Discovery of grey matter lesion-related immune genes for diagnostic prediction in multiple sclerosis
Background Multiple sclerosis (MS) is a chronic debilitating disease characterized by inflammatory demyelination of the central nervous system. Grey matter (GM) lesions have been shown to be closely related to MS motor deficits and cognitive impairment. In this study, GM lesion-related genes for dia...
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| Format: | Article |
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PeerJ Inc.
2023-04-01
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| Online Access: | https://peerj.com/articles/15299.pdf |
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| author | Peiyuan Zhao Xihong Liu Yunqian Wang Xinyan Zhang Han Wang Xiaodan Du Zhixin Du Liping Yang Junlin Hou |
| author_facet | Peiyuan Zhao Xihong Liu Yunqian Wang Xinyan Zhang Han Wang Xiaodan Du Zhixin Du Liping Yang Junlin Hou |
| author_sort | Peiyuan Zhao |
| collection | DOAJ |
| description | Background Multiple sclerosis (MS) is a chronic debilitating disease characterized by inflammatory demyelination of the central nervous system. Grey matter (GM) lesions have been shown to be closely related to MS motor deficits and cognitive impairment. In this study, GM lesion-related genes for diagnosis and immune status in MS were investigated. Methods Gene Expression Omnibus (GEO) databases were utilized to analyze RNA-seq data for GM lesions in MS. Differentially expressed genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) algorithm and protein-protein interaction (PPI) network were used to screen related gene modules and candidate genes. The abundance of immune cell infiltration was analyzed by the CIBERSORT algorithm. Candidate genes with strong correlation with immune cell types were determined to be hub genes. A diagnosis model of nomogram was constructed based on the hub genes. Gene set enrichment analysis (GSEA) was performed to identify the biological functions of hub genes. Finally, an MS mouse model was induced to verify the expression levels of immune hub genes. Results Nine genes were identified by WGCNA, LASSO regression and PPI network. The infiltration of immune cells was significantly different between the MS and control groups. Four genes were identified as GM lesion-related hub genes. A reliable prediction model was established by nomogram and verified by calibration, decision curve analysis and receiver operating characteristic curves. GSEA indicated that the hub genes were mainly enriched in cell adhesion molecules, cytokine-cytokine receptor interaction and the JAK-STAT signaling pathway, etc. Conclusions TLR9, CCL5, CXCL8 and PDGFRB were identified as potential biomarkers for GM injury in MS. The effectively predicted diagnosis model will provide guidance for therapeutic intervention of MS. |
| first_indexed | 2024-03-09T06:54:09Z |
| format | Article |
| id | doaj.art-6965889ae9f340448c8bbe913c267ddd |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:54:09Z |
| publishDate | 2023-04-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-6965889ae9f340448c8bbe913c267ddd2023-12-03T10:08:09ZengPeerJ Inc.PeerJ2167-83592023-04-0111e1529910.7717/peerj.15299Discovery of grey matter lesion-related immune genes for diagnostic prediction in multiple sclerosisPeiyuan Zhao0Xihong Liu1Yunqian Wang2Xinyan Zhang3Han Wang4Xiaodan Du5Zhixin Du6Liping Yang7Junlin Hou8School of Medicine, Henan University of Chinese Medicine, Zhengzhou, ChinaSchool of Medicine, Henan University of Chinese Medicine, Zhengzhou, ChinaThe First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, ChinaThe First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, ChinaThe First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, ChinaSchool of Medicine, Henan University of Chinese Medicine, Zhengzhou, ChinaSchool of Medicine, Henan University of Chinese Medicine, Zhengzhou, ChinaSchool of Medicine, Henan University of Chinese Medicine, Zhengzhou, ChinaSchool of Medicine, Henan University of Chinese Medicine, Zhengzhou, ChinaBackground Multiple sclerosis (MS) is a chronic debilitating disease characterized by inflammatory demyelination of the central nervous system. Grey matter (GM) lesions have been shown to be closely related to MS motor deficits and cognitive impairment. In this study, GM lesion-related genes for diagnosis and immune status in MS were investigated. Methods Gene Expression Omnibus (GEO) databases were utilized to analyze RNA-seq data for GM lesions in MS. Differentially expressed genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) algorithm and protein-protein interaction (PPI) network were used to screen related gene modules and candidate genes. The abundance of immune cell infiltration was analyzed by the CIBERSORT algorithm. Candidate genes with strong correlation with immune cell types were determined to be hub genes. A diagnosis model of nomogram was constructed based on the hub genes. Gene set enrichment analysis (GSEA) was performed to identify the biological functions of hub genes. Finally, an MS mouse model was induced to verify the expression levels of immune hub genes. Results Nine genes were identified by WGCNA, LASSO regression and PPI network. The infiltration of immune cells was significantly different between the MS and control groups. Four genes were identified as GM lesion-related hub genes. A reliable prediction model was established by nomogram and verified by calibration, decision curve analysis and receiver operating characteristic curves. GSEA indicated that the hub genes were mainly enriched in cell adhesion molecules, cytokine-cytokine receptor interaction and the JAK-STAT signaling pathway, etc. Conclusions TLR9, CCL5, CXCL8 and PDGFRB were identified as potential biomarkers for GM injury in MS. The effectively predicted diagnosis model will provide guidance for therapeutic intervention of MS.https://peerj.com/articles/15299.pdfMultiple sclerosisGrey matter lesionImmune infiltrationDiagnosis |
| spellingShingle | Peiyuan Zhao Xihong Liu Yunqian Wang Xinyan Zhang Han Wang Xiaodan Du Zhixin Du Liping Yang Junlin Hou Discovery of grey matter lesion-related immune genes for diagnostic prediction in multiple sclerosis PeerJ Multiple sclerosis Grey matter lesion Immune infiltration Diagnosis |
| title | Discovery of grey matter lesion-related immune genes for diagnostic prediction in multiple sclerosis |
| title_full | Discovery of grey matter lesion-related immune genes for diagnostic prediction in multiple sclerosis |
| title_fullStr | Discovery of grey matter lesion-related immune genes for diagnostic prediction in multiple sclerosis |
| title_full_unstemmed | Discovery of grey matter lesion-related immune genes for diagnostic prediction in multiple sclerosis |
| title_short | Discovery of grey matter lesion-related immune genes for diagnostic prediction in multiple sclerosis |
| title_sort | discovery of grey matter lesion related immune genes for diagnostic prediction in multiple sclerosis |
| topic | Multiple sclerosis Grey matter lesion Immune infiltration Diagnosis |
| url | https://peerj.com/articles/15299.pdf |
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