ArticleComputational intelligence and neuroscience2022
Integration of Genetic and Immune Infiltration Insights into Data Mining of Multiple Sclerosis Pathogenesis.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Integrated network analysis and experimental validation identify CCNA2, CD44, and STAT1 as clinically relevant hub genes in oral squamous cell carcinoma.Frontiers in molecular biosciences · 2026Article
- Gene expression and alternative splicing analysis in a large-scale Multiple Sclerosis study.medRxiv : the preprint server for health sciences · 2024Article
- Exploration of potential shared gene signatures between periodontitis and multiple sclerosis.BMC oral health · 2024Article
- Integration of Genetic and Immune Infiltration Insights into Data Mining of Multiple Sclerosis Pathogenesis.Computational intelligence and neuroscience · 2022Article
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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
Background: Multiple sclerosis (MS) is an immune-mediated demyelinating disease of the central nervous system. MS pathogenesis is closely related to the environment, genetic, and immune system, but the underlying interactions have not been clearly elucidated. This study aims to unveil the genetic basis and immune landscape of MS pathogenesis with bioinformatics. Methods: Gene matrix was retrieved from the gene expression database NCBI-GEO. Then, bioinformatics was used to standardize the samples and obtain differentially expressed genes (DEGs). The protein-protein interaction network was constructed with DEGs on the STRING website. Cytohubba plug-in and MCODE plug-in were used to mine hub genes. Meanwhile, the CIBERSORTX algorithm was used to explore the characteristics of immune cell infiltration in MS brain tissues. Spearman correlation analysis was performed between genes and immune cells, and the correlation between genes and different types of brain tissues was also analyzed using the WGCNA method. Results: A total of 90 samples from 2 datasets were included, and 882 DEGs and 10 hub genes closely related to MS were extracted. Functional enrichment analysis suggested the role of immune response in MS. Besides, CIBERSORTX algorithm results showed that MS brain tissues contained a variety of infiltrating immune cells. Correlation analysis suggested that the hub genes were highly relevant to chronic active white matter lesions. Certain hub genes played a role in the activation of immune cells such as macrophages and natural killer cells. Conclusions: Our study shall provide guidance for the further study of the genetic basis and immune infiltration mechanism of MS.
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