Article3 Biotech2021
Constructing and validating a diagnostic nomogram for multiple sclerosis via bioinformatic analysis.
Article in 3 Biotech, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
5 citing papers in PubMed.
- Single-cell sequencing systematically analyzed the mechanism of Emdogain on the restoration of delayed replantation periodontal membrane.International journal of oral science · 2025Article
- Exploration of potential shared gene signatures between periodontitis and multiple sclerosis.BMC oral health · 2024Article
- Identification of immune cells infiltrating in hippocampus and key genes associated with Alzheimer's disease.BMC medical genomics · 2023Article
- 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.Oxidative medicine and cellular longevity · 2022Article
- Synthesis and Evaluation of the Effects of Nanoparticles of Atorvastatin and Atorvastatin on Cuprizone-Induced Demyelination: Modulation of the Nrf2/NF-kB Signaling Pathway.Iranian journal of pharmaceutical research : IJPRArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study was to identify biomarkers and construct a diagnostic prediction model for multiple sclerosis (MS). Microarray datasets in the Gene Expression Omnibus (GEO) were downloaded. Weighted gene coexpression analysis (WGCNA) was used to search for hub modules and biomarkers related to MS. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to roughly define their biological functions and pathways. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis were used to identify the diagnostic biomarkers and construct a nomogram. The calibration curve and receiver operating characteristic (ROC) curve were used to judge the diagnostic predictive ability. In addition, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to calculate the proportion of 22 kinds of immune cells. GSE41850 was used as the training set, and GSE17048 was used as the test set. WGCNA revealed one hub module containing 165 hub genes. Most of their biological functions and pathways are related to cell metabolism and immune cell activation. The diagnostic nomogram contained SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-021-02675-1.
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Registered trials
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