Constructing and validating a diagnostic nomogram for multiple sclerosis via bioinformatic analysis

Li, Hao; Sun, Yong; Chen, Rong

doi:10.1007/s13205-021-02675-1

Cited by 4 publications

(4 citation statements)

References 48 publications

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“…Acting as an inhibitor of POL1, a new compound, RAM-589.555, was observed to mitigate MS-like syndromes by blocking the ribosomal RNA transcription [29]. However, the immune-related or inflammation-related pathways were not highlighted in our functional assessments, and this is not consistent with previous bioinformatics analyses [22,30]. The use of different tissue samples for the original data might explain these outcomes.…”

Section: Discussioncontrasting

confidence: 60%

“…We also identified hub genes and evaluated their usefulness for the prognosis of MS. Compared with previous reports [22,23], our study set different criteria for dataset selection and employed new bioinformatics approaches. Briefly, (1) we concentrated on the spinal cord, which is extensively involved in MS but has not been thoroughly investigated in neuropathological studies, and (2) we sought to explore the underlying mechanism by creating a ceRNA network in the key module identified to be significantly correlated with MS.…”

Section: Discussionmentioning

confidence: 99%

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Identification of Novel Key Genes and Pathways in Multiple Sclerosis Based on Weighted Gene Coexpression Network Analysis and Long Noncoding RNA-Associated Competing Endogenous RNA Network

Hao

et al. 2022

Oxidative Medicine and Cellular Longevity

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Objective. Multiple sclerosis (MS) is an autoimmune disease of the central nervous system characterized by chronic inflammation and demyelination. This study is aimed at identifying crucial genes and molecular pathways involved in MS pathogenesis. Methods. Raw data in GSE52139 were collected from the Gene Expression Omnibus. The top 50% expression variants were subjected to weighted gene coexpression network analysis (WGCNA), and the key module associated with MS occurrence was identified. A long noncoding RNA- (lncRNA-) associated competing endogenous RNA (ceRNA) network was constructed in the key module. The hub gene candidates were subsequently verified in an individual database. Results. Of the 18 modules obtained, the cyan module was designated as the key module. The established ceRNA network was composed of seven lncRNAs, 45 mRNAs, and 21 microRNAs (miRNAs), and the FAM13A-AS1 was the lncRNA with the highest centrality. Functional assessments indicated that the genes in the cyan module primarily gathered in ribosome-related functional terms. Interestingly, the targeted mRNAs of the ceRNA network enriched in diverse categories. Moreover, highly expressed CYBRD1, GNG12, and SMAD1, which were identified as hub genes, may be associated with “valine leucine and isoleucine degradation,” “base excision repair,” and “fatty acid metabolism,” respectively, according to the results of single gene-based genomes and gene set enrichment analysis (GSEA). Conclusions. Combined with the WGCNA and ceRNA network, our findings provide novel insights into the pathogenesis of MS. The hub genes discovered herein might also serve as novel biomarkers that correlate with the development and management of MS.

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Section: Discussioncontrasting

confidence: 60%

Section: Discussionmentioning

confidence: 99%

Identification of Novel Key Genes and Pathways in Multiple Sclerosis Based on Weighted Gene Coexpression Network Analysis and Long Noncoding RNA-Associated Competing Endogenous RNA Network

Hao

et al. 2022

Oxidative Medicine and Cellular Longevity

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“…Genes were clustered based on the phase dissimilarity machine. The division of modules was based on the high topological overlap of genes within the modules [28]. We selected the modules associated with disease for subsequence analysis.…”

Section: Weighted Gene Co-expression Network Analysis (Wgcna)mentioning

confidence: 99%

Identification of immune cells infiltrating in hippocampus and key genes associated with Alzheimer’s disease

Liu

et al. 2023

BMC Med Genomics

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Alzheimer’s disease (AD) is the most prevalent cause of dementia and is primarily associated with memory impairment and cognitive decline, but the etiology of AD has not been elucidated. In recent years, evidence has shown that immune cells play critical roles in AD pathology. In the current study, we collected the transcriptomic data of the hippocampus from gene expression omnibus database, and investigated the effect of immune cell infiltration in the hippocampus on AD, and analyzed the key genes that influence the pathogenesis of AD patients. The results revealed that the relative abundance of immune cells in the hippocampus of AD patients was altered. Of all given 28 kinds of immune cells, monocytes were the important immune cell associated with AD. We identified 4 key genes associated with both AD and monocytes, including KDELR1, SPTAN1, CDC16 and RBBP6, and they differentially expressed in 5XFAD mice and WT mice. The logistic regression and random forest models based on the 4 key genes could effectively distinguish AD from healthy samples. Our research provided a new perspective on immunotherapy for AD patients.

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“…Multiple sclerosis (MS) is an autoimmune disease that causes inflammatory demyelinating lesions of white matter in the central nervous system [ 4 ]. Even though the cause of MS is unclear, current findings suggest that environmental and genetic variables contribute to the disease’s development [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Exploration of potential shared gene signatures between periodontitis and multiple sclerosis

Wu,

Cheng,

Zhang

et al. 2024

BMC Oral Health

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Background Although periodontitis has previously been reported to be linked with multiple sclerosis (MS), but the molecular mechanisms and pathological interactions between the two remain unclear. This study aims to explore potential crosstalk genes and pathways between periodontitis and MS. Methods Periodontitis and MS data were obtained from the Gene Expression Omnibus (GEO) database. Shared genes were identified by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Then, enrichment analysis for the shared genes was carried out by multiple methods. The least absolute shrinkage and selection operator (LASSO) regression was used to obtain potential shared diagnostic genes. Furthermore, the expression profile of 28 immune cells in periodontitis and MS was examined using single-sample GSEA (ssGSEA). Finally, real-time quantitative fluorescent PCR (qRT-PCR) and immune histochemical staining were employed to validate Hub gene expressions in periodontitis and MS samples. Results FAM46C, SLC7A7, LY96, CFI, DDIT4L, CD14, C5AR1, and IGJ genes were the shared genes between periodontitis, and MS. GO analysis revealed that the shared genes exhibited the greatest enrichment in response to molecules of bacterial origin. LASSO analysis indicated that CFI, DDIT4L, and FAM46C were the most effective shared diagnostic biomarkers for periodontitis and MS, which were further validated by qPCR and immunohistochemical staining. ssGSEA analysis revealed that T and B cells significantly influence the development of MS and periodontitis. Conclusions FAM46C, SLC7A7, LY96, CFI, DDIT4L, CD14, C5AR1, and IGJ were the most important crosstalk genes between periodontitis, and MS. Further studies found that CFI, DDIT4L, and FAM46C were potential biomarkers in periodontitis and MS.

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Constructing and validating a diagnostic nomogram for multiple sclerosis via bioinformatic analysis

Cited by 4 publications

References 48 publications

Identification of Novel Key Genes and Pathways in Multiple Sclerosis Based on Weighted Gene Coexpression Network Analysis and Long Noncoding RNA-Associated Competing Endogenous RNA Network

Identification of Novel Key Genes and Pathways in Multiple Sclerosis Based on Weighted Gene Coexpression Network Analysis and Long Noncoding RNA-Associated Competing Endogenous RNA Network

Identification of immune cells infiltrating in hippocampus and key genes associated with Alzheimer’s disease

Exploration of potential shared gene signatures between periodontitis and multiple sclerosis

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