An Integrative in silico Study to Discover Key Drivers in Pathogenicity of Focal and Segmental Glomerulosclerosis.
Drug target
Glomerulosclerosis
Segmental glomerulosclerosis
Weighted gene co-expression network
Journal
Kidney & blood pressure research
ISSN: 1423-0143
Titre abrégé: Kidney Blood Press Res
Pays: Switzerland
ID NLM: 9610505
Informations de publication
Date de publication:
2022
2022
Historique:
received:
13
11
2021
accepted:
13
03
2022
pubmed:
21
3
2022
medline:
14
6
2022
entrez:
20
3
2022
Statut:
ppublish
Résumé
Focal and segmental glomerulosclerosis (FSGS) is a clinical-pathologic condition marked by segmental and localized glomerular damages. Despite investigations, the molecular mechanisms behind FSGS development remain to be more clarified. By a comprehensive analysis of an FSGS-related array set, the aim of this study was to unravel the top pathways and molecules involved in the pathogenesis of this disorder. FSGS-related microarray dataset (GSE129973) from the Gene Expression Omnibus database was quality checked, analyzed, and its differentially expressed genes (DEGs) (log2 fold change > 1) were used for the construction of a protein-protein interaction (PPI) network (STRING). The degree of centrality was considered to select the hub molecules in the network. The weighted gene co-expression network analysis (WGCNA) was utilized to construct co-expression modules. Hub molecules were selected based on module membership and gene significance values in the disease's most correlated module. After spotting the key molecules considering both strategies, their expression pattern was checked in other FSGS microarray datasets. Gene ontology and Reactome pathway enrichment analyses were performed on the DEGs of the related module. After quality checking, normalization, and analysis of the dataset, 5,296 significant DEGs, including 2,469 upregulated and 2,827 downregulated DEGs were identified. The WGCNA algorithm clustered the DEGs into nine independent co-expression modules. The disease most correlated module (black module) was recognized and considered for further enrichment analysis. The immune system, cell cycle, and vesicle-mediated transports were among the top enriched terms for the identified module's DEGs. The immune system, cell cycle, and vesicle-mediated transports were among the top enriched terms for the black module's DEGs. The key molecules (BMP-2 and COL4A1) were identified as common hub molecules extracted from the two methods of PPI and the co-expressed networks. The two identified key molecules were validated in other FSGS datasets, where a similar pattern of expression was observed for both the genes. Two hub molecules (BMP-2 and COL4A) and some pathways (vesicle-mediated transport) were recognized as potential players in the pathogenesis of FSGS.
Sections du résumé
BACKGROUND
BACKGROUND
Focal and segmental glomerulosclerosis (FSGS) is a clinical-pathologic condition marked by segmental and localized glomerular damages. Despite investigations, the molecular mechanisms behind FSGS development remain to be more clarified. By a comprehensive analysis of an FSGS-related array set, the aim of this study was to unravel the top pathways and molecules involved in the pathogenesis of this disorder.
METHODS
METHODS
FSGS-related microarray dataset (GSE129973) from the Gene Expression Omnibus database was quality checked, analyzed, and its differentially expressed genes (DEGs) (log2 fold change > 1) were used for the construction of a protein-protein interaction (PPI) network (STRING). The degree of centrality was considered to select the hub molecules in the network. The weighted gene co-expression network analysis (WGCNA) was utilized to construct co-expression modules. Hub molecules were selected based on module membership and gene significance values in the disease's most correlated module. After spotting the key molecules considering both strategies, their expression pattern was checked in other FSGS microarray datasets. Gene ontology and Reactome pathway enrichment analyses were performed on the DEGs of the related module.
RESULTS
RESULTS
After quality checking, normalization, and analysis of the dataset, 5,296 significant DEGs, including 2,469 upregulated and 2,827 downregulated DEGs were identified. The WGCNA algorithm clustered the DEGs into nine independent co-expression modules. The disease most correlated module (black module) was recognized and considered for further enrichment analysis. The immune system, cell cycle, and vesicle-mediated transports were among the top enriched terms for the identified module's DEGs. The immune system, cell cycle, and vesicle-mediated transports were among the top enriched terms for the black module's DEGs. The key molecules (BMP-2 and COL4A1) were identified as common hub molecules extracted from the two methods of PPI and the co-expressed networks. The two identified key molecules were validated in other FSGS datasets, where a similar pattern of expression was observed for both the genes.
CONCLUSIONS
CONCLUSIONS
Two hub molecules (BMP-2 and COL4A) and some pathways (vesicle-mediated transport) were recognized as potential players in the pathogenesis of FSGS.
Identifiants
pubmed: 35306494
pii: 000524133
doi: 10.1159/000524133
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
410-422Informations de copyright
© 2022 The Author(s). Published by S. Karger AG, Basel.