Evidence map›Paper›PMID 36408104›Full record

ArticleFrontiers in aging neuroscience2022

Identification and immune characteristics of molecular subtypes related to protein glycosylation in Alzheimer's disease.

Zhaotian Ma, Fan Yang, Jiajia Fan, Xin Li, Yuanyuan Liu, Wei Chen, Honghao Sun, Tengfei Ma, Qiongying Wang, Yueriguli Maihaiti and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.7field-weighted citation impact, top 30% 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

6 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Glycosylation in aging and neurodegenerative diseases.Acta biochimica et biophysica Sinica · 2024
    Review
  6. 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

11 authors at 1 institution in 1 country.

Zhaotian MaSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Fan YangSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Jiajia FanSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Xin LiSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Yuanyuan LiuSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Wei ChenSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Honghao SunSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Tengfei MaSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Qiongying WangSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Yueriguli MaihaitiSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Xiaoqiao RenSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Beijing University of Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Protein glycosylation has been confirmed to be involved in the pathological mechanisms of Alzheimer's disease (AD); however, there is still a lack of systematic analysis of the immune processes mediated by protein glycosylation-related genes (PGRGs) in AD. Materials and methods: Transcriptomic data of AD patients were obtained from the Gene Expression Omnibus database and divided into training and verification datasets. The core PGRGs of the training set were identified by weighted gene co-expression network analysis, and protein glycosylation-related subtypes in AD were identified based on k-means unsupervised clustering. Protein glycosylation scores and neuroinflammatory levels of different subtypes were compared, and functional enrichment analysis and drug prediction were performed based on the differentially expressed genes (DEGs) between the subtypes. A random forest model was used to select important DEGs as diagnostic markers between subtypes, and a line chart model was constructed and verified in other datasets. We evaluated the differences in immune cell infiltration between the subtypes through the single-sample gene set enrichment analysis, analyzed the correlation between core diagnostic markers and immune cells, and explored the expression regulation network of the core diagnostic markers. Results: Eight core PGRGs were differentially expressed between the training set and control samples. AD was divided into two subtypes with significantly different biological processes, such as vesicle-mediated transport in synapses and neuroactive ligand-receptor interactions. The high protein glycosylation subtype had a higher level of neuroinflammation. Riluzole and sulfasalazine were found to have potential clinical value in this subtype. A reliable construction line chart model was constructed based on nine diagnostic markers, and Conclusion: Protein glycosylation and its corresponding immune process play an important role in the occurrence and development of AD. Understanding the role of PGRGs in AD may provide a new potential therapeutic target for AD.

Indexed as

Alzheimer’s diseasediagnostic modelimmune cellsmolecular subtypesprotein glycosylation-related genes

Identifiers

PMID36408104
PMCPMC9667030
OpenAlexW4308121385

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