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Submitted: 05 Mar 2025
Revision: 28 May 2025
Accepted: 10 Jun 2025
ePublished: 04 Oct 2025
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Immunopathol Persa. Inpress.
doi: 10.34172/ipp.2025.43866
  Abstract View: 10

Original

Bioinformatics analysis of differentially expressed genes in pediatric multiple sclerosis; identification of key biomarkers and therapeutic targets

Zahra Chaichy 1 ORCID logo, Maryam Naseri 2 ORCID logo, Yasaman Fatemeh Khazaee 3 ORCID logo, Gholamreza Khademi 2,4 ORCID logo, Majid Sezavar 2,4* ORCID logo

1 Department of Pediatric Diseases, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
2 Department of Pediatrics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
3 Department of Pediatrics, Faculty of Medicine, Gonabad University of Medical Sciences, Gonabad, Iran
4 Clinical Research Development Unit of Akbar Hospital, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
*Corresponding Author: Majid Sezavar, Email: Sezavardm@mums.ac.ir

Abstract

Introduction: Pediatric-onset multiple sclerosis (POMS) is a rare but severe form of MS that presents before the age of 18, leading to long-term disability. Despite its clinical significance, the molecular mechanisms driving POMS remain poorly understood.

Objectives: This study aims to identify key genes, pathways, and potential therapeutic targets involved in POMS using integrative bioinformatics approaches.

Materials and Methods: In this in silico study, gene expression profiles from peripheral blood mononuclear cells of POMS patients and healthy controls were analyzed using the GEO2R platform (GSE203241 dataset). Differentially expressed genes (DEGs) were identified using adjusted P value < 0.05 and a fold change>1.1 or <-1.1. Gene ontology (GO) and pathway enrichment analyses were conducted using BiNGO and Enrichr to explore biological functions and dysregulated pathways. A protein-protein interaction (PPI) network was constructed using STRING (search tool for the retrieval of interacting genes/proteins) and analyzed by Cytoscape with CytoHubba and CytoCluster plugins to identify hub genes and functional modules. Promoter motif analysis was conducted using Tomtom and GoMo to uncover regulatory elements, while DrugBank was utilized to identify potential drug-target interactions.

Results: A total of 1101 DEGs were identified, with enrichment in immune response pathways, cytokine signaling, and interferon responses. PPI network analysis revealed 15 hub genes, with MYC, HSP90AA1, JUN, HSPA4, and HIF1A identified by two independent algorithms, highlighting their central role in POMS. CytoCluster analysis uncovered functional modules related to RNA metabolism and vesicle transport. Promoter motif analysis revealed transcription factor binding sites that could influence hub gene expression. Drug-target analysis identified Food and Drug Administration (FDA)-approved and investigational drugs that interact with key hub proteins, offering potential therapeutic candidates.

Conclusion: This study provides a comprehensive molecular overview of POMS, identifying critical genes, regulatory elements, and potential drug targets. These findings offer valuable insights into POMS pathogenesis and highlight novel avenues for therapeutic intervention, advancing the prospects for personalized treatment strategies in pediatric MS.


Citation: Chaichy Z, Naseri M, Khazaee YF, Khademi G, Sezavar M. Bioinformatics analysis of differentially expressed genes in pediatric multiple sclerosis; identification of key biomarkers and therapeutic targets. Immunopathol Persa. 2025;x(x):e43866. DOI:10.34172/ipp.2025.43866.
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