Gene Set Enrichment Analysis of Interaction Networks Weighted by Node Centrality.
gene set enrichment analysis
network medicine
neurodegeneration
neurodevelopment
systems medicine
topological analysis
Journal
Frontiers in genetics
ISSN: 1664-8021
Titre abrégé: Front Genet
Pays: Switzerland
ID NLM: 101560621
Informations de publication
Date de publication:
2021
2021
Historique:
received:
02
07
2020
accepted:
04
02
2021
entrez:
15
3
2021
pubmed:
16
3
2021
medline:
16
3
2021
Statut:
epublish
Résumé
Gene set enrichment analysis (GSEA) is a powerful tool to associate a disease phenotype to a group of genes/proteins. GSEA attributes a specific weight to each gene/protein in the input list that depends on a metric of choice, which is usually represented by quantitative expression data. However, expression data are not always available. Here, GSEA based on betweenness centrality of a protein-protein interaction (PPI) network is described and applied to two cases, where an expression metric is missing. First, personalized PPI networks were generated from genes displaying alterations (assessed by array comparative genomic hybridization and whole exome sequencing) in four probands bearing a 16p13.11 microdeletion in common and several other point variants. Patients showed disease phenotypes linked to neurodevelopment. All networks were assembled around a cluster of first interactors of altered genes with high betweenness centrality. All four clusters included genes known to be involved in neurodevelopmental disorders with different centrality. Moreover, the GSEA results pointed out to the evidence of "cell cycle" among enriched pathways. Second, a large interaction network obtained by merging proteomics studies on three neurodegenerative disorders was analyzed from the topological point of view. We observed that most central proteins are often linked to Parkinson's disease. The selection of these proteins improved the specificity of GSEA, with "Metabolism of amino acids and derivatives" and "Cellular response to stress or external stimuli" as top-ranked enriched pathways. In conclusion, betweenness centrality revealed to be a suitable metric for GSEA. Thus, centrality-based GSEA represents an opportunity for precision medicine and network medicine.
Identifiants
pubmed: 33719329
doi: 10.3389/fgene.2021.577623
pmc: PMC7943873
doi:
Types de publication
Journal Article
Langues
eng
Pagination
577623Informations de copyright
Copyright © 2021 Zito, Lualdi, Granata, Cocciadiferro, Novelli, Alberio, Casalone and Fasano.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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