Iron metabolism disorder and multiple sclerosis: a comprehensive analysis.

Mendelian randomization bioinformatic analysis causal relationship iron metabolism multiple sclerosis

Journal

Frontiers in immunology
ISSN: 1664-3224
Titre abrégé: Front Immunol
Pays: Switzerland
ID NLM: 101560960

Informations de publication

Date de publication:
2024
Historique:
received: 26 01 2024
accepted: 04 03 2024
medline: 9 4 2024
pubmed: 9 4 2024
entrez: 9 4 2024
Statut: epublish

Résumé

Multiple sclerosis (MS) is the most common chronic inflammatory disease of the central nervous system. Currently, the pathological mechanisms of MS are not fully understood, but research has suggested that iron metabolism disorder may be associated with the onset and clinical manifestations of MS. The study utilized publicly available databases and bioinformatics techniques for gene expression data analysis, including differential expression analysis, weighted correlation network analysis, gene enrichment analysis, and construction of logistic regression models. Subsequently, Mendelian randomization was used to assess the causal relationship between different iron metabolism markers and MS. This study identified IREB2, LAMP2, ISCU, ATP6V1G1, ATP13A2, and SKP1 as genes associated with multiple sclerosis (MS) and iron metabolism, establishing their multi-gene diagnostic value for MS with an AUC of 0.83. Additionally, Mendelian randomization analysis revealed a potential causal relationship between transferrin saturation and MS (p=2.22E-02; OR 95%CI=0.86 (0.75, 0.98)), as well as serum transferrin and MS (p=2.18E-04; OR 95%CI=1.22 (1.10, 1.36)). This study comprehensively explored the relationship between iron metabolism and MS through integrated bioinformatics analysis and Mendelian randomization methods. The findings provide important insights for further research into the role of iron metabolism disorder in the pathogenesis of MS and offer crucial theoretical support for the treatment of MS.

Sections du résumé

Background UNASSIGNED
Multiple sclerosis (MS) is the most common chronic inflammatory disease of the central nervous system. Currently, the pathological mechanisms of MS are not fully understood, but research has suggested that iron metabolism disorder may be associated with the onset and clinical manifestations of MS.
Methods and materials UNASSIGNED
The study utilized publicly available databases and bioinformatics techniques for gene expression data analysis, including differential expression analysis, weighted correlation network analysis, gene enrichment analysis, and construction of logistic regression models. Subsequently, Mendelian randomization was used to assess the causal relationship between different iron metabolism markers and MS.
Results UNASSIGNED
This study identified IREB2, LAMP2, ISCU, ATP6V1G1, ATP13A2, and SKP1 as genes associated with multiple sclerosis (MS) and iron metabolism, establishing their multi-gene diagnostic value for MS with an AUC of 0.83. Additionally, Mendelian randomization analysis revealed a potential causal relationship between transferrin saturation and MS (p=2.22E-02; OR 95%CI=0.86 (0.75, 0.98)), as well as serum transferrin and MS (p=2.18E-04; OR 95%CI=1.22 (1.10, 1.36)).
Conclusion UNASSIGNED
This study comprehensively explored the relationship between iron metabolism and MS through integrated bioinformatics analysis and Mendelian randomization methods. The findings provide important insights for further research into the role of iron metabolism disorder in the pathogenesis of MS and offer crucial theoretical support for the treatment of MS.

Identifiants

pubmed: 38590521
doi: 10.3389/fimmu.2024.1376838
pmc: PMC11000231
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1376838

Informations de copyright

Copyright © 2024 Tang, Yang, Zhu, Ding, Yang, Xu and He.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Auteurs

Chao Tang (C)

Department of Neurology, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.

Jiaxin Yang (J)

School of Clinical Medicine, Guizhou Medical University, Guiyang, Guizhou, China.

Chaomin Zhu (C)

School of Clinical Medicine, Guizhou Medical University, Guiyang, Guizhou, China.

Yaqi Ding (Y)

Department of Neurology, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.

Sushuang Yang (S)

Department of Neurology, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.

Bingyang Xu (B)

School of Clinical Medicine, Guizhou Medical University, Guiyang, Guizhou, China.

Dian He (D)

Department of Neurology, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.

Classifications MeSH