGlycoproteinsGlycosylationHumansMaleMiddle AgedPolysaccharidesPrognosisBiomarkers, TumorGlycoproteinsglycosylated IgGImmunoglobulin GPolysaccharidesBioinformatics analysisGastric cancerIgG glycopeptidesIgG N-glycansIL-11

Identifiers

PMID39920693
PMCPMC11806667

What Socratic holds

Textmetadata
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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 Socratic graph.