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Tytuł pozycji:

Constructing a Multiple Sclerosis Diagnosis Model Based on Microarray

Tytuł:
Constructing a Multiple Sclerosis Diagnosis Model Based on Microarray
Autorzy:
Haoran Li
Hongyun Wu
Weiying Li
Jiapei Zhou
Jie Yang
Wei Peng
Temat:
multiple sclerosis
diagnosis model
microarray
Random Forest
logistic regression
Area Under Curve
Neurology. Diseases of the nervous system
RC346-429
Źródło:
Frontiers in Neurology, Vol 12 (2022)
Wydawca:
Frontiers Media S.A., 2022.
Rok publikacji:
2022
Kolekcja:
LCC:Neurology. Diseases of the nervous system
Typ dokumentu:
article
Opis pliku:
electronic resource
Język:
English
ISSN:
1664-2295
Relacje:
https://www.frontiersin.org/articles/10.3389/fneur.2021.721788/full; https://doaj.org/toc/1664-2295
DOI:
10.3389/fneur.2021.721788
Dostęp URL:
https://doaj.org/article/116b5fd333964b598f5c077429cf5fdc  Link otwiera się w nowym oknie
Numer akcesji:
edsdoj.116b5fd333964b598f5c077429cf5fdc
Czasopismo naukowe
IntroductionMultiple sclerosis is an immune-mediated demyelinating disorder of the central nervous system. Because of the complexity of etiology, pathology, clinical manifestations, and the diversity of classification, the diagnosis of MS is very difficult. We found that McDonald Criteria is very strict and relies heavily on the evidence for DIS and DIT. Therefore, we hope to find a new method to supplement the evidence and improve the accuracy of MS diagnosis.ResultsWe finally selected GSE61240, GSE18781, and GSE185047 based on the GPL570 platform to build a diagnosis model. We initially selected 54 MS susceptibility locus genes identified by IMSGC and WTCCC2 as predictors for the model. After Random Forests and other series of screening, the logistic regression model was established with 4 genes as the final predictors. In external validation, the model showed high accuracy with an AUC of 0.96 and an accuracy of 86.30%. Finally, we established a nomogram and an online prediction tool to better display the diagnosis model.ConclusionThe diagnosis model based on microarray data in this study has a high degree of discrimination and calibration in the validation set, which is helpful for diagnosis in the absence of evidence for DIS and DIT. Only one SLE case was misdiagnosed as MS, indicating that the model has a high specificity (93.93%), which is useful for differential diagnosis. The significance of the study lies in proving that it is feasible to identify MS by peripheral blood RNA, and the further application of the model and be used as a supplement to McDonald Criteria still need to be trained with larger sample size.

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