Evidence map›Paper›PMID 35197962›Full record

ArticleFrontiers in immunology2021

Exploration of Potential Integrated Models of N6-Methyladenosine Immunity in Systemic Lupus Erythematosus by Bioinformatic Analyses.

Xingwang Zhao, Lan Ge, Juan Wang, Zhiqiang Song, Bing Ni, Xiaochong He, Zhihua Ruan, Yi You

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
1.6field-weighted citation impact, top 17% 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

18 citing papers in PubMed, 19 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Crosstalk between N6-methyladenosine modification and ncRNAs in rheumatic diseases: therapeutic and diagnostic implications.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2025
    Review
  6. The Role of MClinical reviews in allergy & immunology · 2025
    Review
  7. The mJournal of inflammation research · 2025
    Review
  8. Review
  9. Article
  10. Exploring the impact of mFrontiers in immunology · 2024
    Review
  11. Article
  12. Review
  13. TIGIT axis: novel immune checkpoints in anti-leukemia immunity.Clinical and experimental medicine · 2023
    Review
  14. Article
  15. The role of N6-methyladenosine (mFrontiers in medicine · 2023
    Review
  16. The Role of NOxidative medicine and cellular longevity · 2022
    Review
  17. Article
  18. Article
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

8 authors at 2 institutions in 1 country.

Xingwang ZhaoDepartment of Dermatology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Lan GeDepartment of Dermatology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Juan WangDepartment of Dermatology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Zhiqiang SongDepartment of Dermatology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Bing NiDepartment of Pathophysiology, College of High Altitude Military Medicine, Army Medical University (Third Military Medical University), Chongqing, China.
Xiaochong HeDepartment of Nursing Administration, Faculty of Nursing, Army Medical University (Third Military Medical University), Chongqing, China.
Zhihua RuanDepartment of Oncology and Southwest Cancer Center, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Yi YouDepartment of Dermatology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Army Medical University · CNSouthwest Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Systemic lupus erythematosus (SLE) is a prototypical systemic autoimmune disease of unknown etiology. The epigenetic regulation of N6-methyladenosine (m6A) modification in immunity is emerging. However, few studies have focused on SLE and m6A immune regulation. In this study, we aimed to explore a potential integrated model of m6A immunity in SLE. The models were constructed based on RNA-seq data of SLE. A consensus clustering algorithm was applied to reveal the m6A-immune signature using principal component analysis (PCA). Univariate and multivariate Cox regression analyses and Kaplan-Meier analysis were used to evaluate diagnostic differences between groups. The effects of m6A immune-related characteristics were investigated, including risk evaluation of m6A immune phenotype-related characteristics, immune cell infiltration profiles, diagnostic value, and enrichment pathways. CIBERSORT, ESTIMATE, and single-sample gene set enrichment analysis (ssGSEA) were used to evaluate the relative immune cell infiltrations (ICIs) of the samples. Conventional bioinformatics methods were used to identify key m6A regulators, pathways, gene modules, and the coexpression network of SLE. In summary, our study revealed that IGFBP3 (as a key m6A regulator) and two pivotal immune genes (CD14 and IDO1) may aid in the diagnosis and treatment of SLE. The potential integrated models of m6A immunity that we developed could guide clinical management and may contribute to the development of personalized immunotherapy strategies.

Indexed as

Computational BiologyLupus Erythematosus, SystemicAdenosineEpigenesis, GeneticGene Expression Regulation, NeoplasticHumansAdenosineN-methyladenosinebioinformatic analysesimmuneimmune cell infiltrationsm6Asystemic lupus erythematosus

Identifiers

PMID35197962
PMCPMC8859446
OpenAlexW4210757346

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.