Evidence map›Paper›PMID 34294105›Full record

ArticleJournal of translational medicine2021

N6-Methyladenosine RNA modification in cerebrospinal fluid as a novel potential diagnostic biomarker for progressive multiple sclerosis.

Fei Ye, Tianzhu Wang, Xiaoxin Wu, Jie Liang, Jiaoxing Li, Wenli Sheng

Open access · goldAbstract read
In one paragraph

Article in Journal of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
1.8field-weighted citation impact, top 14% 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

27 citing papers in PubMed, 32 citations in OpenAlex.

  1. mNature reviews. Neuroscience · 2026
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  11. Reduced Expression of mInternational journal of molecular sciences · 2025
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  12. The Role of MClinical reviews in allergy & immunology · 2025
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  13. The mJournal of inflammation research · 2025
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  16. mAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024
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  18. Exploring the impact of mFrontiers in immunology · 2024
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  19. The role of mGenes & diseases · 2024
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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

6 authors at 2 institutions in 1 country.

Fei Ye *Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.ORCID 0000-0002-2359-1486
Tianzhu Wang *Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Xiaoxin Wu *Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Jie LiangDepartment of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Jiaoxing LiDepartment of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Wenli ShengDepartment of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China. shengwl@mail.sysu.edu.cn.ORCID 0000-0001-6867-371X
Sun Yat-sen University · CNThe Affiliated Yongchuan Hospital of Chongqing Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProgressive multiple sclerosis (PMS) is an uncommon and severe subtype of MS that worsens gradually and leads to irreversible disabilities in young adults. Currently, there are no applicable or reliable biomarkers to distinguish PMS from relapsing-remitting multiple sclerosis (RRMS). Previous studies have demonstrated that dysfunction of N6-methyladenosine (m6A) RNA modification is relevant to many neurological disorders. Thus, the aim of this study was to explore the diagnostic biomarkers for PMS based on m6A regulatory genes in the cerebrospinal fluid (CSF).

methodsGene expression matrices were downloaded from the ArrayExpress database. Then, we identified differentially expressed m6A regulatory genes between MS and non-MS patients. MS clusters were identified by consensus clustering analysis. Next, we analyzed the correlation between clusters and clinical characteristics. The random forest (RF) algorithm was applied to select key m6A-related genes. The support vector machine (SVM) was then used to construct a diagnostic gene signature. Receiver operating characteristic (ROC) curves were plotted to evaluate the accuracy of the diagnostic model. In addition, CSF samples from MS and non-MS patients were collected and used for external validation, as evaluated by an m6A RNA Methylation Quantification Kit and by real-time quantitative polymerase chain reaction.

resultsThe 13 central m6A RNA methylation regulators were all upregulated in MS patients when compared with non-MS patients. Consensus clustering analysis identified two clusters, both of which were significantly associated with MS subtypes. Next, we divided 61 MS patients into a training set (n = 41) and a test set (n = 20). The RF algorithm identified eight feature genes, and the SVM method was successfully applied to construct a diagnostic model. ROC curves revealed good performance. Finally, the analysis of 11 CSF samples demonstrated that RRMS samples exhibited significantly higher levels of m6A RNA methylation and higher gene expression levels of m6A-related genes than PMS samples.

conclusionsThe dynamic modification of m6A RNA methylation is involved in the progression of MS and could potentially represent a novel CSF biomarker for diagnosing MS and distinguishing PMS from RRMS in the early stages of the disease.

Indexed as

Multiple SclerosisMultiple Sclerosis, Chronic ProgressiveAdenosineBiomarkersHumansRNAYoung AdultAdenosineBiomarkersN-methyladenosineRNACerebrospinal fluid (CSF)Diagnostic biomarkerN6-methyladenosine (m6A)Progressive multiple sclerosis (PMS)

Identifiers

PMID34294105
PMCPMC8296732
OpenAlexW3186454842

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.