ArticleBiological research2021
Identification of blood-derived candidate gene markers and a new 7-gene diagnostic model for multiple sclerosis.
Article in Biological research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 27 citations in OpenAlex.
- Artificial Intelligence Algorithm Based on Genetics to Predict Responses to Interferon-Beta Treatment in Multiple Sclerosis Patients.Bioengineering (Basel, Switzerland) · 2026Article
- Translational medicine research on the role of key gene network modulation mediated by procyanidin B2 in the precise diagnosis and treatment of multiple sclerosis.Journal of translational medicine · 2025Article
- A novel gene signature for forecasting time to next relapse in multiple sclerosis using peripheral blood mononuclear cells.BMC neurology · 2025Article
- 4D marmoset brain map reveals MRI and molecular signatures for onset of multiple sclerosis-like lesions.Science (New York, N.Y.) · 2025Article
- Identification of immune-related mitochondrial metabolic disorder genes in septic shock using bioinformatics and machine learning.Hereditas · 2024Article
- A machine learning approach to determine the risk factors for fall in multiple sclerosis.BMC medical informatics and decision making · 2024Article
- Modulating the RPS27A/PSMD12/NF-κB pathway to control immune response in mouse brain ischemia-reperfusion injury.Molecular medicine (Cambridge, Mass.) · 2024Article
- B cell depletion attenuates CD27 signaling of T helper cells in multiple sclerosis.Cell reports. Medicine · 2024Article
- Article
- A 4D transcriptomic map for the evolution of multiple sclerosis-like lesions in the marmoset brain.bioRxiv : the preprint server for biology · 2023Article
- AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation.Science advances · 2023Article
- 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
- A Deep Learning Approach for Predicting Multiple Sclerosis.Micromachines · 2023Article
- Attention to time-of-day variability improves the reproducibility of gene expression patterns in multiple sclerosis.iScience · 2021Article
- Constructing a Multiple Sclerosis Diagnosis Model Based on Microarray.Frontiers in neurology · 2021Article
- Identification of lncRNA and mRNA Expression Profile in Relapsed Graves' Disease.Frontiers in cell and developmental biology · 2021Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMultiple sclerosis (MS) is a central nervous system disease with a high disability rate. Modern molecular biology techniques have identified a number of key genes and diagnostic markers to MS, but the etiology and pathogenesis of MS remain unknown.
resultsIn this study, the integration of three peripheral blood mononuclear cell (PBMC) microarray datasets and one peripheral blood T cells microarray dataset allowed comprehensive network and pathway analyses of the biological functions of MS-related genes. Differential expression analysis identified 78 significantly aberrantly expressed genes in MS, and further functional enrichment analysis showed that these genes were associated with innate immune response-activating signal transduction (p = 0.0017), neutrophil mediated immunity (p = 0.002), positive regulation of innate immune response (p = 0.004), IL-17 signaling pathway (p < 0.035) and other immune-related signaling pathways. In addition, a network of MS-specific protein-protein interactions (PPI) was constructed based on differential genes. Subsequent analysis of network topology properties identified the up-regulated CXCR4, ITGAM, ACTB, RHOA, RPS27A, UBA52, and RPL8 genes as the hub genes of the network, and they were also potential biomarkers of MS through Rap1 signaling pathway or leukocyte transendothelial migration. RT-qPCR results demonstrated that CXCR4 was obviously up-regulated, while ACTB, RHOA, and ITGAM were down-regulated in MS patient PBMC in comparison with normal samples. Finally, support vector machine was employed to establish a diagnostic model of MS with a high prediction performance in internal and external datasets (mean AUC = 0.97) and in different chip platform datasets (AUC = (0.93).
conclusionThis study provides new understanding for the etiology/pathogenesis of MS, facilitating an early identification and prediction of MS.
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