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Prognostic significance of migrasomes in neuroblastoma through machine learning and multi-omics

Authors :
Wanrong Li
Yuren Xia
Jian Wang
Hao Jin
Xin Li
Source :
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Publication Year :
2024
Publisher :
Nature Portfolio, 2024.

Abstract

Abstract This study explores migrasomes' role in neuroblastoma, a common malignant tumor in children, and their potential impact on tumor formation. We analyzed neuroblastoma RNA-seq datasets from public databases, including GSE62564, GSE181559, target, and fwr144. Through data normalization and unsupervised classification using migrasome-specific molecular markers, Differentially Expressed Genes were identified, followed by functional enrichment analysis. Our novel migrasome-associated machine learning model, MigScore, was developed using ten algorithms and 101 combinations, validated on two single-cell datasets. This enabled immune infiltration assessment and drug compatibility prediction, highlighting the utility of MS275, a histone deacetylase inhibitor. Results showed a significant inverse relationship between MigScore and favorable clinical outcomes, elucidating the link between migrasome pathways and tumor immunogenicity. These findings suggest that migrasomes are crucial in neuroblastoma prognosis, leading to the possibility of personalized treatment strategies and improved outcomes.

Details

Language :
English
ISSN :
20452322
Volume :
14
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
edsdoj.2ba71b30effc48bf94365ca3795bc3d9
Document Type :
article
Full Text :
https://doi.org/10.1038/s41598-024-67682-7