Evidence map›Paper›PMID 34489947›Full record

ArticleFrontiers in immunology2021

Discovery of Novel Biomarkers for Diagnosing and Predicting the Progression of Multiple Sclerosis Using TMT-Based Quantitative Proteomics.

Yijun Shi, Yaowei Ding, Guoge Li, Lijuan Wang, Rasha Alsamani Osman, Jialu Sun, Lingye Qian, Guanghui Zheng, Guojun Zhang

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 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.3field-weighted citation impact, top 12% 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

12 citing papers in PubMed, 21 citations in OpenAlex.

  1. Biomaterials for biomarker imaging and detection.Journal of advanced research · 2026
    Review
  2. Recent Advances in Human Cerebrospinal Fluid Proteomics.Methods in molecular biology (Clifton, N.J.) · 2025
    Article
  3. Article
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  11. Proteomics in Multiple Sclerosis: The Perspective of the Clinician.International journal of molecular sciences · 2022
    Review
  12. 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 at 2 institutions in 1 country.

Yijun ShiLaboratory of Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Yaowei DingLaboratory of Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Guoge LiLaboratory of Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Lijuan WangLaboratory of Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Rasha Alsamani OsmanLaboratory of Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Jialu SunLaboratory of Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Lingye QianLaboratory of Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Guanghui ZhengLaboratory of Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Guojun ZhangLaboratory of Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Beijing Tian Tan Hospital · CNCapital Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Here, we aimed to identify protein biomarkers that could rapidly and accurately diagnose multiple sclerosis (MS) using a highly sensitive proteomic immunoassay. Methods: Tandem mass tag (TMT) quantitative proteomic analysis was performed to determine the differentially expressed proteins (DEPs) in cerebrospinal fluid (CSF) samples collected from 10 patients with MS and 10 non-inflammatory neurological controls (NINCs). The DEPs were analyzed using bioinformatics tools, and the candidate proteins were validated using the ELISA method in another cohort comprising 160 samples (paired CSF and plasma of 40 patients with MS, CSF of 40 NINCs, and plasma of 40 healthy individuals). Receiver operating characteristic (ROC) curves were used to determine the diagnostic potential of this method. Results: Compared to NINCs, we identified 83 CSF-specific DEPs out of a total of 343 proteins in MS patients. Gene ontology (GO) enrichment analysis revealed that these DEPs are mainly involved in platelet degranulation, negative regulation of proteolysis, and post-translational protein modification. Pathway enrichment analysis revealed that the complement and coagulation cascades, Ras signaling pathway, and PI3K-Akt signaling pathway are the main components. Insulin-like growth factor-binding protein 7 (IGFBP7), insulin-like growth factor 2 (IGF2), and somatostatin (SST) were identified as the potential proteins with high scores, degree, and centrality in the protein-protein interaction (PPI) network. We validated the expression of these three proteins using ELISA. Compared to NINCs, the level of CSF IGFBP7 was significantly upregulated, and the level of CSF SST was significantly downregulated in the MS group. Conclusion: Our results suggest that SST and IGFBP7 might be associated with the pathogenesis of MS and would be helpful in diagnosing MS. Since IGFBP7 was used to classify relapsing remitting MS (RRMS) and secondary progressive MS (SPMS) patients, therefore, it may act as a potential key marker and therapeutic target in MS.

Indexed as

AdultBiomarkersFemaleHumansInsulin-Like Growth Factor Binding ProteinsMaleMiddle AgedMultiple SclerosisProteomicsSomatostatinBiomarkersinsulin-like growth factor binding protein-related protein 1Insulin-Like Growth Factor Binding ProteinsSomatostatinbiomarkerdifferentially expressed proteinsIGFBP7multiple sclerosisproteomicsSST

Identifiers

PMID34489947
PMCPMC8417809
OpenAlexW3196055180

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.