ArticlePeerJ2020
Identification of key genes associated with multiple sclerosis based on gene expression data from peripheral blood mononuclear cells.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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.
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Who cites it
18 citing papers in PubMed.
- Article
- Phenotypic Changes in a Monocyte Cluster with High Interleukin-1 Beta Expression during Long-Term Anti-CD20 Therapy.Annals of neurology · 2025Article
- Assessing Peripheral Blood Biomarkers and Predictive Patterns in Multiple Sclerosis Using Cytokines and Immune Gene Expression Profiles in Ocrelizumab-Treated Patients: Tracking Tumor Necrosis Factor.International journal of molecular sciences · 2025Article
- The Polygenic Nature of Multiple Sclerosis: Genetic Variants, Immunological Modulation, and Environmental Connections.Endocrine, metabolic & immune disorders drug targets · 2025Review
- Investigation of miR-335-5p and Its Target Gene C1QA Associated with the Complement System in Conversion from Clinically Isolated Syndrome to Multiple Sclerosis.Noro psikiyatri arsivi · 2025Article
- Quantitative proteomics and multi-omics analysis identifies potential biomarkers and the underlying pathological molecular networks in Chinese patients with multiple sclerosis.BMC neurology · 2024Article
- Single-cell profiling indicates a high similarity between immune cells in the cerebrospinal fluid and in meningeal ectopic lymphoid tissue in experimental autoimmune encephalomyelitis.Frontiers in immunology · 2024Article
- Understanding the role of potential biomarkers in attenuating multiple sclerosis progression via multiomics and network-based approach.PloS one · 2024Article
- Shared Molecular Signatures Across Zika Virus Infection and Multiple Sclerosis Highlight AP-1 Transcription Factor as a Potential Player in Post-ZIKV MS-Like Phenotypes.Molecular neurobiology · 2023Article
- Article
- Gene network revealsFrontiers in neurology · 2023Article
- Multiple sclerosis and computational biology (Review).Biomedical reports · 2022Review
- An Enhanced Multiple Sclerosis Disease Diagnosis via an Ensemble Approach.Diagnostics (Basel, Switzerland) · 2022Article
- Identification of potential regulatory long non-coding RNA-associated competing endogenous RNA axes in periplaque regions in multiple sclerosis.Frontiers in genetics · 2022Article
- Transcriptomic and macroscopic architectures of intersubject functional variability in human brain white-matter.Communications biology · 2021Article
- RNA Sequencing of CD4Journal of molecular neuroscience : MN · 2021Article
- Imprinted Genes and Multiple Sclerosis: What Do We Know?International journal of molecular sciences · 2021Review
- Gene correlation network analysis to identify regulatory factors in sepsis.Journal of translational medicine · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to identify the potential key candidate genes of multiple sclerosis (MS) and uncover mechanisms in MS. We combined data from the microarray expression profile of three MS stages and performed bioinformatics analysis. Differentially expressed genes (DEGs) were identified among the distinct stages of MS and healthy controls, and a total of 349 shared DEGs were identified. Gene ontology (GO) and pathway enrichment analyses showed that the DEGs were significantly enriched in the biological processes (BPs) of purine-related metabolic processes and signaling, especially the common DEGs, which were enriched in some immunological processes. Most of the DEGs were enriched in signaling pathways associated with the immune system, some immune diseases and infectious disease pathways. Through a protein-protein interaction (PPI) network analysis and a gene expression regulatory network constructed with MS-related miRNAs, we confirmed FOS, TP53, VEGFA, JUN, HIF1A, RB1, PTGS2, CXCL8, OAS2, NFKBIA and OAS1 as candidate genes of MS. Furthermore , we explored the potential SNPs associated with MS by database mining. In conclusion, this study provides the identified genes, SNPs, biological processes, and cellular pathways associated with MS. The uncovered candidate genes may be potential biomarkers involved in the diagnosis and therapy of MS.
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