Evidence map›Paper›PMID 36100873›Full record

ArticleBMC bioinformatics2022

Investigation of CRS-associated cytokines in CAR-T therapy with meta-GNN and pathway crosstalk.

Zhenyu Wei, Qi Cheng, Nan Xu, Chengkui Zhao, Jiayu Xu, Liqing Kang, Xiaoyan Lou, Lei Yu, Weixing Feng

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 18% 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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. Pooled it
  2. AnFrontiers in cellular and infection microbiology · 2026
    Article
  3. Review
  4. Review
  5. Article
  6. Article
  7. Review
  8. [Recent research on chimeric antigen receptor T cells in children with refractory/relapsed acute lymphoblastic leukemia].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2023
    Review
  9. Review
  10. 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

9 authors at 2 institutions in 1 country.

Zhenyu Wei *College of Intelligent Systems Science and Engineering, Institute of Intelligent System and Bioinformatics, Harbin Engineering University, Harbin, 150001, China.
Qi Cheng *College of Intelligent Systems Science and Engineering, Institute of Intelligent System and Bioinformatics, Harbin Engineering University, Harbin, 150001, China.
Nan XuShanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, Institute of Biomedical Engineering and Technology, East China Normal University, No. 3663 North Zhongshan Road, Shanghai, 200065, China.
Chengkui ZhaoCollege of Intelligent Systems Science and Engineering, Institute of Intelligent System and Bioinformatics, Harbin Engineering University, Harbin, 150001, China.
Jiayu XuCollege of Intelligent Systems Science and Engineering, Institute of Intelligent System and Bioinformatics, Harbin Engineering University, Harbin, 150001, China.
Liqing KangShanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, Institute of Biomedical Engineering and Technology, East China Normal University, No. 3663 North Zhongshan Road, Shanghai, 200065, China.
Xiaoyan LouShanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, Institute of Biomedical Engineering and Technology, East China Normal University, No. 3663 North Zhongshan Road, Shanghai, 200065, China.
Lei YuShanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, Institute of Biomedical Engineering and Technology, East China Normal University, No. 3663 North Zhongshan Road, Shanghai, 200065, China. ylyh188@163.com.
Weixing FengCollege of Intelligent Systems Science and Engineering, Institute of Intelligent System and Bioinformatics, Harbin Engineering University, Harbin, 150001, China. fengweixing@hrbeu.edu.cn.
Harbin Engineering University · CNEast China Normal University · CN

Funding

Yu Lei 82073800
6 · The paper itself

Abstract

backgroundChimeric antigen receptor T-cell (CAR-T) therapy is a new and efficient cellular immunotherapy. The therapy shows significant efficacy, but also has serious side effects, collectively known as cytokine release syndrome (CRS). At present, some CRS-related cytokines and their roles in CAR-T therapy have been confirmed by experimental studies. However, the mechanism of CRS remains to be fully understood.

methodsBased on big data for human protein interactions and meta-learning graph neural network, we employed known CRS-related cytokines to comprehensively investigate the CRS associated cytokines in CAR-T therapy through protein interactions. Subsequently, the clinical data for 119 patients who received CAR-T therapy were examined to validate our prediction results. Finally, we systematically explored the roles of the predicted cytokines in CRS occurrence by protein interaction network analysis, functional enrichment analysis, and pathway crosstalk analysis.

resultsWe identified some novel cytokines that would play important roles in biological process of CRS, and investigated the biological mechanism of CRS from the perspective of functional analysis.

conclusions128 cytokines and related molecules had been found to be closely related to CRS in CAR-T therapy, where several important ones such as IL6, IFN-γ, TNF-α, ICAM-1, VCAM-1 and VEGFA were highlighted, which can be the key factors to predict CRS.

Indexed as

Receptors, Chimeric AntigenCytokine Release SyndromeCytokinesHumansImmunotherapy, AdoptiveT-LymphocytesCytokinesReceptors, Chimeric AntigenCAR-T therapyCytokine release syndromeFunctional enrichment analysisMeta-learning graph neural networkPathway crosstalk

Identifiers

PMID36100873
PMCPMC9469618
OpenAlexW4295756007

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.