Evidence map›Paper›PMID 41514217›Full record

ArticleBMC neurology2026

Urinary proteomic profiling reveals diagnostic biomarkers and regulatory networks in myasthenia gravis.

Jing Yang, Xi Yang, Zhen-Kun Zhu, Liang Shen, Shi-Ge San, Lianchen Xiao, Fan Ye, Chun-Hua Wang, Kun Meng

Abstract read
In one paragraph

Article in BMC neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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

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.

Jing Yang *Department of Neurology, Central Laboratory, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China.
Xi Yang *Department of Internal Medicine, Affiliated Hospital of Xiangyang Vocational and Technical College, Xiangyang, Hubei, China.
Zhen-Kun Zhu *Department of Pathology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China.
Liang Shen *Department of Neurology, Central Laboratory, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China.
Shi-Ge SanDepartment of Neurology, Central Laboratory, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China.
Lianchen XiaoDepartment of Neurology, Central Laboratory, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China.
Fan YeDepartment of Neurology, Central Laboratory, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China. fanye@hbuas.edu.cn.
Chun-Hua WangDepartment of Neurology, Central Laboratory, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China. wchdye@126.com.
Kun MengDepartment of Neurology, Central Laboratory, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China. mengk1029@126.com.

Funding

Hubei Provincial Natural Science Foundation of China general project 2025AFB942National Natural Science Foundation of China 82303050Open Research Program of Xiangyang Central Hospital 2023NB102
6 · The paper itself

Abstract

backgroundMyasthenia gravis (MG) is a chronic autoimmune neuromuscular junction disorder mediated by autoantibodies. Existing diagnostic methods mainly rely on serum antibody detection and electrophysiological testing, which are limited by invasiveness and suboptimal sensitivity and specificity. This study aimed to identify potential urinary biomarkers for noninvasive MG diagnosis using proteomics.

methodsData-independent acquisition (DIA) proteomic profiling was performed using a high-resolution Orbitrap Astral mass spectrometer on urine samples from 10 MG patients and 10 healthy controls. Differentially expressed proteins (DEPs) were identified and analyzed through Gene Ontology and KEGG pathway enrichment. LASSO regression and support vector machine models were applied to identify hub diagnostic proteins. The expression and diagnostic performance of the hub protein TPD52 were further validated in an independent cohort of 34 participants using ELISA.

resultsA total of 2,003 urinary proteins were identified between MG patients and healthy controls, among which 216 were significantly differentially expressed (|fold change| ≥ 1.2, p < 0.05). Enrichment analysis revealed that these DEPs were associated with neurodegenerative and neuroinflammatory diseases. Eight key proteins (TPD52, SORL1, PLAU, TSPAN3, CASP14, QPCT, CILP2, and CTHRC1) were identified by both LASSO and SVM algorithms, all exhibiting strong diagnostic performance (AUC > 0.76). In the independent validation cohort, the urinary expression of TPD52 was confirmed to be significantly elevated in MG patients, and ROC analysis yielded an AUC of 0.746, supporting its potential diagnostic value.

conclusionThis pilot urinary proteomic study provides preliminary evidence for urinary TPD52 as a potential noninvasive biomarker for MG and suggests its possible involvement in MG pathogenesis. These findings offer new insights into the molecular mechanisms of MG and lay a foundation for applying urinary proteomics in neuroimmune disorders.

Indexed as

Gene Regulatory NetworksMyasthenia GravisProteomicsAdultBiomarkersFemaleHumansMaleMiddle AgedBiomarkersBiomarkersMyasthenia gravisNoninvasive diagnosisTPD52Urinary proteomics

Identifiers

PMID41514217
PMCPMC12882442

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.